Quantitative Imaging in Medicine and Surgery
Aims and Scope Quantitative Imaging in Medicine and Surgery (QIMS, Quant Imaging Med Surg, Print ISSN 2223-4292; Online ISSN 2223-4306) publishes peer-reviewed original reports and reviews in medical imaging, including X-ray, ultrasound, computed tomography, magnetic resonance imaging and spectroscopy, nuclear medicine and related modalities, and their application in medicine and surgery. While focus is on clinical investigations, papers on medical physics, image processing, or biological studies which have apparent clinical relevance are also published. This journal encourages authors to look at the medical images from a quantitative angle. This journal also publishes important topics on imaging-based epidemiology, and debates on research methodology, medical ethics, and medical training. Descriptive radiological studies of high clinical importance are published as well. Published quarterly from Dec. 2011 to Dec. 2012 and bimonthly from Feb. 2013 to Feb 2018, QIMS now follows a monthly publication model. QIMS is indexed in PubMed/PubMed Central, Scopus and Web of Science [Science Citation Index Expanded (SCIE)]. Its latest impact factor is: 2.8. QIMS is published by AME Publishing Company, with support from Wolters Kluwer (the journal content will be made available on Wolters Kluwer Health’s Lippincott and Ovid platforms). Indexing Quantitative Imaging in Medicine and Surgery is indexed and covered by Web of Science [Science Citation Index Expanded (SCIE)] PubMed PubMed Central (PMC) Google Scholar Scopus Ovid Information for Authors QIMS is a member of Committee on Publication Ethics (COPE) and it follows the Committee on Publication Ethics (COPE)'s guidelines and the ICMJE recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journal. Manuscripts submitted must be the original work of the author(s) and must not be published previously or under consideration for publication elsewhere. Manuscripts Turnaround Time First Editorial Decision: 3-5 days. Peer review: 1-2 months. Revision time: 2-4 weeks. Publication Ahead of Print: within 1 month after being accepted (with APC payment received). Formal publication: within 1-3 months after being accepted (with APC payment received). Original Articles are listed as priority. QIMS’s position on case reports and review articles. QIMS welcomes case reports where quantitative imaging played a role for diagnosis and/or treatment; also welcome first time realization (in animals or in human subjects) of a new imaging technique. These case reports are usually written a concise and short letter format (see <https://qims.amegroups.com/>) for example. Case reports of particular clinical importance are also published in a longer format; for these cases we expect important pathophysiological, diagnostic, therapeutic implications. We do not publish case report only because of the rarity of the cases. Note, although we believer reporting case materials is important for the advancement of medicine. The space reserved for case report remains limited for each issue. The decision to publish or not publish a case material submission can sometimes depend on the available space of the journal. QIMS welcomes reviews and comments on published papers. Review papers should contain authors’ analytical appraisal of published papers and personal viewpoints, instead of a mere aggregation of published abstracts. We expect review papers are in three forms, 1) expert reviews, usually published in editorial format, provide authors’ own insights and perspective; 2) systematic review; 3) educational reviews, including pictorial reviews. Systematic reviews (maybe narrative in writing but critical in nature) are particularly welcomed. Publication Schedule Published quarterly from Dec. 2011 to Dec. 2012 and bimonthly from Feb. 2013 to Feb 2018, QIMS now follows a monthly publication model. Open Access Statement This journal is a peer reviewed, open access journal. All content of the journal is published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). All articles published open access will be immediately and permanently free for all to read, download, copy and distribute as defined by the applied license. Free access and usage Permitted third party reuse is defined by the CC BY-NC-ND 4.0 license. This license allows users to copy and distribute the article, provided: this is not done for commercial purposes and further does not permit distribution of the Article if it is changed or edited in any way. the user gives appropriate credit (with a link to the formal publication through the relevant DOI) and provides a link to the license but not in an any way implying that the licensor is endorsing the user or the use of the work. no derivatives including remix, transform, or build upon the material was allowed for distribution. The full details of the license are available at https://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright For open access publishing, this journal uses an exclusive licensing agreement. Authors will transfer copyright to AME Publishing Company, but will have the right to share their article in the same way permitted to third parties under the relevant user license, as well as certain scholarly usage rights. For any inquiry/special circumstance on the copyright, commercial usage or adaptation of QIMS articles, please contact: permissions@amegroups.com For reprint order, please contact: sales@amegroups.com Support for Authors to Comply with Funding Body Mandates We work with authors of research articles supported by funding bodies with open access mandates to ensure that authors can meet their funders’ requirements for public access to research results. In addition, we offer further support for authors who are required to comply with funding body mandates, including but not limited to: All articles published open access will be immediately and permanently free for everyone to read, download, copy and distribute. If a specific open access license is needed, please contact the editorial office for confirmation before submission. Example of statements in a published article: https://jgo.amegroups.org/article/view/76355/html. No copyright is claimed for any work of the U.S. government. Example of statements in published articles: https://actr.amegroups.org/article/view/8875; https://med.amegroups.org/article/view/8358/html. Contact with Editorial Office Suki Tang, Corresponding Editor, Quantitative Imaging in Medicine and Surgery Email: qims@amepc.org Publisher Information QIMS is published by AME Publishing Company. Addresses: Hong Kong branch office: Flat/RM C 16F, Kings Wing Plaza 1, NO. 3 on Kwan Street, Shatin, NT, Hong Kong, China. Updated on June 18, 2026
Epicardial adipose tissue density, myocardial fibrosis, and heart failure with preserved ejection fraction in postmenopausal women: a mediation analysis
Background: Heart failure with preserved ejection fraction (HFpEF) predominantly affects postmenopausal women, but its underlying mechanisms remain unclear. Epicardial adipose tissue (EAT), a metabolically active fat depot adjacent to the myocardium, has been implicated in HFpEF. This study aimed to evaluate the associations of EAT volume and density with HFpEF in postmenopausal women and to determine whether myocardial fibrosis mediates this relationship. Methods: In this retrospective cross-sectional study, we included 70 postmenopausal women with HFpEF and 70 age-matched controls (±3 years) who underwent cardiac computed tomography (CT). EAT volume and density were quantified from coronary artery calcium scoring images. Myocardial extracellular volume (ECV), derived from dual-energy CT, was used to quantify myocardial fibrosis. Multivariable regression analyses and mediation analyses were performed to evaluate the associations between EAT characteristics, ECV, and HFpEF. Results: EAT volume index, density, and ECV were significantly higher in the HFpEF group than in controls (all P<0.05). After adjustment for cardiovascular risk factors and comorbidities, higher EAT density [per 1-Hounsfield unit (HU) increase; odds ratio (OR) =1.209; 95% confidence interval (CI): 1.073–1.362; P=0.002] and ECV (per 1% increase; OR =1.306; 95% CI: 1.104–1.510; P=0.001) remained independently associated with HFpEF. ECV mediated 47.7% of the relation between EAT density and HFpEF (effect =0.188; 95% CI: 0.094–0.296; P<0.05). EAT density was independently associated with ECV in the postmenopausal population (β =0.39, P<0.001). Conclusions: EAT density is more strongly and independently associated with HFpEF in postmenopausal women than EAT volume. Myocardial fibrosis, measured as ECV, partially mediated the association between EAT density and HFpEF, supporting the concept of an EAT-myocardial fibrotic axis in the pathophysiology of HFpEF in this population.
X-Pruning: a dual-stream information fusion mammography diagnosis network based on pruned transformer and cross-attention mechanism
Background: Breast cancer remains the most prevalent malignancy among women globally. While early detection through mammography is crucial for improving survival rates, automating this process poses significant computational challenges. Specifically, detecting small malignant lesions within high-resolution images and effectively integrating complementary information from standard craniocaudal (CC) and mediolateral oblique (MLO) views are difficult tasks. Therefore, the aim of this study is to develop a novel, highly efficient dual-view mammography diagnostic network, termed X-Pruning, designed to overcome these computational and integration challenges. Methods: The proposed X-Pruning framework addresses multi-view mammography by combining pruned transformer blocks (PTBs) with cross-attention fusion. The model employs parallel PTBs to process standard CC and MLO views. By implementing a dynamic pruning strategy based on window importance within the transformer layers, the network focuses computational resources specifically on suspicious regions. This reduces the heavy burden of self-attention calculations while strictly preserving full-image scale information to enhance the detection of small lesions. Additionally, a novel cross-attention fusion module was developed and integrated into the network to facilitate interactive information exchange between the CC and MLO views, enabling comprehensive multi-view feature integration. Results: The X-Pruning framework was evaluated on two widely recognized public datasets: Vindr-Mammo and Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). Experimental results demonstrated that the proposed model consistently outperformed several mainstream baseline architectures. Specifically, X-Pruning achieved area under the curve (AUC) scores of 0.812 on the Vindr-Mammo dataset and 0.792 on the CBIS-DDSM dataset. Furthermore, these performance improvements were achieved while significantly reducing overall computational demands compared to standard models. Conclusions: The X-Pruning network successfully resolves the trade-off between high diagnostic accuracy and computational efficiency in automated mammogram analysis. By intelligently allocating computational resources to critical lesion areas and effectively fusing cross-view information, this framework demonstrates robust diagnostic capabilities. These advancements highlight the clinical potential of X-Pruning to serve as an efficient, automated tool for enhancing the early detection and diagnosis of breast cancer.
The application of shear wave elastography in monitoring renal allografts
Background: Follow-up for renal transplant recipients depends on laboratory tests for the monitoring of renal function. Ultrasound shear wave elastography (SWE), as a noninvasive technique, can measure renal allograft stiffness, offering a potential direct indicator of the allograft’s functional status. This study aimed to establish a model for monitoring and assessing renal allograft function by combining SWE imaging of renal allografts with laboratory indicators to assist in monitoring renal function. Methods: Ultrasound SWE was performed in renal transplant recipients who met the inclusion and exclusion criteria during routine follow-up ultrasound examinations at West China Hospital of Sichuan University between December 2021 and August 2022. The stiffness values were recorded as shear wave velocity (SWV). Stiffness was measured three times in each patient, and the median of the three measurements was used for subsequent analysis. The data collected comprised relevant laboratory test indicators—including serum creatinine (Scr), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), serum uric acid (UA), serum cystatin C (Cys C), cholesterol, and albumin—and Doppler parameters—including the systolic peak velocity, end-diastolic flow velocity, and resistance index from the main, segmental, interlobar, and arcuate arteries of the renal allograft. Correlation analysis was conducted between SWV values and the collected parameters. Univariate and multivariate analyses were performed to identify factors associated with impaired allograft function, defined as an eGFR <60 mL/min/1.73 m2. A binary logistic regression model was subsequently constructed. Based on the model, nomograms, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis curves were generated. All statistical analyses were performed with SPSS 25.0 software and the rms package in R, with a two-sided P value <0.05 considered statistically significant. Results: A total of 462 renal transplant recipients were included (256 males and 206 females), with a median age of 34 years (interquartile range, 29–42 years). Spearman rank correlation analysis revealed that SWV significantly correlated with several parameters: renal transplantation time (ρ=–0.128; P=0.007), Scr (ρ=0.209; P=0.000), eGFR (ρ=–0.234; P=0.000), BUN (ρ=0.128; P=0.006), UA (ρ=0.146; P=0.002), and Cys C (ρ=0.213; P=0.000). Univariate analysis revealed that SWV, age, renal transplantation time, BUN, Scr, Cys C, UA, serum albumin, and the resistive indices of the segmental, interlobar, and arcuate arteries differed significantly (P<0.05). The binary logistic regression model, which incorporated the factors of SWV, age, Scr, and Cys C identified in the multivariate analyses, demonstrated excellent discriminative performance, with an area under the ROC curve of 0.962 in the training set and 0.929 in the validation set. Conclusions: Ultrasound SWE served as a reliable, noninvasive adjunct for assessing renal allograft function. Furthermore, the binary logistic regression model integrating SWV with clinical parameters (age, Scr, and Cys C) demonstrated high predictive accuracy, offering a promising composite tool for stratifying graft dysfunction risk.
Ultrasonographic assessment of pediatric adnexal lesions: validation of the O-RADS system and development of a simplified C-CAS framework
Background: The Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) is a standardized tool for assessing adult adnexal lesions, but its performance in the pediatric population has not been fully validated. This study aimed to validate O-RADS US in children and develop a simplified assessment framework for pediatric adnexal lesions. Methods: This retrospective study included a training cohort of 301 adnexal lesions from three centers and an independent validation cohort of 95 lesions from a fourth center. All lesions were pathologically confirmed. In the training cohort, lesions were classified according to the O-RADS V2022 criteria, and the diagnostic performance of the system was evaluated. Independent predictors of malignancy identified by firth penalized regression were used to construct a simplified framework [Cysts-Color-Ascites-Shadowing (C-CAS)]. This C-CAS framework was subsequently tested in the validation cohort. Results: Of the 396 lesions, 44 (11.11%) were malignant. In the training cohort, O-RADS showed excellent diagnostic performance [area under the curve (AUC) =0.931, 95% confidence interval (CI): 0.898–0.964]. Using a cutoff of category ≥4, sensitivity was 97.2% (95% CI: 91.9–100.0), specificity 89.1% (95% CI: 85.3–92.8%), positive predictive value 54.7% (95% CI: 42.5–66.9%), and negative predictive value 99.6% (95% CI: 98.8–100.0%). Firth regression analysis confirmed that “color score 4” [odds ratio (OR) =21.78, 95% CI: 1.80–255.38, P=0.012] and “ascites” (OR =49.13, 95% CI: 4.25–417.96, P<0.001) were strong independent predictors of malignancy, whereas “uni- or bilocular cyst without solid component” (OR ≈0.00, 95% CI: 0.00–0.04, P<0.001) and “acoustic shadowing” (OR =0.04, 95% CI: 0.00–0.45, P=0.004) were significant protective factors. The derived C-CAS framework achieved an AUC of 0.932 (95% CI: 0.911–0.953) in the training set and maintained high diagnostic accuracy in the independent validation set (AUC =0.948, 95% CI: 0.916–0.980). Conclusions: O-RADS US is a clinically applicable tool for pediatric adnexal lesions. The proposed C-CAS framework offers a validated, simplified alternative which maintains high diagnostic accuracy.
Electroacupuncture at Dazhui (GV14) and Mingmen (GV4) acupoints promoting nerve repair in rat model of spinal cord injury: a study based on diffusion tensor imaging
Background: The rupture and demyelination of nerve fibers after spinal cord injury (SCI) are primary contributors to neurological dysfunction. The axon attraction signal pathway mediated by netrin-1 is crucial for promoting the effective regeneration and repair of nerve axons. Previous studies have proved that electroacupuncture (EA) can improve nerve function and promote nerve repair and regeneration in rats with SCI, yet its underlying mechanism remains to be elucidated. This study aims to evaluate the effects of EA on neural repair and to investigate its regulatory role in the axon guidance signaling pathway, thereby clarifying the mechanisms by which EA promotes nerve repair following SCI. Methods: Male Sprague Dawley rats were randomly assigned to the Normal group, Sham group, SCI group, and EA group, with each group further divided into subgroups based on intervention duration: 7, 14, and 28 days. Allen’s method was employed to establish the SCI model. Dazhui (GV14) and Mingmen (GV4) acupoints were selected for EA intervention. Basso-Beattie-Bresnahan (BBB) score and the inclined plate test were used to evaluate the motor function of rats in each group. Morphological and structural changes in the injured spinal cord were assessed through magnetic resonance imaging (MRI). The fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD) values in the injured area were measured via diffusion tensor imaging (DTI), and the morphological changes of nerve fiber bundles were analyzed via diffusion tensor tractography (DTT). The expressions of netrin-1, deleted in colorectal cancer (DCC), ras-related C3 botulinum toxin substrate 1 (Rac1) and F-actin were quantified using immunofluorescence (IF) staining, Western blot (WB) and real-time quantitative polymerase chain reaction (RT-PCR). Results: Compared with the Sham group, the SCI group exhibited significant deficits in motor function (P<0.01), disorganized spinal cord tissue structure, a markedly increased lesion area (P<0.01), impaired integrity of nerve fiber bundles, a significant decrease in FA value (P<0.01), and significantly elevated MD and RD values (P<0.01). Moreover, the expression levels of netrin-1, DCC, Rac1, and F-actin were significantly reduced (P<0.01). EA improved the behavioral performance of rats with SCI (P<0.01). Following EA intervention, the extent of spinal cord structural damage was alleviated, with increased FA values, decreased MD and RD values (P<0.01), alongside evident repair and reconstruction of damaged fiber bundles. Additionally, EA upregulated the expression levels of netrin-1, DCC, Rac1, and F-actin (P<0.01). Conclusions: EA can significantly improve the motor function of rats with SCI, regulate the nerve guidance factor netrin-1 and its receptor DCC, as well as the molecular switch Rac1, thereby promoting axonal cytoskeletal remodeling and facilitating neural repair. The nerve repair effect of EA may be achieved by regulating the axon attraction signal pathway mediated by netrin-1.
Reversible left ventricular trabecular remodeling after bariatric surgery in obesity: a prospective cohort study using quantitative cardiac MRI
Background: Obesity is associated with structural and functional cardiac remodeling; however, its quantitative imaging characteristics and the role of left ventricular trabeculation remain incompletely understood. This study aimed to characterize left ventricular trabeculation using multi-parametric cardiac magnetic resonance in patients with obesity, explore its association with subclinical cardiac dysfunction, and evaluate longitudinal changes following bariatric surgery. Methods: In this prospective single-center study, 30 patients with severe obesity underwent cardiac magnetic resonance imaging before and approximately 12 months after bariatric surgery, along with 30 age- and sex-matched healthy controls. Quantitative parameters included the non-compacted to compacted layer ratio (NC/C), trabecular mass fraction, left ventricular mass, left ventricular ejection fraction, global longitudinal strain, and diastolic filling indices. Left ventricular ejection fraction indexed to body surface area was analyzed as an exploratory parameter. Mediation analysis was performed to assess whether trabeculation statistically explained part of the association between body mass index and cardiac function, adjusting for age, sex, and blood pressure. Results: Compared with controls, patients with obesity showed significantly higher NC/C (1.68±0.24 vs. 1.11±0.19, P<0.001) and lower left ventricular ejection fraction (57.87%±6.22% vs. 63.40%±6.94%, P<0.05). In mediation analysis, the total effect of body mass index on left ventricular ejection fraction was −0.29 [95% confidence interval (CI): −0.56 to −0.02]. The indirect effect via the NC/C was statistically significant (β=−0.46, 95% CI: −0.81 to −0.09), while the direct effect was not significant (β=0.17, 95% CI: −0.25 to 0.59). Following bariatric surgery, trabeculation and left ventricular mass decreased significantly, while left ventricular ejection fraction, global longitudinal strain, and diastolic filling indices improved (P<0.05). Conclusions: Quantitative cardiac magnetic resonance analysis demonstrates increased left ventricular trabeculation in obesity, which decreases following weight reduction. Trabeculation is associated with subclinical cardiac dysfunction and may contribute to the association between obesity and impaired cardiac function. These findings suggest that trabecular remodeling in obesity represents a dynamic imaging phenotype, although further studies are required to clarify underlying mechanisms.
Renal volumetry and functional impairment prediction in pediatric vesicoureteral reflux based on contrast-enhanced voiding urosonography
Background: Contrast-enhanced voiding urosonography (CeVUS) provides a radiation-free alternative for diagnosing vesicoureteral reflux (VUR), but there is limited literature on its utility in evaluating renal volumetric loss and predicting split renal function (SRF) impairment as well as renal scarring (RS). This study aimed to elucidate the impact of VUR and intrarenal reflux (IRR) on renal volume, identify independent risk factors for SRF impairment and RS, and develop a predictive model. Methods: In this retrospective cohort study (from January 2022 to December 2024), 261 children (119 VUR-positive, 142 controls) underwent CeVUS. Renal volume was calculated from two-dimensional (2D) ultrasound measurements using the ellipsoid formula (length × width × depth ×0.523). VUR was graded based on the International Reflux Study Committee criteria (low grade: I–II, moderate grade: III, high grade: IV–V), and IRR was assessed during CeVUS. SRF and RS were evaluated by Tc-99m dimercaptosuccinic acid (99mTc-DMSA) scintigraphy within 2 weeks relative to the CeVUS examination in a subset of patients (n=86). Multivariable logistic regression was performed to identify independent risk factors of SRF impairment and RS. Results: Across VUR grades, renal volume decreased progressively, but only high-grade VUR showed a statistically significant reduction. No significant differences in renal volume were observed among low-grade VUR, contralateral negative, and negative control groups. Among high-grade VUR cases, kidneys with IRR were smaller than those without IRR, though not significantly (P=0.11). IRR predominantly involved the upper poles (cumulative prevalence 83%) and showed strong spatial concordance with both SRF impairment [Jaccard similarity 0.72, 95% confidence interval (CI): 0.65–0.78] and RS (Jaccard similarity 0.69, 95% CI: 0.62–0.75). In multivariate logistic regression analysis, frequent urinary tract infections (UTIs), higher VUR grade, and IRR emerged as independent risk factors for SRF impairment (all P<0.05), while older age was an additional risk factor for RS. The prediction model demonstrated discriminative ability with areas under the curve (AUCs) of 0.89 (95% CI: 0.86–0.94) for SRF impairment and 0.92 (95% CI: 0.87–0.96) for RS. Conclusions: High-grade VUR is a primary determinant of renal volume reduction, and IRR demonstrates a potential association with accelerated volume loss. Crucially, frequent UTIs, high-grade VUR, and IRR constitute independent risk factors for SRF impairment, while older age independently predicts RS. The multivariable prediction model achieved strong discriminative capacity, supporting CeVUS as a comprehensive, radiation-free modality for anatomical assessment and functional risk stratification in pediatric reflux nephropathy.
The value of dual-layer detector spectral CT multiparameter imaging in distinguishing between benign lesions and carcinomas of the larynx and hypopharynx
Background: Accurately distinguishing between benign and malignant lesions in the larynx and hypopharynx is crucial for formulating individualized and precise treatment plans. Due to the low soft-tissue resolution of conventional computed tomography (CT), the diagnostic accuracy of laryngeal and hypopharyngeal lesions is not satisfactory in clinical practice. Therefore, this study aimed to assess the value of dual-layer detector spectral computed tomography (DLSCT) multiparameter imaging for distinguishing between laryngeal and hypopharyngeal benign lesions (LHBLs) and laryngeal and hypopharyngeal carcinomas (LHCs) and to identify the optimal DLSCT parameters. Methods: The data of 141 patients were retrospectively analyzed. Spectral CT parameters, including those derived from the arterial phase (AP) and venous phase (VP), as well as the maximum lesion diameter (MLD), were measured and analyzed. The diagnostic performance of each spectral parameter was evaluated using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). The DeLong test was then used to compare differences in diagnostic performance. The parameters demonstrating the highest AUC in the AP and VP were integrated via logistic regression. Clinical utility was assessed by decision curve analysis (DCA). Results: With the exception of the virtual non-contrast CT values in the AP and the standardized effective atomic number (sZeff) in both the VP and AP, all the measured spectral CT parameters differed significantly between the LHBLs and LHCs (P<0.002). Among all the single parameters analyzed, the 40-kiloelectron volt (keV) virtual monochromatic image (VMI) in the VP achieved the highest AUC of 0.932, which was significantly higher than that of the conventional image (CI) (P<0.05). The AUC values for the CIs in the AP and VP were 0.837 and 0.866, respectively. When the 40 keV-AP VMI, 40 keV-VP VMI, and MLD were combined in a model, the AUC increased to 0.970, which was significantly higher than the AUC of the model that combined the 40 keV-AP VMI and MLD (P=0.009), but did not differ significantly from the AUC of the model that combined the 40 keV-VP VMI and MLD (P=0.403). The model that combined the 40 keV-AP VMI, 40 keV-VP VMI, and MLD had the greatest net clinical benefit, but it was only superior to the model that combined the 40 keV-VP VMI and MLD within certain risk threshold ranges. Conclusions: DLSCT exhibited better diagnostic performance for distinguishing between LHBLs and LHCs than conventional CT. Notably, 40 keV-VP VMI demonstrated favorable diagnostic value and may serve as a potential auxiliary imaging tool for this differential diagnosis.
Synthesizing cone-beam projections in radiotherapy using deep learning network for patients with head and neck cancer
Background: Initially, cone-beam (CB) projection was developed to serve as a two-dimensional (2D) radiograph for fast target localization in radiotherapy. Later, it was gradually replaced by three-dimensional (3D) cone-beam computed tomography (CBCT), which is reconstructed from multiple CB projections. Since the size of CB projections is relatively large, they are usually discarded to conserve clinical storage. To effectively regenerate these projections, a deep learning (DL) method was developed to synthesize CB projections from CBCT. Methods: CB projection images from 50 patients under image-guided radiotherapy for head and neck cancer was collected. First, the digitally reconstructed radiograph (DRR) was created via a ray-tracing based algorithm. Next, a DL network was built to learn the pixel-to-pixel correspondence between the DRRs and CB projections in the training set. Finally, the CB projections were synthesized from DRRs in the testing set by a DL model. Three DL networks, Attention-UNet, Residual AutoEncoder, and Pix2Pix, were examined. Ablation studies on the effects of scatter correction, CBCT resolution, and training sample size were conducted. Non-DL and DL methods were compared, and a clinically relevant downstream task was conducted. Three similarity metrics, peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and video quality metric (VQM), were used to evaluate model performance. Results: Among three DL models, Attention-UNet achieved the best performance in terms of PSNR (29.71), SSIM (0.97), and VQM (0.20). The other two models showed comparable performance with PSNR, SSIM, and VQM values of 26.55, 0.96, and 0.27, respectively, for Residual AutoEncoder, and of 27.22, 0.94, and 0.25, respectively, for Pix2Pix. With scatter correction, the PSNR, SSIM, and VQM for Attention-UNet improved by 21.3%, 2.1%, and 39.3%, respectively. Under the finest voxel size (0.5×0.5×2.0 mm3), Attention-UNet achieved the highest PSNR (29.71±3.62), SSIM (0.97±0.008), and lowest VQM (0.20±0.091). The scatter correction and higher CBCT resolution could effectively improve the prediction accuracy of these DL models. Conclusions: The DL-based method can effectively synthesize CB projections from CBCT for patients with head and neck cancer. It may thus serve as a means to saving storage space for clinical CB projections.
Enhanced hippocampal-cortical functional connectivity following post-stroke cognitive impairment: a resting-state functional magnetic resonance imaging study
Background: Resting-state functional magnetic resonance imaging (rs-fMRI) method was employed to investigate the abnormal patterns of functional connectivity (FC) between hippocampus and whole brain in patients with post-stroke cognitive impairment (PSCI). The aim was to explore imaging biomarkers, providing imaging evidence for understanding neurocognitive mechanisms of PSCI and formulating targeted intervention strategies. Methods: A total of 42 patients with acute ischemic stroke (AIS) were recruited from The First Affiliated Hospital of Xinjiang Medical University between June 2025 to October 2025 in this retrospective cross-sectional study. Concurrently, 40 healthy controls (HCs) without a history of cognitive impairment (CI) were recruited from the community. All participants underwent structural magnetic resonance imaging (MRI) and rs-fMRI scans. Cognitive function in PSCI patients was assessed longitudinally at 3 months post-stroke using standardized neuropsychological instruments, including Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). Seed-based FC analysis was performed using bilateral hippocampus as regions of interest (ROIs) to compute whole brain connectivity maps. Pearson partial correlation analyses were conducted to examine the associations between altered FC strengths and cognitive scores, controlling for potential confounding variables. Results: Compared with the HC group, PSCI patients exhibited significantly lower MoCA (Z=0.203, P<0.001) and MMSE (Z=0.129, P<0.001) scores. Enhanced FC was observed in PSCI patients between left hippocampus and left superior temporal pole (t=4.435, P<0.001), right frontal inferior opercular (t=5.079, P<0.001), left inferior parietal (t=4.310, P<0.001), and right superior frontal gyrus (t=3.870, P<0.001). Additionally, enhanced connectivity was found between right hippocampus and right frontal inferior opercular (t=4.246, P<0.001) as well as the right supramarginal gyrus (t=3.794, P=0.001). Pearson correlation analyses revealed that enhanced FC between right hippocampus and right frontal inferior opercular was positively correlated with both MMSE (r=0.467, P=0.001) and MoCA (r=0.434, P=0.003) scores. Conclusions: The FC between hippocampus and cerebral cortex is enhanced in PSCI patients. Abnormal connection between hippocampus and right frontal inferior opercular can be used as a potential imaging biomarker to reveal the neurocognitive mechanism of PSCI. In the future, cognitive function can be optimized by regulating these regions.
Prognostic incremental value of perivascular adipose tissue in myocardial infarction with non-obstructive coronary arteries: a multicenter cohort study
Background: Perivascular adipose tissue (PVAT) is recognized as a key contributor to atherosclerosis and adverse clinical outcomes. However, its relationship with metabolic status and prognosis in patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) remains unclear. The aim of this study was to investigate the relationship between PVAT and clinical prognosis in patients with MINOCA, and to assess its incremental predictive value beyond the traditional metrics. Methods: A simple scoring tool, the PVAT score, was constructed based on cardiac magnetic resonance (CMR)-derived region-specific PVAT indicators. A total of 272 patients with MINOCA and 204 healthy controls were enrolled. The prognostic increment was assessed by analyzing the association of PVAT index and metabolic index with composite major adverse cardiovascular events (MACE). Results: The region-specific PVAT index and PVAT score were significantly higher in patients with MINOCA than in controls. Over a median 3.2-year follow-up, 57 MACEs occurred. In addition to age [hazard ratio (HR): 1.09, 95% confidence interval (CI): 1.05–1.13, P<0.001], late gadolinium enhancement positivity (HR: 1.98, 95% CI: 1.16–3.39, P=0.012), triglyceride-glucose (TyG) index (HR: 4.28, 95% CI: 1.92–9.51, P=0.016), a higher PVAT score (HR: 3.84, 95% CI: 1.52–9.72, P=0.004) was significantly associated with an increased risk of MACE. Notably, the prognostic association of PVAT score was significantly stronger in MINOCA patients with concomitant diabetes mellitus (DM) (P<0.001). Conclusions: Region-specific PVAT indicators and PVAT score were increased and associated with poorer prognosis in patients with MINOCA. The PVAT score provided incremental prognostic value beyond established clinical predictors, enabling improved risk stratification, particularly in the DM subgroup.
Deep learning-based computed tomography quantification integrated with circulating tumor cells for prognostic evaluation in stage I lung adenocarcinoma
Background: Prognosis varies among patients with early-stage lung adenocarcinoma (LUAD) even within the same clinical stage. This study aimed to evaluate the prognostic value of measurements of computed tomography (CT), circulating tumor cells (CTC), and their combinations in clinical stage I LUAD. Methods: This retrospective analysis included 183 patients at stage I peripheral LUAD who underwent preoperative thin-section chest CT and CTC detection. The maximal solid size to tumor ratio on axial image was measured by radiologists (R-MSSA%). The three-dimensional volume (3D-SV) and mass (3D-SM) of solid components were measured by deep learning, followed by calculation of the consolidation-to-tumor ratios of 3D-SV (3D-SV%) and 3D-SM (3D-SM%). The differences in recurrence rates or overall survival of CTC, CT measurements, and their combinations were compared. Multivariate Cox proportional hazards models were used to identify independent risk factors. Results: Our study results indicated that 3D-SM, 3D-SV, 3D-SM%, and CTC can stratify the recurrence risk in clinical stage I patients. For clinical stage IA patients with high values of 3D-SM, 3D-SV, R-MSSA%, or 3D-SM%, the recurrence risk showed no significant difference compared with stage IB patients. Furthermore, for stage IA patients stratified as high-risk by the integration of CTC with 3D-SM or 3D-SV, the prognosis was worse than that of stage IB patients. A model combining 3D-SM and CTC had a higher C-index [0.75; 95% confidence interval (CI): 0.70, 0.81] compared to that of 3D-SM or CTC alone. Conclusions: The integration of CTC and CT measurements can enhance the stratification ability for early-stage lung cancer patients and improve the prognosis prediction, especially for those at stage IA.
Comparing deep-learning, radiomics, and fusion models for parathyroid tumor classification using ultrasound: a multicenter retrospective study
Background: Accurate preoperative differentiation between parathyroid adenoma (PA) and parathyroid carcinoma (PC) or atypical parathyroid tumor (APT) is critical for surgical planning, yet ultrasound accuracy remains highly operator-dependent. Leveraging the complementary advantages of radiomics and deep learning (DL), this study aimed to develop and compare radiomics, DL, and fusion models based on ultrasound imaging for the identification of APT/PC. Methods: A total of 1,122 patients (270 men and 852 women; mean age 54.2±13.7 years) with parathyroid neoplasms were retrospectively reviewed from two Chinese hospitals between January 1, 2016, and April 30, 2025. To address the limited number of APT/PC cases (n=74), multicenter data were pooled and stratified by pathological type into training (733 PA and 53 APT/PC), validation (158 PA and 10 APT/PC), and test sets (57 PA and 11 APT/PC). Radiomic features were extracted from preprocessed ultrasound images. DL features came from 1ch_ResNet101 (raw images) and 2ch_ResNet101 (concatenated region of interest images). Two fusion models were built: Merged model 1 (radiomics + 1ch_ResNet101) and Merged model 2 (radiomics + 2ch_ResNet101). Results: Statistically significant differences were observed in age at diagnosis between the training and validation sets, as well as between the validation and test sets (both P<0.001). Additionally, serum phosphate levels differed significantly between the training and test sets (P=0.022). On the test set, the top-performing models were Merged model 2 [area under the receiver operating characteristic (ROC) curve (AUC) =0.933] and Merged model 1 (AUC =0.926), both of which surpassed the ResNet101 (P<0.001) and showed comparable performance to radiomics (AUC =0.905; P=0.171 and 0.059, respectively). 2ch_ResNet101 significantly outperformed 1ch_ResNet101 (AUC =0.873 vs. 0.804; P<0.001). Radiomics significantly outperformed both ResNet101 models (P<0.001). No significant difference was found between the two merged models (P=0.593). Conclusions: All models enable timely identification of potentially malignant parathyroid tumors, with the fusion model outperforming DL and matching radiomics in diagnostic performance.
Non-invasive diagnostic model for myocarditis using cardiac magnetic resonance radiomics
Background: Myocarditis is an inflammatory myocardial disease with high clinical heterogeneity, and its early diagnosis relies on invasive endomyocardial biopsy (EMB) or advanced cardiac magnetic resonance (CMR) techniques with poor accessibility in primary hospitals. Conventional CMR interpretation is subjective with inter-observer variability, and single-modality radiomics models have insufficient diagnostic accuracy. This study aimed to construct and validate a machine learning model integrating multimodal CMR radiomics features and clinical parameters for early non-invasive and accurate diagnosis of myocarditis. Methods: This study recruited 344 participants from two hospitals (185 with myocarditis and 159 without myocarditis). Radiomics features were extracted from the left ventricular myocardium using multimodal CMR sequences [cine sequence (CINE), T2-weighted imaging (T2WI), and late gadolinium enhancement imaging (LGE)] and combined with clinical parameters. Feature selection was performed sequentially: first, intraclass correlation coefficient (ICC) analysis was used to retain features with ICC >0.8 for reproducibility; then, Mann-Whitney U test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression (for dimensionality reduction). For clinical predictors, univariate logistic regression (P<0.10) was used for preliminary screening, and multivariate logistic regression (P<0.05) for further selection. Models were constructed using random forests, including single-modality, radiomics fusion, and clinical-radiomics fusion variants. Model performance was evaluated in the training set, internal validation set, and external validation set. Results: The Fusion_Clinical_Radiomic model exhibited optimal and stable diagnostic performance, with area under the curve (AUC) values of 0.936 [95% confidence interval (CI): 0.905–0.967], 0.862 (95% CI: 0.779–0.944), and 0.854 (95% CI: 0.754–0.953) in the training set, internal validation set, and external validation set, respectively. Its accuracy, specificity, and sensitivity in the training set were 0.848, 0.845, and 0.850, respectively, with a favorable Brier score of 0.141. In the external validation set, it maintained a specificity of 0.906 and a Brier score of 0.193. The model’s performance was significantly superior to all unimodal models and the radiomics fusion model alone (all P<0.05). Decision curve analysis (DCA) showed that the model had extensive clinical net benefits. Conclusions: The fusion model provides an accurate, non-invasive auxiliary diagnostic tool for the diagnosis of acute myocarditis within 14 days of symptom onset. By relying on conventional CMR sequences (CINE, T2WI, LGE), it may reduce the reliance on invasive EMB and enhance diagnostic accessibility in primary hospitals. Supported by multicenter internal and external validation, the model exhibits favorable generalizability, offering a promising reference for clinical practice in the early identification of acute myocarditis.
Comparison of ultrasound-guided needle-knife release of the transverse carpal ligament with glucocorticoid injection in the treatment of carpal tunnel syndrome: a randomized trial
Background: Ultrasound-guided needle-knife release of the transverse carpal ligament (TCL) has the potential to treat mild-to-moderate carpal tunnel syndrome (CTS). This study aimed to compare the clinical efficacy and safety of ultrasound-guided needle-knife release of the TCL with those of glucocorticoid injection therapy. Methods: A total of 27 patients with mild or moderate CTS, involving 32 carpal tunnels, were enrolled between September 2025 and January 2026. They were randomly assigned to either the ultrasound-guided needle-knife therapy group (Group T, 17 carpal tunnels) or the ultrasound-guided glucocorticoid injection group (Group C, 15 carpal tunnels). Functional scores, Numerical Rating Scale (NRS) numbness and pain scores, Boston carpal tunnel questionnaire (BCTQ) scale scores, and 36-Item Short Form Health Survey (SF-36) scale scores were recorded and compared between groups at baseline, 1 month, and 3 months post-treatment. Clinical efficacy rates were assessed using the Kelly criteria. Results: Compared with baseline, at 1 and 3 months post-treatment, Group T showed a significant improvement in carpal tunnel function scores (P=0.011, P<0.001); a significant reduction in NRS pain scores (P=0.006, P<0.001); the NRS numbness score decreased significantly (P<0.001), with better results observed at 3 months post-treatment compared to 1 month post-treatment (P=0.014); and the Symptom Severity Scale (SSS) score of BCTQ scale decreased significantly (P=0.0179, P<0.001); the physical functioning (PF), bodily pain (BP), role physical (RP), general health (GH), role emotional (RE), and social functioning (SF) scores of SF-36 scale all increased significantly (P<0.05). Compared with baseline, in Group T, the Functional Status Scale (FSS) score of the BCTQ scale decreased significantly by 3 months after treatment (P=0.028), and the vitality (VT) and mental health (MH) scores of the SF-36 scale both increased significantly (P<0.05). Group C showed improvement in the aforementioned scores at both 1 and 3 months post-treatment compared to baseline, but the magnitude was smaller than that of Group T and did not differ significantly from baseline (P>0.05). Furthermore, the efficacy at 3 months post-treatment in the corticosteroid injection group was almost indistinguishable from that at 1 month, indicating poorer long-term efficacy. According to the Kelly criteria, the overall response rate was higher in Group T than in Group C. There were significant differences between Group T and Group C in terms of carpal tunnel function, Visual Analog Scale (VAS), NRS, BCTQ, and SF-36 scores (P<0.05). Conclusions: Ultrasound-guided needle-knife release of the TCL demonstrated superior efficacy compared to glucocorticoid injection.
Left atrial dysfunction in different morphologic phenotypes of hypertrophic cardiomyopathy: a cardiac magnetic resonance feature tracking study
Background: Hypertrophic cardiomyopathy (HCM) exhibits substantial phenotypic heterogeneity; however, differences in left atrial (LA) function among morphological subtypes remain poorly characterized. Cardiac magnetic resonance feature tracking (CMR-FT) offers advanced capabilities for quantifying LA deformation, yet its application to the evaluation of subtype-specific LA mechanics remains limited. This study aimed to investigate LA functional impairment among HCM phenotypic subtypes using CMR-FT. Methods: A total of 225 patients with HCM, divided into septal (n=153), apical (n=40), and concentric (n=32) subgroups, along with 68 age- and sex-matched healthy controls (HC), were retrospectively included from March 2023 to October 2024 at three tertiary hospitals. LA functional parameters, including reservoir strain (εs), conduit strain (εe), booster strain (εa), peak positive strain rate (SRs), peak early negative strain rate (SRe), and peak late negative strain rate (SRa), were obtained using CMR-FT. Statistical analyses included Chi-squared tests, one-way analysis of variance (ANOVA) with post hoc testing, linear regression, and intraclass correlation coefficients (ICCs). Results: All HCM subgroups showed impaired LA strain parameters compared to HC [(εs, %): septal: 26.69±11.02, apical: 23.47±8.16, concentric: 21.30±6.53 vs. control: 46.43±13.73; (εe, %): septal: 12.36±6.42, apical: 10.87±4.28, concentric: 9.72±4.28 vs. control: 27.88±11.15; (εa, %): septal: 14.34±6.41, apical: 12.60±4.97, concentric: 11.58±5.56 vs. control: 18.55±6.04; all P<0.05]. The concentric HCM subgroup demonstrated more severe impairment in LA εs and εa than the septal subgroup (both P<0.05). Septal HCM showed significant correlations between all LA strain parameters and left ventricular ejection fraction (LVEF) and global longitudinal strain (LVGLS) (|r|≥0.324, all P<0.001); apical HCM demonstrated correlations between LA εs, εe, and the aforementioned LV function parameters (|r|≥0.373, all P<0.05); only LA εa correlated with LVEF and LVGLS in concentric HCM (|r|≥0.389, both P<0.05). CMR-FT showed excellent reproducibility for LA strain measurements in patients with HCM (all ICCs >0.75). Conclusions: LA dysfunction varies across HCM phenotypes, with greater reservoir and booster impairment in concentric than septal HCM. These findings underscore the subtype-specific alterations in atrial mechanics and highlight the potential utility of CMR-FT for phenotypic characterization in HCM.
Impact of deep learning-based MRI reconstruction on image quality and apparent diffusion coefficient measurements in patients with intracranial tumors: a prospective intra-individual study
Background: Deep learning-based magnetic resonance imaging (MRI) reconstruction has shown promise in improving image quality and reducing scan time. However, its impact on diffusion-weighted imaging (DWI) and quantitative parameters such as apparent diffusion coefficient (ADC) in patients with intracranial tumors remains unclear. This study aims to investigate the impact of deep learning-based MRI reconstruction on the quality of diffusion-weighted images and the measurements of lesion ADC in patients with intracranial tumors. Additionally, it assessed the potential influence of this reconstruction method on the consistency of ADC-based quantitative analysis. Methods: This single-center, prospective intra-individual study involved 58 consecutive adults diagnosed with intracranial space-occupying lesions through imaging and/or pathology. These patients underwent brain MRI between January and May 2025, and all received standard single-b-value DWI (b=1,000 s/mm2). The raw DWI data were reconstructed using two methods: a conventional algorithm and a vendor-supplied deep learning reconstruction (DLR) algorithm based on convolutional neural networks. An experienced neuroradiologist manually placed regions of interest (ROIs) on the slice containing the lesion, including the lesion itself, the surrounding perilesional tissue, and the background. For each ROI, signal intensity (SI) and its standard deviation (SD) were measured, along with the lesion’s ADC values. The image quality metrics and ADC values obtained from the two reconstruction methods were compared using paired t-tests. Results: Compared with conventional reconstruction, DLR significantly reduced background SI (29.40±5.05 vs. 34.95±4.99; t=13.948, P<0.001) and background noise (14.63±3.50 vs. 20.61±4.17; t=14.721, P<0.001), corresponding to approximately a 30% noise reduction. Lesion SI was slightly but significantly higher with DLR (1,786.42±878.56 vs. 1,782.41±878.79; t=2.863, P=0.006), while SI in perilesional tissue exhibited no significant difference (P=0.259). DLR also demonstrated significantly lower lesion ADC values than conventional reconstruction [(1,247.10±567.37)×10−6 vs. (1,324.41±617.97)×10−6 mm2/s, t=2.974, P=0.004], with an average decrease of about 6%. Conclusions: Under a conventional single-b-value DWI protocol (b=1,000 s/mm2), deep learning-based MRI reconstruction improved image quality in patients with intracranial tumors by reducing background noise and slightly increasing lesion signal. However, it was also associated with a modest but systematic decrease in lesion ADC values, suggesting that quantitative diffusion metrics may be affected to some extent. Further validation is needed to determine the clinical significance of this ADC shift and to assess the potential utility of DLR in more demanding diffusion imaging settings.
Downsampling attention fusion network (DAFNet): a You Only Look Once network for lung nodule detection
Background: Lung nodule detection in computed tomography (CT) imaging is crucial for the early diagnosis and treatment of lung diseases. However, accurate detection remains challenging due to the varying sizes, shapes, and densities of nodules, as well as interference from surrounding tissues. This study aimed to develop a novel deep learning framework to improve the accuracy and efficiency of lung nodule detection. Methods: We developed the downsampling attention fusion network (DAFNet). Specifically, we incorporated a dual-branch downsampling module that employs a parallel learning strategy to effectively extract multiscale features while reducing computational complexity as compared to traditional convolutional modules. Additionally, we designed a global attention module (GAM), which cascades the channel and spatial attention mechanisms to enhance feature representation across different dimensions. To evaluate DAFNet, experiments were conducted on a lung CT image dataset (LCTD) and the public dataset Lung Nodule Analysis 2016 (LUNA16). LCTD comprises 2,172 CT images from 1,060 individuals. On the LUNA16, common object detection metrics such as mean average precision (mAP) and recall were adopted for evaluation, rather than the official specified free-response receiver operating characteristic (FROC) evaluation protocol. Results: On the augmented LCTD, DAFNet achieved 93.2% precision and 90.7% recall. Meanwhile, on LUNA16, DAFNet outperformed state-of-the-art methods in terms of mAP. Furthermore, DAFNet achieved GPU-accelerated near-real-time inference with a processing speed of 1.5 ms per image, and the model parameter size was only 2.5 M, enabling efficient and lightweight inference. Conclusions: Our study examined a novel method for detecting lung nodules, offering new insights and technical references for relevant research fields. The proposed modules can be flexibly and seamlessly integrated into various detection frameworks as plug-and-play components, effectively enhancing the flexibility and scalability of lung nodule detection. As our DAFNet does not incorporate false-positive reduction or malignancy grading in its nodule detection, the clinical translation and practical application of the model remain to be further validated and optimized in future work.
Multimodal quantitative ultrasound assessment of perioperative neck muscle alterations in patients with cervical spondylosis
Background: Cervical spondylosis (CS) involves degenerative changes that increase muscle fatigue and stiffness. Postoperative cervical collars are commonly used to maintain stability. This study used multimodal ultrasound to compare neck muscle thickness and stiffness between patients with CS and healthy controls (HC), and to longitudinally evaluate muscle parameter changes associated with different postoperative cervical collar wearing durations. Methods: This prospective study enrolled 40 patients undergoing cervical spine surgery and 40 healthy volunteers matched by age, sex, and body mass index (BMI) as HC. The thickness and stiffness of eight neck muscles in the anterior and posterior cervical regions were measured. Subsequently, 18 patients were randomly allocated to two groups for longitudinal follow-up. Group A wore a cervical collar for 1 month, whereas Group B wore one for 3 months. Longitudinal changes in neck muscle parameters were quantitatively measured, and the generalized estimating equation (GEE) model was used to evaluate changes at 3 and 6 months postoperatively. Results: No significant baseline differences were found between the case and HC groups. Regarding posterior cervical muscles, the case group showed significantly greater trapezius thickness in the neutral position (0.18 vs. 0.15 cm, P=0.008), but reduced thickness in other posterior muscles (all P<0.001, e.g., semispinalis capitis: 0.35 vs. 0.59 cm). In a forward flexion position, the case group had lower stiffness in all five posterior muscles (e.g., multifidus: 4.79 vs. 5.57 m/s, P<0.001). For the anterior cervical muscles, the case group had reduced thickness in longus capitis and longus colli in both neutral (0.21 vs. 0.30 cm, P<0.001; 0.66 vs. 0.75 cm, P<0.001) and deflection positions (0.23 vs. 0.30 cm, P=0.006; 0.59 vs. 0.65 cm, P=0.002). In the deflection position, the case group also showed lower stiffness in sternocleidomastoid (2.74 vs. 3.47 m/s, P=0.001), longus capitis (3.13 vs. 4.05 m/s, P<0.001), and longus colli (3.63 vs. 4.52 m/s, P<0.001). Longitudinally, the GEE model revealed that collar wearing duration had no significant effect on cervical muscle thickness (group effect: Wald χ2=2.396; P=0.122), but significantly influenced muscle stiffness (group effect: Wald χ2=13.306; P<0.001). Specifically, Group B had significantly higher stiffness than Group A [β=0.122; 95% confidence interval (CI): 0.049 to 0.196; Wald χ2=10.77; P=0.001]. No significant effects were observed for muscle type, cervical position, or time (all P>0.05). Furthermore, muscle thickness showed no significant changes over time in either group. By contrast, Group B had significantly greater stiffness than Group A at 3 months (mean difference =−0.122; 95% CI: −0.221 to −0.024; adjusted P=0.006) and 6 months (mean difference =−0.160; 95% CI: −0.280 to −0.040; adjusted P=0.003), postoperatively. Conclusions: Multimodal ultrasound effectively detects changes in cervical muscle thickness and stiffness in patients with CS. Longer cervical collar wearing duration may be associated with higher muscle stiffness; however, these findings are preliminary owing to the small sample size, and further large-scale studies are warranted.
MRI analysis of undifferentiated pleomorphic sarcoma: correlating imaging features with histological grade
Background: Undifferentiated pleomorphic sarcoma (UPS) is an aggressive soft tissue malignancy in which the accurate assessment of histological grade is crucial for treatment planning and prognosis. Noninvasive magnetic resonance imaging (MRI)-based tumor features may reflect tumor biology, but their association with histological grade and survival outcomes in UPS remains unclear. The aim of this study was to investigate the relationship between MRI-derived tumor features, histological grade, and survival outcomes in patients with UPS, in order to evaluate the potential of MRI as a noninvasive tool for prognostication and treatment planning. Methods: This retrospective study included 83 patients with pathologically confirmed UPS between January 2015 and December 2023. All patients underwent pre-treatment 3.0 Tesla (3T) MRI scans, which included T1-weighted, T2-weighted, and diffusion-weighted imaging (DWI). The MRI features assessed included growth pattern, signal intensity (SI) heterogeneity, necrosis volume, and apparent diffusion coefficient (ADC) values. Tumor histological grade was determined using the Fédération Nationale des Centres de Lutte Contre le Cancer (FNCLCC) system. Statistical analyses, including univariate and multivariate logistic regression, were performed to identify imaging features associated with high-grade UPS. Results: The study included 83 patients with a mean age of 59.5 years, and 67.5% had high-grade (Grade II–III) tumors. Significant MRI features associated with high-grade UPS included diffuse infiltrative growth pattern (P=0.047), high T2-weighted SI heterogeneity (P=0.04), tumor volume with necrosis ≥50% (P=0.03), and lower ADC mean values (P=0.001). Multivariable analysis revealed that growth pattern, necrosis volume, and ADC difference were independent predictors of high-grade tumors. The combination of these features had a high diagnostic accuracy, with an area under the curve (AUC) of 0.876, sensitivity of 82.14%, and specificity of 85.19%. Conclusions: MRI features, including growth pattern, necrosis-related signal, and ADC values, are significantly associated with histological grade in UPS.
Comparison of dosimetric parameters and delivery accuracy between conformal radiotherapy-intensity modulated radiotherapy-combined and intensity-modulated radiotherapy planning for central lung cancer patients receiving stereotactic body radiotherapy
Background: With the development of technology, stereotactic body radiotherapy (SBRT) has been increasingly applied in the treatment of patients with central lung cancer. Three-dimensional conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) are common SBRT techniques for lung cancer. Although 3DCRT plans have low complexity, the quality of such plans is highly dependent on the experience of the planner. IMRT plans can provide better organ-at-risk (OAR) sparing, but they are associated with a high degree of modulation. Therefore, an SBRT planning strategy that combines the advantages of 3DCRT and IMRT has been developed. The purpose of this study was to investigate the differences in dosimetric parameters and delivery accuracy between CRT-IMRT-combined (Co-CRIM) and IMRT plans for central lung cancer patients treated with SBRT. Methods: Twenty patients were retrospectively included in this study. Four plans were designed for each patient. Co-CRIM-E and IMRT-E plans were designed based on Co-CRIM and IMRT, and delivered with Varian Edge. Co-CRIM-T and IMRT-T plans were designed based on Co-CRIM and IMRT, and delivered with Varian TrueBeam. Dosimetric parameters were compared between Co-CRIM-E and IMRT-E plans, as well as between Co-CRIM-T and IMRT-T plans. Monitor unit (MU), modulation factor (MF) and gamma (γ) passing rate were calculated for each plan. Paired-samples t-test was used to check the differences between datasets and P<0.05 was considered statistically significant. Results: Conformity index (CI), ratio of 50% prescription isodose volume to the planning target volume (PTV) volume (R50) and maximum dose (in % of dose prescribed) at 2 cm from PTV in any direction (D2cm) were significantly higher in Co-CRIM-E plans than those in IMRT-E plans. The CI, D2cm in Co-CRIM-T plans were also significantly higher than those in IMRT-T plans. Mean lung dose (MLD), percentage of the total lung volume receiving more than 10 Gy (V10), 12.5 Gy (V12.5), 13.5 Gy (V13.5) and 20 Gy (V20) were significantly higher in Co-CRIM-E plans than those in IMRT-E plans. MLD, V10, V12.5, V13.5 and V20 of total lung in Co-CRIM-T plans were also significantly higher than those in IMRT-T plans. MU and MF were significantly lower in Co-CRIM-E and Co-CRIM-T plans than those in IMRT-E and IMRT-T plans and γ passing rate was significantly higher than that in IMRT-E and IMRT-T plan. Conclusions: For central lung cancer patients treated with SBRT, the Co-CRIM approach was able to achieve plans that met clinical constraints. Compared with IMRT, Co-CRIM significantly reduced plan complexity and improved delivery accuracy with slightly reduced target conformability.
Fine-tuned multimodal GPT-4o for generating diagnostic impressions in breast magnetic resonance imaging: insights into non-mass enhancement lesions
Background: Previous studies have reported limited performance of publicly available large language models for Breast Imaging Reporting and Data System (BI-RADS) categorization of breast lesions. However, their application to breast non-mass enhancement (NME) lesions has not been specifically investigated. This study evaluated the diagnostic capability of multimodally fine-tuned ChatGPT-4 omni [generative pre-trained transformer 4 omni (GPT-4o)] for breast NME lesions, focusing on the accuracy of generated diagnostic impressions. Methods: In this retrospective study, magnetic resonance imaging (MRI) contrast-enhanced images, radiology reports, and clinical histories of 229 patients with breast NME lesions were collected. Six models were developed across three settings: zero-shot without fine-tuning (A1 and A2), single-modality fine-tuning (B and C), and multimodal fine-tuning (D and E). The models were evaluated for their ability to generate summarized imaging reports. Their diagnostic performance was compared against that of three radiologists with varying experience levels. Finally, the models were comprehensively assessed by the three additional experts using a five-point Likert scale. Results: The zero-shot models achieved area under the receiver operating characteristic curve (AUC) of 0.67–0.68, slightly outperforming junior radiologists but remaining inferior to experienced radiologists. Among the fine-tuned models, the image-only model (Model B) showed the poorest performance [AUC, 0.56; 95% confidence interval (CI): 0.45–0.66], significantly inferior to the other models (all P≤0.047). In contrast, the text-only and multimodal fine-tuned models showed substantial performance improvements. Model E achieved the best performance, with an AUC of 0.81 (95% CI: 0.74–0.89), significantly outperforming the zero-shot, image-only, and text-only models (all P≤0.039). Model E also outperformed junior radiologists (P<0.001), while showing no significant difference compared with intermediate and senior radiologists (P=0.060 and 0.367, respectively). The average expert evaluation score for Model E was 4.94/5. Compared with senior radiologists, specificity increased by 14.0%, whereas sensitivity decreased by 5.3%. Agreement for model-generated BI-RADS assessments was moderate both within the model outputs (κ=0.47) and between the model and senior radiologists (κ=0.49). Conclusions: Multimodally fine-tuned GPT-4o can generate clinically accurate diagnostic impressions for complex breast NME lesions with performance approaching that of experienced radiologists. The model can improve diagnostic specificity and may serve as a useful second reader for breast MRI interpretation, particularly for less experienced radiologists, although expert supervision remains necessary because sensitivity was slightly lower than that of senior radiologists.
Impact of deep-learning image reconstruction on multiplexed sensitivity encoding diffusion-weighted imaging in the female pelvis
Background: Diffusion-weighted imaging (DWI) in the female pelvis is degraded by artefacts. The study aimed to evaluate the impact of deep-learning image reconstruction (DLRecon) on the image quality of multiplexed sensitivity encoding (MUSE) DWI in the female pelvis. Methods: Female patients scheduled for pelvic magnetic resonance imaging (MRI) with 2-shot MUSE DWI were prospectively recruited. A subset of patients underwent paired 4-shot MUSE DWI. Images were qualitatively reviewed by two radiologists in terms of overall image quality, artefacts, lesions conspicuity and sharpness using a 5-point scale, with and without DLRecon. Intra-observer and inter-observer agreements were evaluated. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) of normal structures and lesions were quantitatively compared between 2-shot MUSE with and without DLRecon. Statistical analyses were performed using Cohen’s kappa, Wilcoxon signed-rank test and paired-sample t-test with Bonferroni correction. Results: Among the 65 female patients evaluated, 27 malignant lesions and 32 benign lesions were detected, while no pathology was found in 6 patients. DLRecon significantly improved the image quality in all aspects evaluated compared to 2-shot MUSE without DLRecon: overall image quality, artefacts, lesions conspicuity and sharpness (N=65, P<0.001). Furthermore, there was no statistical difference in the image quality between 2-shot MUSE with DLRecon and 4-shot MUSE without DLRecon (N=42, P=0.079–0.225). Inter- and intra-observer agreements were substantial to excellent (κ=0.664–0.815). Both SNR and CNR were increased with DLRecon (P<0.050); while no statistical difference was observed in ADC measurements in gluteus muscle (P=0.194), uterus (P=0.127) and lesions (P=0.414). Conclusions: DLRecon improved image quality of MUSE DWI in the female pelvis by reducing impact of artefacts, improving lesion conspicuity and image sharpness, while increasing SNR and CNR but preserving the stability of ADC quantification.
A predictive model combining contrast-enhanced ultrasound and Shamblin classification for risk stratification of internal carotid artery resection in carotid body tumor surgery
Background: Carotid body tumors (CBTs) are rare paragangliomas at the carotid bifurcation. Surgical resection is the primary treatment; however, internal carotid artery (ICA) resection may be required and carries high risks of cerebral ischemia and complex reconstruction. The conventional Shamblin classification, based on arterial encasement, does not reliably predict ICA resection. This study aimed to evaluate whether the tumor-ICA interface on contrast-enhanced ultrasound (CEUS) can serve as a predictor of ICA resection, and to develop a combined predictive model for risk stratification. Methods: This prospective study included 54 patients with 59 CBT lesions who underwent preoperative CEUS and subsequent surgery at a tertiary referral center between December 2022 and March 2025. The CEUS interface was qualitatively assessed as present or absent. The primary outcome was intraoperative ICA management (preservation vs. resection). Univariate analyses were performed, followed by multivariate analysis using Firth’s penalized‑likelihood regression. Predictive models based on each independent predictor and their combination were constructed, and their performance was compared using the area under the curve (AUC) of the receiver operating characteristic (ROC). A risk-stratification system was then established based on the combined model. Results: ICA resection was required in 12 of the 59 lesions (20.3%). The multivariate analysis identified the CEUS interface [odds ratio (OR) 40.46, 95% confidence interval (CI): 4.55–1190.09, P<0.001] and the Shamblin grade (OR 11.92, 95% CI: 1.02–314.23, P=0.049) as independent predictors of ICA resection in CBT surgery. The combined model achieved an AUC of 0.949 (95% CI: 0.860–1.000), compared with 0.906 (95% CI: 0.794–1.000) for the CEUS interface alone and 0.843 (95% CI: 0.710–0.976) for the Shamblin grade alone. The model stratified patients into four distinct risk tiers, with the predicted probability of ICA resection ranging from 3.1% (CEUS interface present and Shamblin grade I/II) to 96.3% (CEUS interface absent and Shamblin grade III). Conclusions: The CEUS interface is a strong preoperative predictor of ICA resection in CBT surgery. When combined with the Shamblin grade, it provides a promising predictive model for risk stratification that may aid in surgical planning. However, external validation is needed before clinical implementation.
Comprehensive risk prediction of acute coronary syndrome by integrating coronary plaque morphology and hemodynamic characteristics
Background: Acute coronary syndrome (ACS) represents a severe manifestation of coronary artery disease (CAD). Although coronary computed tomography angiography (CCTA) enables the effective assessment of the anatomical characteristics of plaque and high-risk morphological features, its predictive accuracy for ACS remains limited. This study aimed to integrate computational fluid dynamics (CFD) techniques with CCTA image data to investigate the combined value of CCTA-derived plaque characteristics and CFD-derived local wall shear stress (WSS) for predicting ACS. Methods: This retrospective study included 85 patients who underwent CCTA between January 1, 2022, and January 31, 2024, and subsequently developed ACS, including acute myocardial infarction or unstable angina with plaque rupture. A total of 216 lesions were analyzed and classified as culprit or non-culprit lesions based on treatment status. CCTA-derived morphological features and CFD-calculated WSS parameters were compared between the 83 culprit and 133 non-culprit lesions. Multivariate logistic regression was used to identify independent predictors of ACS. Different predictive models were constructed, and their discriminative and reclassification performances were evaluated using the Harrell’s concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI). Results: Morphologically, culprit lesions exhibited more severe stenosis (70.6%±8.5% vs. 66.4%±7.8%, P<0.001), longer lesion length (15.8±8.4 vs. 12.1±7.4 mm, P=0.002), and a higher prevalence of high-risk plaque (HRP) (51.4% vs. 48.6%, P=0.02). Hemodynamically, the culprit lesions exhibited significantly higher total WSS {17.3 [interquartile range (IQR), 11.7–24.1] vs. 14.9 (IQR, 9.6–20.2) Pa, P=0.045} and proximal WSS (WSSprox) [10.3 (4.4–17.1) vs. 6.4 (3.4–11.4) Pa, P<0.001]. Stenosis, lesion length, HRP, and hemodynamic parameters were associated with the subsequent occurrence of ACS. The addition of hemodynamic parameters improved the ability of the model to predict and reclassify ACS. Among the individual indicators added to the model, WSSprox showed the highest C-index (0.767 vs. 0.733; P=0.048), as well as the greatest incremental and net reclassification improvement (NRI: 0.369, P<0.007; relative IDI: 0.048, P<0.001). Conclusions: CFD-derived non-invasive hemodynamic assessment, particularly WSS, significantly enhanced the ability of the model to predict ACS. The integration of non-invasive hemodynamic parameters may improve the identification of culprit lesions associated with future ACS, with relevant diagnostic and therapeutic implications.
Development of a clinical prediction model to guide the selective use of SPECT/CT in papillary thyroid cancer
Background: Radioiodine whole-body scan (WBS) is routinely performed after radioiodine therapy in papillary thyroid cancer (PTC), but its two-dimensional nature limits anatomical localization. The addition of single-photon emission computed tomography/computed tomography (SPECT/CT) improves diagnostic accuracy; however, routine use exposes patients to additional radiation, longer imaging time, and higher costs. This study aimed to establish a predictive model to identify which patients with PTC are more likely to benefit by adding SPECT/CT imaging after WBS, enabling selective rather than routine use. Methods: This retrospective study included 265 patients with pathologically confirmed PTC who underwent postoperative radioiodine therapy followed by WBS and SPECT/CT. Patients were classified into non-metastasis and metastasis groups according to WBS diagnosis. The WBS results were also compared with the SPECT/CT results with WBS, and accordingly, the patients were categorized into true-negative, false-negative, true-positive, and false-positive subgroups. Additionally, the least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to screen for significant clinical factors that could be applied to develop predictive models for the addition of SPECT/CT imaging. Finally, the performance of the predictive models was evaluated using nomograms and receiver operating characteristic (ROC) curves. Results: Of the 232 patients diagnosed as non-metastasis by WBS, 17 false-negative patients were identified via SPECT/CT. A comparison of the clinicopathological characteristics of the patients revealed statistically significant differences in tumor size and thyroglobulin (TG) levels between the true-negative and false-negative diagnosis subgroups. Furthermore, of the patients diagnosed with metastases by WBS, SPECT/CT identified 22 true-positive patients and excluded 11 false-positive patients. Comparing the true-positive and false-positive patients of the metastasis group, we found that the American Joint Committee on Cancer (AJCC) stage was statistically low in the false-positive subgroup. Finally, the LASSO logistic regression model (LL-model) and the LASSO logistic regression model with clinical factors (Clin-model) were established to predict the utilization of SPECT/CT imaging, which produces an area under the curve (AUC) of 0.814 [95% confidence interval (CI): 0.725–0.902] and 0.855 (95% CI: 0.778–0.932). Conclusions: Our predictive algorithm integrated standard clinical variables (sex, tumor size, TG levels), WBS and SPECT/CT results through optimized LASSO regression. Additionally, evaluation of the model indicated optimal results that can be used to facilitate individualized prediction of PTC patients requiring additional SPECT/CT imaging.
Quantitative evaluation of key factors influencing hepatic oxygen saturation measurement by photoacoustic imaging in mice
Background: Photoacoustic imaging (PAI) has emerged as a promising non-invasive technique for assessing hepatic oxygen saturation (sO2). However, systematic evaluation of its baseline characteristics and influencing factors in normal liver tissue remains limited. This study aimed to establish a standardized PAI protocol for hepatic sO2 measurement in normal C57BL/6 mice and identify key factors contributing to measurement variability. Methods: A total of 48 healthy C57BL/6 mice (24 males and 24 females, aged 7–8 weeks) were used. Inter- and intra-observer reliability were assessed. The effects of multiple factors, including feeding status, hepatic lobe location, scanning plane, measurement approach (percutaneous vs. laparotomy), sex, region of interest (ROI) depth, and ROI size, on hepatic sO2 were quantitatively analyzed using a Vevo LAZR-X PAI system. Results: Excellent inter- and intra-observer reliability were observed. Hepatic sO2 exhibited significant variation across feeding states and hepatic lobe locations, between sexes, and between percutaneous and laparotomy measurements (all P<0.001). Specifically, sO2 was higher in free-feeding than fasted mice. The left lobe showed higher sO2 and greater variability than the median and right lobes. Post-laparotomy measurement yielded significantly higher sO2 values (63.06%±6.06%) than percutaneous measurement (45.08%±4.58%). Female mice had higher sO2 (48.79%) than did male mice (45.12%) (all P<0.001). In contrast, scanning plane, ROI depth (1 vs. 2 mm), and ROI size (0.7 vs. 7 mm2) had no significant effect on sO2 measurements (all P>0.05). Conclusions: These findings identify key influencing factors for hepatic sO2 measurement and provide a foundation for standardized PAI protocols in hepatic research. These results enhance the methodological rigor of PAI applications in hepatic research and offer valuable insights for the clinical translation of this non-invasive imaging modality for evaluating hepatic oxygen metabolism.
Postoperative lung geometric repositioning after right upper lobectomy: the role of thoracic morphology configuration
Background: Right upper lobectomy (RUL) is the most frequently performed lobectomy for lung cancer, yet postoperative functional loss varies widely among patients. We hypothesized that thoracic morphology constrains three-dimensional (3D) repositioning of the residual lung after RUL, leading to characteristic bronchial axis deviation and rotation-like deformation, and that these geometric changes might be associated with postoperative residual-lobe expansion. Methods: We retrospectively analyzed 26 patients who underwent RUL (2016–2018), including five with middle lobe atelectasis (MLA). Paired pre- and postoperative computed tomography was used to quantify residual-lobe volumes and geometric markers (thoracic morphology, sagittal bronchial axis deviation of B4/5 and B6, and plane-based rotation-like deformation) and to test their associations with repositioning patterns and postoperative expansion. Results: Two distinct residual-lung repositioning patterns were identified: a B6-dominant pattern, in which the superior segment (S6) ascended to occupy the postoperative apical space (Type B6), and a B4/5-dominant pattern, in which the middle lobe migrated cranially (Type B4/5). Type B6 was associated with barrel-shaped thoraces, whereas Type B4/5 was associated with flatter thoraces [thoracic height-to-anteroposterior diameter (H/AP) ratio, P=0.001; 8th-rib angle, P<0.001]. Rotation-like deformation patterns differed between the two types and supported their geometric distinction. However, neither thoracic morphology, repositioning pattern, bronchial axis deviation, nor rotation-like metrics were associated with postoperative middle-plus-lower lobe volume expansion (Type B6 vs. Type B4/5, P=0.42; H/AP ratio, P=0.28; 8th-rib angle, P=0.61; ΔB4/5, P=0.06; ΔB6, P=0.65; ΔY, P=0.68; ΔX, P=0.69). MLA occurred in both patterns (Type B6, n=2; Type B4/5, n=3) and was associated with reduced postoperative expansion [middle + lower volume ratio (post/pre), P=0.02] but showed no distinctive geometric predictors. Conclusions: Thoracic morphology influences 3D residual-lung repositioning after RUL, producing characteristic geometric patterns. Nevertheless, these geometric responses alone do not explain postoperative residual-lobe expansion, highlighting the need to integrate functional determinants with geometric assessment.
7.0 Tesla MRI in fetal autopsy: a preliminary exploration of integrating image optimization with clinical practice in a multidisciplinary workflow
Background: Fetal and neonatal autopsies are crucial for determining causes of death and guiding future pregnancies; however, due to cultural and personal factors, their clinical application rates are decreasing. Noninvasive postmortem magnetic resonance imaging (PMMRI) has emerged as an alternative to fetal and neonatal autopsies. Ultra-high-field 7.0 Tesla (7.0T) PMMRI provides near-microscopic spatial resolution, superior tissue contrast, and a high signal-to-noise ratio compared to lower-field magnetic resonance imaging (MRI). However, its broader clinical application is challenged by stringent specimen preservation requirements, complex scanning parameters, and a lack of standardized multidisciplinary workflows. Thus, this study aimed to establish a multidisciplinary protocol for 7.0T PMMRI in fetuses, optimize the technical parameters based on practical experience, and validate its diagnostic consistency with pathological autopsy findings. Methods: This single-center workflow-optimization study included 20 fetuses delivered by induced labor (mean gestational age 26.4 weeks). Of the 20 fetuses, 19 underwent final pathological autopsy, and 13 had previously undergone in vivo 1.5T MRI evaluations. A Plan-Do-Check-Act (PDCA) cycle was systematically applied to refine cadaver preparation, imaging sequences, and multidisciplinary collaboration. Diagnostic consistency among in vivo 1.5T MRI, postmortem 7.0T PMMRI, and pathological autopsy was statistically analyzed using Cohen’s kappa coefficient. Results: The 7.0T PMMRI workflow was optimized across five key aspects: body selection and preparation, 7.0T MRI setup, personnel readiness, data processing, and multidisciplinary death review. To ensure image quality and minimize issues related to freezing, compression, and autolysis, strict postmortem pretreatments were established, including that imaging should be conducted within 24 hours at 4 ℃. Scanning protocols were optimized to a duration of 60–120 minutes through the selection of appropriate magnetic resonance (MR) coils and sequences [e.g., T2-weighted imaging spin-echo (T2WI-SE) and susceptibility-weighted imaging (SWI)]. The consistency analysis revealed excellent diagnostic agreement between 7.0T PMMRI and pathological autopsy (Cohen’s kappa =0.872, P<0.001), a value significantly higher than that between in vivo 1.5T MRI and autopsy (Cohen’s kappa =0.22). Conclusions: Integrating 7.0T PMMRI into a structured multidisciplinary workflow via the PDCA cycle effectively improved standardization, operational efficiency, and image quality. High-resolution 7.0T PMMRI exhibited high diagnostic consistency with traditional autopsy, supporting its role as a valuable and reliable noninvasive alternative for fetal postmortem examinations.
Correlation between the coronary computed tomography angiography-derived pericoronary fat attenuation index and coronary artery lesions in Kawasaki disease
Background: Kawasaki disease (KD), a type of acute systemic vasculitis that affects children, involves the coronary arteries and causes coronary artery lesions (CALs), including dilation, aneurysms, stenosis, and thrombosis. The pericoronary fat attenuation index (FAI), a novel biomarker of perivascular inflammation derived from coronary computed tomography angiography (CCTA), has been extensively validated in the context of adult coronary artery disease for stratifying plaque vulnerability and predicting future cardiac events. This study aimed to investigate the correlation between the FAI and the presence and severity of CALs in patients with KD. Methods: We retrospectively analyzed the coronary computed tomography angiography (CTA) datasets of 71 patients with KD (40 with documented CALs and 31 without CALs) and 30 age- and sex-matched controls. Pericoronary FAI was measured around the proximal segments of all major coronary arteries. The global FAI values and their differences between groups were analyzed. The FAI values, clinical data, and coronary artery dimensions (Z-scores) were compared between groups. In statistical analyses, independent samples t-tests, Pearson or Spearman correlation coefficients, and multivariate logistic regression were applied to determine the independent association between FAI and CALs. Results: Pericoronary FAI values were significantly higher in patients with KD and CALs than in those without CALs [−69.2±5.0 vs. −76.0±7.2 Hounsfield units (HU), P<0.001] and were higher in all patients with KD than in healthy controls (−72.1±6.9 vs. −77.6±7.5 HU, P<0.001). A significant positive correlation was observed between elevated FAI and the inflammation marker, peak erythrocyte sedimentation rate (ESR) (r=0.39; P<0.001), and severity of CALs, including the maximum coronary artery size (r=0.31; P<0.05) and Z-score (r=0.34; P<0.05). Multivariate analysis identified FAI as an independent factor associated with the presence of CALs [odds ratio (OR) =1.32; 95% confidence interval (CI): 1.15–1.51; P<0.001]. In predicting CALs in patients with KD, the FAI provided additional value [area under the curve (AUC) =0.935; 95% CI: 0.885–0.986] when combined with clinical parameters. Conclusions: The pericoronary FAI is significantly elevated in patients with KD and CALs and correlates with the severity of coronary involvement. FAI derived from routine coronary CTA may serve as a novel, noninvasive imaging biomarker for quantifying pericoronary inflammation and stratifying risk in patients with KD.
CEUS-based quantitative analysis of washout time in focal liver lesions affects hepatocellular carcinoma diagnosis
Background: According to the American Association for the Study of Liver Diseases (AASLD) 2023 guidelines, contrast-enhanced ultrasound (CEUS) is a valuable complementary imaging modality for characterizing focal liver lesions (FLLs) that are not definitively diagnosed by contrast-enhanced computed tomography (CECT) or contrast-enhanced magnetic resonance imaging (CEMRI). This study compared the washout time of FLLs assessed by quantitative analysis (QA) and visual analysis (VA) on CEUS, evaluated their effects on CEUS Liver Imaging Reporting and Data System (LI-RADS) classification, and assessed their diagnostic performance in hepatocellular carcinoma (HCC). Furthermore, we sought to identify the QA-derived optimal washout time threshold for HCC diagnosis. Methods: Patients at high risk for HCC who underwent CEUS examinations at our hospital between April 2020 and October 2021 were retrospectively enrolled. All FLLs were pathologically confirmed. Washout time was assessed quantitatively using VueBox® software (QA) and visually by radiologists (VA). LR-5 was defined by CEUS LI-RADS (2017) as the category indicating liver lesions that are definitely HCC. QA LR-5 and VA LR-5 were defined as LR-5 categories assigned according to CEUS LI-RADS (version 2017) based on QA and VA, respectively. Furthermore, a modified washout time threshold was applied in the QA group to determine the optimal cutoff for HCC diagnosis, resulting in a new category termed “modified QA LR-5”. The Wilcoxon signed-rank test was used to compare the washout time between QA and VA, and the diagnostic performance of VA LR-5, QA LR-5, and modified QA LR-5 for HCC was compared using the McNemar test. Results: A total of 233 FLLs from 225 patients were included, comprising 220 malignant lesions (184 HCCs) and 13 benign lesions. Median washout time of all FLLs was 55–59 seconds by QA and ≥60 seconds by VA (Z=−6.185, P<0.001). For HCCs, the median washout time was ≥60 seconds with both QA and VA, although QA detected earlier onset (Z=−6.022, P<0.001). For HCC diagnosis, QA LR-5 showed lower sensitivity (51.6% vs. 66.3%, P<0.001), accuracy (60.5% vs. 72.1%, P<0.001) and area under the receiver operating characteristic curve (AUC) (0.728 vs. 0.801, P<0.001) than VA LR-5, while maintaining comparable specificity (93.9% vs. 93.9%, P>0.05). With a 40-second threshold, modified QA LR-5 significantly improved in sensitivity (79.9% vs. 66.3%, P<0.001) and accuracy (80.7% vs. 72.1%, P<0.001), without significant loss of specificity (83.7% vs. 93.9%, P>0.05) or AUC (0.818 vs. 0.801, P>0.05) compared with VA LR-5. Conclusions: QA reveals earlier washout than VA, while both methods show good agreement with CEUS LI-RADS classification. Modifying the washout threshold from 60 to 40 seconds significantly improves the diagnostic performance of QA LR-5, supporting recalibration of CEUS LI-RADS for quantitative assessment, likely because QA is more sensitive in detecting early contrast clearance.
Lateralization-specific motor network reorganization in pontine infarction revealed by resting-state functional connectivity magnetic resonance imaging
Background: Pontine infarction (PI) accounts for 7% of ischemic strokes, yet motor recovery varies significantly despite comparable lesion topography. The neural mechanisms underlying this heterogeneity remain unclear. This study aimed to investigate lesion laterality–dependent functional reorganization within the motor execution network following PI through the use of region of interest (ROI)-based resting-state functional magnetic resonance imaging (rs-fMRI). Methods: A total of 31 patients with acute unilateral PI [19 with left PI (LPI) and 12 with right PI (RPI)] and 31 matched controls underwent rs-fMRI. Seed-based functional connectivity (FC) analysis of the motor execution network was performed with spherical ROIs (6-mm radius). Group differences in FC were tested with one-way analysis of variance (ANOVA) and post hoc Bonferroni correction [cluster-level family-wise error (FWE)-corrected P<0.05]. Correlations between FC, infarct volume, and National Institute of Health Stroke Scale (NIHSS) scores were assessed. Results: No significant intergroup differences were observed in age, education years, head motion parameters, or gender distribution (P>0.05). Lesion volumes during the acute phase and NIHSS scores did not differ significantly between the LPI and RPI groups (P>0.05). In patients with LPI, FC was enhanced between the right dorsolateral prefrontal cortex (DLPFC) and the thalamus/basal ganglia, between the right supplementary motor area (SMA)/precentral gyrus and ipsilateral basal ganglia, and between the thalamus and bilateral cerebellum (all cluster-level FWE-corrected P values <0.05). In contrast, FC was reduced within the ipsilateral motor cortex and between the ventral premotor cortex and sensory cortex (all cluster-level FWE-corrected P values <0.05). In patients with RPI, FC was enhanced between the posterior cingulate cortex (PCC)/precuneus and thalamus and between the left SMA and contralateral basal ganglia (all cluster-level FWE-corrected P values <0.05). Within the LPI group, the FC between the right DLPFC and thalamus correlated positively with infarct volume (r=0.575; P=0.012). However, no significant correlations were observed between FC alterations and NIHSS scores or the NIHSS motor subscores (upper and lower extremity items), respectively, in either the LPI or RPI groups (all P values >0.05). Conclusions: Motor recovery post-PI may be influenced by dynamic imbalances in multinetwork early alterations and potentially shaped by lesion laterality: left-sided lesions primarily show recruitment of contralateral prefrontal cognitive resources, whereas right-sided lesions appear to engage default mode network (DMN)-mediated spatial remapping. The thalamo-basal ganglia hub may orchestrate transhemispheric integration, and its FC alterations, such as those within the DLPFC-thalamic pathway, might reflect compensatory potential, potentially offering insights into rehabilitation strategies. However, longitudinal studies are needed to validate these preliminary findings.
Clinical application of musculoskeletal for supraspinatus tendon tears with fat infiltration
Background: There is a relative scarcity of studies focusing on fat infiltration in the supraspinatus muscle using musculoskeletal ultrasound (MSK US). This study aimed to investigate the clinical application value of MSK US in assessing fat infiltration of the supraspinatus muscle in patients with supraspinatus tendon tears. Methods: We conducted a retrospective analysis of 154 patients diagnosed with supraspinatus tendon tears by arthroscopy. All patients underwent semi-quantitative grading of supraspinatus muscle fat infiltration using MSK US, complemented by shear wave elastography (SWE). Magnetic resonance imaging (MRI) served as the gold standard. Diagnostic performance metrics were calculated for MSK US in detecting fat infiltration, and Kappa consistency analysis was assessed between MSK US and MRI findings. Additionally, Spearman correlation analysis was performed to evaluate the relationship between the degree of fat infiltration quantified by MSK US and the extent of supraspinatus tendon tear as determined arthroscopically. Results: Arthroscopic results revealed full-thickness tears in 50 patients (32.47%, 50/154), and partial tears in 104 patients (67.53%, 104/154). In terms of tear grades by arthroscopy, 22 cases were Grade I (14.29%, 22/154), 38 were Grade II (24.68%, 38/154), and 94 were Grade III (61.04%, 94/154). The SWE measurements demonstrated significantly lower shear wave velocity in the affected supraspinatus muscle (2.52±0.56 cm/s) compared with the healthy side (3.03±0.64 cm/s; t=7.42, P<0.001). Using MRI as a reference standard, MSK US achieved a sensitivity of 94.90%, specificity of 82.14%, positive predictive value of 90.29%, negative predictive value of 90.20%, accuracy rate of 90.26%, and an area under the receiver operating characteristic (ROC) curve of 0.89. A statistically significant positive correlation was observed between the degree of supraspinatus muscle fat infiltration assessed by MSK US and the extent of supraspinatus tendon tear determined arthroscopically (rs=0.47, P<0.001). Conclusions: The fat infiltration in the supraspinatus muscle, as assessed by MSK US, demonstrated moderate consistency with MRI diagnoses, underscoring its high clinical value. A strong association was observed between supraspinatus muscle fat infiltration and the severity of supraspinatus tendon tears. Specifically, higher-grade tendon tears were significantly correlated with greater degrees of fat infiltration within the supraspinatus muscle.
Detecting clinically significant prostate cancer with a distributed parameter model based on quantitative dynamic contrast-enhanced magnetic resonance imaging
Background: Prostate cancer (PCa) is a common malignancy with heterogeneous biological behavior, and accurate differentiation between clinically significant (csPCa) and clinically insignificant PCa (ciPCa) is critical for guiding treatment decisions. Multiparametric magnetic resonance imaging (mp-MRI) has become an essential tool for PCa detection; however, the role of dynamic contrast-enhanced MRI (DCE-MRI) remains limited in the current Prostate Imaging Reporting and Data System (PI-RADS) guidelines due to its largely qualitative nature. Quantitative analysis of DCE-MRI through use of advanced pharmacokinetic models may provide additional diagnostic value. This study aimed to evaluate the feasibility of a distributed parameter (DP) model based on quantitative DCE-MRI to differentiate csPCa from ciPCa. Methods: Patients with suspected PCa were prospectively enrolled and underwent 3.0-T DCE-MRI between June 2022 and May 2025. Voxel-wise kinetic parameters were estimated with the DP model, the extended Tofts model (ETM), and the adiabatic tissue homogeneity (ATH) model. Regions of interest (ROIs) were manually delineated by radiologists on parameter maps, including the permeability surface area product (PS) from the DP and ATH models and the transfer constant (Ktrans) maps from the ETM. Group comparisons were performed to evaluate the ability of individual DCE-derived parameters to differentiate ciPCa from csPCa, with the Gleason score serving as the reference standard. The diagnostic performance of these parameters was further assessed via receiver operating characteristic (ROC) analysis. In addition, the diagnostic performance of biparametric MRI and mp-MRI was compared to evaluate the incremental value of incorporating quantitative DCE parameters into the PI-RADS. Results: A total of 70 patients comprising 88 biopsy-proven lesions (42 ciPCas and 46 csPCas) were included. Several DP model-derived parameters (PS, mean transit time, plasma volume, and blood flow) were significantly higher in patients with csPCa than in those with ciPCa (all P<0.05), with PS showing the strongest discriminative ability (P<0.001). ROC analysis revealed that DP-derived PS achieved the highest performance [area under the curve (AUC) =0.87, sensitivity =0.74, specificity =0.83, and accuracy =0.78], outperforming parameters derived from the ETM (AUC ≤0.71) and ATH model (AUC ≤0.82). Subgroup analyses stratified by lesion zone and size consistently confirmed the superiority of the DP model. Incorporation of DCE parameters into mp-MRI improved diagnostic performance as compared with biparametric MRI (AUC: 0.92 vs. 0.88). Conclusions: Quantitative DCE-MRI based on a DP model outperformed the ETM and ATH model in distinguishing csPCa from ciPCa. Among all evaluated parameters, PS derived from the DP model demonstrated the strongest diagnostic ability, supporting its potential as a robust quantitative imaging biomarker for PCa risk stratification.
The value of [18F]PSMA-1007 PET/CT combined with mpMRI in diagnosing prostate cancer with PI-RADS 3–4 lesions
Background: Accurate differentiation between malignant and benign prostate lesions remains challenging, particularly in patients with Prostate Imaging Reporting and Data System (PI-RADS) 3–4 scores. This study aimed to compare the diagnostic performance of fluorine-18 prostate-specific membrane antigen-1007 ([18F]PSMA-1007) positron emission tomography/computed tomography (PET/CT) and multiparametric magnetic resonance imaging (mpMRI), and to establish a multiparameter model for improving prostate cancer (PCa) detection. Methods: A retrospective analysis was conducted on 135 patients with PI-RADS 3–4 lesions who underwent [18F]PSMA-1007 PET/CT between 2 February 2024 and 30 September 2025. Clinical parameters [age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA)/total prostate-specific antigen (tPSA), prostate volume (PV), prostate-specific antigen density (PSAD)] and imaging variables [maximum standardized uptake value (SUVmax), lesion-to-background ratio (LBR)] were analyzed. Group comparisons were performed using Student’s t-test or Mann-Whitney U test. Diagnostic performance was evaluated by sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV), with paired comparisons using McNemar’s test. Agreement with pathology was assessed using Cohen’s kappa. Logistic regression identified predictors of PCa. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess and compare diagnostic performance. Results: Significant differences between benign prostatic hyperplasia (BPH) and PCa groups were observed in age, PSA, fPSA/tPSA, PV, PSAD, SUVmax, and LBR (all P<0.05), whereas PSA category showed no significant difference (P>0.05). In the overall cohort, [18F]PSMA-1007 PET/CT demonstrated higher sensitivity (95.5% vs. 73.1%, P<0.001) and accuracy (83.0% vs. 68.1%, P=0.009) than mpMRI, and showed better agreement with pathology (κ=0.66 vs. 0.36). In the PI-RADS 3 subgroup, SUVmax (P=0.003) and LBR (P=0.002) were significant predictors in univariate analysis, whereas SUVmax showed borderline significance in multivariate analysis [odds ratio (OR) =1.26, 95% confidence interval (CI): 0.999–1.590, P=0.051]. The combined model did not significantly improve diagnostic performance (P>0.05). In the PI-RADS 4 subgroup, PV was an independent predictor. The combined model showed better performance than PV alone (P<0.01), but was not superior to SUVmax or LBR alone (P>0.05). Conclusions: Our findings suggest that [18F]PSMA-1007 PET/CT may offer better diagnostic performance compared to mpMRI in patients with PI-RADS 3–4. Combining PV with PET/CT parameters may improve diagnostic accuracy in PI-RADS 4 lesions, whereas no additional benefit is observed for combined models in PI-RADS 3 lesions. Further large-scale validation is required.
Value of the DeepSeek-R1 large language model in extracting structured data from magnetic resonance imaging reports of rectal cancer and assisting in tumor staging
Background: Structured magnetic resonance imaging (MRI) reports improve rectal cancer diagnosis and treatment management, whereas free-text reports better describe complex MRI features, leading to divergent preferences and inconsistent acceptance among radiologists. This study aimed to evaluate the potential of DeepSeek-R1 to assist in the analysis of MRI reports for rectal cancer. Methods: This retrospective study analyzed 465 MRI reports of rectal cancer. Sixty reports were used to facilitate structured information extraction, refine reporting reminders and complete preliminary screening of large language models (LLMs). LLMs from the same developers were compared [i.e., GPT-4 vs. GPT-4o, Wenxinyiyan-4 vs. Wenxinyiyan-4Turbo, and DeepSeek-R1-7B vs. DeepSeek-R1-32B, vs. DeepSeek-R1-671B (DSR1-671B)], and the accuracy and average processing time of the LLMs were evaluated with paired-samples t-tests, one-way repeated-measures analysis of variance, McNemar tests, and Cochran Q tests. The top-performing model was selected for tumor-node (TN) staging determination. Both a five-point Likert scale and accuracy were employed as performance metrics for evaluating two prompting strategies (default knowledge and in-context knowledge) in TN staging determination, and any confabulations were documented. To assess reproducibility, the LLM analysis was independently repeated three times for all reports. Radiologists’ consensus interpretation of the report text served as the reference standard. Results: Five of the LLMs evaluated, including DSR1-671B, Wenxinyiyan-4, Wenxinyiyan-4Turbo, GPT-4, and GPT-4o, were identified in the preliminary screening by the same developers. All five LLMs processed reports significantly faster than did radiologists (time per report: 22.4–84.6 vs. 132.1 s; P<0.01). DSR1-671B outperformed all other LLMs in the extraction of structured information and the generation of reporting cues, with an accuracy of 98.9% (P<0.01). Thus, DSR1-671B was selected for TN staging reasoning. The in-context knowledge strategy yielded a higher median score for TN stage than did the default knowledge strategy (5.0 vs. 4.0; P<0.01) and also yielded a higher accuracy in T substaging (86.5% vs. 72.3%; P<0.01) and N substaging (95.0% vs. 77.2%; P<0.01). Significant differences in the number of events and confabulation rates between the two prompting strategies were identified by the paired-samples Wilcoxon test (P<0.01), although their medians were equal. However, the basis for TN staging of the in-context knowledge strategy was more robust than that of the default knowledge strategy (mean number of events: 3.3 vs. 2.8) and produced a lower mean confabulation rate (2.9% vs. 4.1%). DSR1-671B showed good reproducibility across all tasks, and the lowest agreement was observed for N staging determination (Fleiss’ kappa value =0.932). Conclusions: In an English-language environment with human supervision, DSR1-671B can perform structured information extraction from MRI reports of rectal cancer, generate reporting cues, and enable TN staging based on the improved reports.
Persistent left superior vena cava causing change in cardiac chamber size: anatomical and hemodynamic changes
Background: Persistent left superior vena cava (PLSVC) is one of the most common abnormalities of the fetus during pregnancy. The aim of this study was to assess the utility of two-dimensional (2D) ultrasonography in evaluating fetal cardiac chamber sizes and major artery diameters for comparing isolated PLSVC with normal fetuses. Methods: We retrospectively analyzed 166 cases of isolated PLSVC and 166 normal fetuses, measuring heart chamber and major artery diameters. The thoracic transverse diameter (TTD), ventricle sinistrum (VS), ventriculus dexter (VD), atrium sinistrum (AS), and atrium dextrum (AD) were measured in the four-chamber view. The aortic diameter (AOD), pulmonary artery diameter (PAD), aortic velocity (AOV), and pulmonary artery velocity (PAV) were measured in the left and right ventricular outflow tract sections, respectively. The arterial duct diameter (ADD) was measured in the three-vessel-trachea (3VT) view. Results: Compared with normal fetuses, isolated PLSVC cases showed significant differences in VD, AD, AOD, PAD, ADD, AOV, and PAV (P<0.05). The ratios of cardiac chamber and major artery diameters, including VD/VS and AD/AS also differed significantly between the two groups (P<0.05). Fetal VD positively correlated with AD (R2=0.96, P<0.001) and PAD correlated positively with AOD (R2=0.54, P<0.001) in the isolated PLSVC group. AD achieved an area under the curve (AUC) of 0.674 with 47.59% sensitivity and 83.03% specificity, whereas AOD yielded an AUC of 0.695 with 50.00% sensitivity and 83.13% specificity. Combined use of AD and AOD enhanced diagnostic accuracy, demonstrating a positive predictive value of 81.2% and a negative predictive value of 75.6%. Conclusions: Fetal isolated PLSVC was associated with changes in cardiac chamber size, which may be important for prenatal diagnosis due to hemodynamic changes caused by coronary sinus dilation.
Ultrasound assessment of blood flow in branches of the external carotid artery as potential donors for microsurgical revascularisation of the central nervous system
Background: Vascular bypasses that connect branches of the superficial temporal artery (STA) to cerebral arteries are a recognised treatment option for carefully selected cases involving various cerebral vascular conditions. Among the different microsurgical techniques, one common method is the direct STA-middle cerebral artery (MCA) bypass. This study aimed to evaluate blood flow parameters in the STA and its branches in healthy volunteers. Methods: The study involved ultrasound measurements on a group of 157 adult volunteers without vascular disease. The male-to-female ratio was 1.01. Additionally, cardiovascular risk factors within the population were assessed. Blood flow parameters—peak systolic velocity (PSV), end-diastolic velocity (EDV), mean velocity (MnV), and minute flow—were obtained. Statistical analysis of the acquired values was performed, and the relation charts have been created. Results: The STA diameters ranged from 0.7 to 2.3 mm, with minute flow rates from 3 to over 100 mL/min. The sex ratio for each vessel size was approximately 1.15, favoring males (P value <0.001). Significantly higher flow values in right STA were observed in men (P value =0.01). Gender differences for left STA were borderline statistically significant (P value =0.051). Neither vessel diameter nor flow rate was influenced by age or cerebrovascular risk factors. No significant difference was observed between sides of the head, though individual cases showed notable left-right asymmetries. Both artery size and blood flow volume increased gradually with higher body weight and overall growth in both female and male groups. There was no strong correlation between the diameters and flow rates of the terminal branches and the trunk of the temporal artery (R value <0.57). Conclusions: Assessing the vessel intended as a graft donor directly is essential in all revascularisation procedures. Comparing patient-specific features to population references can aid in forecasting the success of bypass blood flow. Ultrasound measurement might provide a good compromise between precision and ease of access.
Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP)
Background: Magnetic resonance (MR) parametric maps provide quantitative tissue characteristics that are valuable for medical diagnosis. However, existing T1 and T2 measurement techniques confront challenges such as prolonged reconstruction/fitting times and the requirement for multi-sequence image registration, limiting the clinical applicability of quantitative parameter mapping. The development of compressed sensing combined with parallel imaging technology has improved reconstruction efficiency. However, traditional two-step workflow lacks spatial constraints for parameter mapping and suffers from slow pixel-level fitting. Furthermore, deep learning methods are applied to reconstruction to accelerate reconstruction and remove noise but exhibit heavy dependence on training datasets and neglect to incorporate inherent low-rank and sparse data constraints. To address these challenges, this study proposes the spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP) method. Methods: The method utilizes physical models for backpropagation of deep learning features, allowing physical priors to explicitly express the spatiotemporal correlations of images. This integration enables mutual enhancement between low-rank/sparse constraints and deep learning priors, thereby improving both constraint-based reconstruction and deep learning performances. Results: Experimental results from simulated brain data, real phantoms, and healthy volunteers demonstrate that the proposed method produces more accurate T1 and T2 maps than those obtained using conjugate gradient (CG), low-rank plus sparse (LS) matrix factorization least squares fitting, or conventional deep learning-based mapping methods. The method uses only 2–4% (spokes of 1,000 to 2,000) of full k-space data to simultaneously generate quantitative T1 and T2 maps, yet still achieves a structural similarity (SSIM) greater than 0.7 and a normalized root mean square error (nRMSE) lower than 0.1. The proposed method also yielded excellent Pearson correlation coefficients of R2=0.99 for T1 and R2=0.94 for T2. The entire process from reconstructing three dimensions isotropic weighted images with high spatiotemporal resolution to fitting approximately 152 corresponding T1 and T2 images has been accelerated by about 150 times compared to traditional methods. Conclusions: The proposed method extracts feature information through low-rank sparse priors and optimizes backpropagation via physical models, integrating the synergistic advantages of deep learning and low-rank sparse iterative processing to achieve concurrent T1 and T2 quantification.
68Ga-pentixafor PET pulmonary uptake in patients with suspected primary aldosteronism: incidence and etiology
Background: We have incidentally noted focal pulmonary uptake in a cohort of our patients with suspected primary aldosteronism (PA) on 68Ga-pentixafor positron emission tomography/computed tomography (PET/CT) or positron emission tomography/magnetic resonance (PET/MR) images. We aimed to assess the incidence and possible etiology of pulmonary uptake in these patients. Methods: There were 217 consecutive suspected PA patients referred for 68Ga-pentixafor PET/CT or PET/MR retrospectively reviewed. The presence or absence of pulmonary uptake was recorded. Clinical and PET-related parameters were collected. Modality-based analyses were performed to evaluate pulmonary uptake incidence and blood-pool standardized uptake value (SUV) measurements between PET/CT and PET/MR. Among patients with pulmonary uptake, the highest pulmonary maximum standardized uptake value (SUVmax) and blood pool (BP) normalized highest pulmonary SUVmax were also compared descriptively between modalities. Results: Pulmonary uptake was present in 37 of 217 patients (17%). The incidence of pulmonary uptake did not significantly differ between PET/CT and PET/MR groups (1/11, 9.1% vs. 36/206, 17.5%; P=0.695), and BP SUVmax and mean standardized uptake value (SUVmean) were also comparable between PET/CT and PET/MR groups (SUVmax: median, 2.99 vs. 2.68, P=0.075; SUVmean: median, 2.03 vs. 2.02, P=0.799). Among patients with pulmonary uptake, lesion-level SUV comparison was descriptive because only one patient underwent PET/CT; the highest pulmonary SUVmax and BP normalized highest pulmonary SUVmax were 8.15 and 4.20 in this patient, compared with median values of 4.25 and 2.07 in PET/MR patients, respectively. Among all clinical and PET-related parameters, only elevated leukocyte and neutrophil counts were observed to be significantly associated with pulmonary uptake (25.9% vs. 1.8%, P<0.001). Among patients with available laboratory data, the highest pulmonary SUVmax was not significantly correlated with absolute neutrophil count (Spearman r=−0.250, P=0.208) or leukocyte count (Spearman r=−0.283, P=0.153). In patients with uptake, median SUVmax showed no significant difference between left and right lungs (3.71 vs. 3.76, P=0.473). Additionally, no significant difference was observed between lower and upper lung fields (47% vs. 49%, P>0.99). Of these 37 patients, 20 patients had recent chest computed tomography (CT) scan. Abnormality was noted in one patient, who had subpleural linear opacity in the corresponding area. Conclusions: Pulmonary uptake was observed in approximately one-sixth of suspected PA patients undergoing 68Ga-pentixafor scans. Our findings suggest that pulmonary uptake on 68Ga-pentixafor scans may be associated with potential inflammation.
Sex differences in CT-FFR of myocardial bridging with or without atherosclerosis: an AI-based quantitative study
Background: Myocardial bridging (MB) is a prevalent coronary anomaly with potential links to major adverse cardiac events. While computed tomography-derived fractional flow reserve (FFRCT) offers a non-invasive functional assessment, current evidence predominantly treats MB as a homogeneous entity, overlooking potential sex differences in hemodynamic impact. Existing studies often fail to distinguish between isolated MB and MB with concomitant atherosclerosis, and rarely employ sex-stratified analyses. This study aimed to noninvasively assess sex-specific differences in FFRCT among patients with MB, with or without atherosclerosis, using an artificial intelligence (AI)-based platform to clarify the clinical implications of these differences. Methods: This retrospective cohort study included 300 left anterior descending artery (LAD)-MB patients, subdivided into an MB group (n=155) and an MB with atherosclerosis (MBLA) group (n=145), along with 104 controls with normal coronary computed tomography angiography (CCTA) findings. Demographic data, clinical symptoms, and risk factors were collected. Morphological parameters of MB were quantitatively analyzed using cardiac function post-processing software, and whole-vessel and local segments (proximal to MB, within MB, and distal to MB) FFRCT values were obtained via an AI-based platform (Shukun-FFRCT). Patients were stratified by FFRCT <0.8, and statistical analyses [t-tests, analysis of variance (ANOVA), Mann-Whitney U tests, and binary logistic regression] were applied to examine sex-based associations with FFRCT abnormalities and influencing factors. Results: (I) Demographic analysis revealed significantly higher proportions of male patients in the MB (52.9%) and MBLA (57.2%) groups compared to controls (24.0%, both P<0.05). (II) Sex-stratified analysis within pathological subgroups showed that in the MB group, females had significantly lower distal FFRCT values (FFR3: median 0.92 vs. 0.95, P=0.006) and higher trans-bridge pressure drop (ΔFFR: 0.08 vs. 0.05, P=0.004) than males. No significant sex-based differences in FFRCT were observed in the MBLA group (all P>0.05). (III) Intra-group comparisons of FFRCT between systolic and diastolic phases showed no significant differences in either case or control groups (all P>0.05). (IV) Binary logistic regression indicated that MB length was an independent risk factor for FFRCT abnormalities in both sexes. Each 1-mm increase in MB length raised the risk of FFRCT abnormality by 5.3% in males [odds ratio (OR) =1.053, 95% confidence interval (CI): 1.004–1.104, P=0.033] and 11.3% in females (OR =1.113, 95% CI: 1.024–1.209, P=0.011). Conclusions: This study innovatively demonstrates through CT-AI quantitative analysis that sex significantly influences FFRCT in LAD-MB patients. Females with isolated MB exhibit more pronounced distal hemodynamic alterations, whereas sex differences diminish when atherosclerosis coexists. Notably, MB length is an independent risk factor for FFRCT abnormalities, with a greater impact observed in females. These findings provide preliminary evidence to inform risk stratification of MB.
The value of multi-parametric ultrasonography nomogram in predicting nipple-areolar complex involvement in breast cancer
Background: Accurate assessment of nipple-areolar complex (NAC) involvement in breast cancer (BC) plays a key role in guiding preoperative surgical strategy. This study developed and evaluated a multi-parametric ultrasonography nomogram, incorporating conventional ultrasound (CUS), strain elastography (SE), and contrast-enhanced ultrasound (CEUS) to predict NAC involvement in BC. Methods: In this prospective study, consecutive patients were enrolled if they had a preoperative biopsy confirming primary BC and were scheduled for surgical treatment. All patients underwent CUS, SE, and CEUS examinations preoperatively. Histopathological findings of NAC after surgical resection served as the gold standard. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify the predictors most strongly associated with NAC involvement. The diagnostic performance of different model combinations (CUS, CUS + SE, CUS + SE + CEUS) was compared using receiver operating characteristic (ROC) analysis with the DeLong test. A multi-parametric ultrasonography nomogram was developed and internally validated by 500 bootstrap resamples, with calibration curve analysis assessing its predictive accuracy and decision curve analysis (DCA) evaluating its clinical utility. Results: A total of 166 lesions were enrolled from 164 patients, comprising 33 (19.9%) lesions with NAC involvement and 133 (80.1%) lesions with NAC non-involvement. LASSO regression identified four independent predictors: ductal echo extending from the lesion on CUS, the mean elastography strain value of nipple on SE, and ductal enhancement extending to the nipple and focal nipple enhancement on CEUS. The combined CUS + SE + CEUS model demonstrated the highest diagnostic efficacy with an area under the curve (AUC) of 0.917, a sensitivity of 90.91%, and a negative predictive value (NPV) of 97.32%. Its performance was significantly better than that of CUS alone (AUC =0.732, P<0.001) and CUS + SE (AUC =0.859, P=0.009). The nomogram based on multi-parametric ultrasonography showed good calibration and internal validation (bias corrected AUC =0.916). DCA confirmed net benefit across clinically relevant thresholds. Conclusions: The multi-parametric ultrasonography nomogram demonstrated excellent performance in predicting NAC involvement and offers a valuable, non-invasive tool for preoperative surgical planning.
Multi-position head-and-neck rotational CT venography evaluation of dynamic changes in internal jugular vein compression in patients with an elongated styloid process
Background: Neurosurgical procedures often require patients to maintain a rotated neck position for extended periods. This sustained posture may lead to internal jugular vein (IJV) compression, particularly in individuals with an elongated styloid process (ESP). This study aimed to elucidate the dynamic changes in IJV compression among patients with an ESP using multi-position head-and-neck rotational computed tomographic venography (CTV) imaging, and to preliminarily investigate the key anatomical structures responsible for venous compression, thereby providing imaging and anatomical evidence for individualized treatment planning. Methods: Images from 42 consecutive head-and-neck rotational CTV scans of patients with an ESP performed between August 2024 and December 2025 were reviewed. The slices for measurement were selected from images obtained in four positions (extension, flexion, left rotation, and right rotation) based on the criterion of the minimum distance between the IJV and its key adjacent structures (bony, muscular, and vascular). The shortest distance between these key structures and the IJV, as well as the minimum IJV diameter and cross-sectional area at the same slice level, was measured. The presence of collateral circulation in bilateral IJVs in each position was assessed, and a preliminary analysis of the compressive structures at the site of maximal narrowing of the IJV was performed. Results: A total of 42 patients were included in the study, of whom 30 were identified as positive for severe IJV compression or complete IJV interruption, yielding a positivity rate of 71.43%. At the carotid artery (CA) and sternocleidomastoid muscle (SCM) levels, the diameter and cross-sectional area of the ipsilateral IJV were significantly smaller during left and right neck rotation. In contrast, at the transverse process (TP) and styloid process (SP) levels, the ipsilateral IJV diameter and area were largest during left and right neck rotations. Further, among the four positions, flexion showed the shortest distance between the bilateral C1 TP and SP. During left and right neck rotation, the degree of collateral circulation around the ipsilateral IJV was significantly increased. All reported differences were statistically significant (P<0.05). A total of 240 vascular states were observed across the four positions, of which 80 (33.3%) exhibited severe stenosis or occlusion. Dual-structure compression was the predominant type, with the most common combinations being muscle-muscle compression (27/80, 33.75%), muscle–vessel compression (23/80, 28.75%), and bone-bone compression (11/80, 13.75%). Conclusions: In patients with an ESP, left and right neck rotation significantly increases the incidence of ipsilateral IJV compression and even occlusion, as assessed by multi-position head-and-neck rotational CTV. This finding has important clinical implications, as it may guide modifications to patients’ daily habits and improve perioperative evaluation protocols.
Prenatal prediction of neonatal respiratory distress syndrome using uteroplacental Doppler and placental microvascular perfusion indices: development of a risk model
Background: Neonatal respiratory distress syndrome (NRDS) is a leading cause of neonatal morbidity and mortality; however, current antenatal risk assessments lack physiological specificity. This study aimed to evaluate the early predictive value of mid-pregnancy placental perfusion indices derived from three-dimensional power Doppler (3D-PD) imaging—the vascularization index (VI), flow index (FI), and vascularization flow index (VFI)—together with conventional Doppler parameters [e.g., the uterine artery pulsatility index (UtA-PI), cerebroplacental ratio (CPR)] and maternal clinical factors for NRDS, and to develop and validate a nomogram for individualized risk stratification. Methods: In this retrospective study, pregnant women who underwent second-trimester ultrasound at a tertiary hospital in Xiangyang between January 2021 and December 2024 and had traceable delivery outcomes were screened (n=610). After applying the exclusion criteria, 362 women were included in the study (NRDS group, n=58; control group, n=304). Maternal variables and UtA-PI, umbilical artery PI (UA-PI), middle cerebral artery PI (MCA-PI), and CPR were collected; VI, FI, and VFI were computed offline after placental volume acquisition. The primary outcome was NRDS, defined as the need for ≥24 hours of oxygen/respiratory support and neonatal intensive care unit (NICU) admission. Univariate and multivariate logistic regression analyses were performed to identify independent predictors; a nomogram was constructed from the final model. Discrimination, calibration, and clinical utility were assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA), with internal validation by bootstrap resampling. Results: Compared with the control group, the NRDS group showed higher UtA-PI (1.15±0.38 vs. 1.02±0.35, P<0.05), lower CPR (1.53±0.27 vs. 1.62±0.30, P=0.003), and reduced VI and VFI (both P<0.05). In the multivariate analysis, UtA-PI [odds ratio (OR) =1.52, 95% confidence interval (CI): 1.14–2.02], CPR (OR =0.70, 95% CI: 0.57–0.86), VFI (OR =0.75, 95% CI: 0.63–0.90), and gestational hypertension (OR =3.08, 95% CI: 1.44–6.59) were independent predictors of NRDS. The combined nomogram achieved an area under the curve (AUC) of 0.849 (95% CI: 0.745–0.871) with good calibration (mean absolute error: 0.046). DCA demonstrated higher net benefit across threshold probabilities of 0.05–0.75. Conclusions: Integrating uteroplacental macrocirculatory resistance (UtA-PI and CPR) with microcirculatory perfusion (VFI) and key maternal factors enables effective early risk stratification for NRDS. The proposed nomogram is intuitive and practical, exhibits good discriminatory performance, and may guide delivery planning, perinatal monitoring, and timely intervention.
A generative adversarial network model for improved three-dimensional mapping of pulmonary arteries and veins from non-contrast computed tomography in sublobar resection planning
Background: Preoperative three-dimensional (3D) vascular reconstruction from non-contrast computed tomography (NCCT) is often hindered by limited vessel differentiation, whereas contrast-enhanced computed tomography (CECT) is contraindicated for certain patients. This study examined the use of a generative adversarial network (GAN) for synthetic CECT (Syn-CECT)-like images from NCCT, preliminarily evaluating its clinical potential to assist in perinodular vascular segmentation and 3D reconstruction. Methods: This retrospective study included 212 patients. To mitigate the spatial misalignments caused by respiratory motion, the training phase (130 patients) included paired virtual non-contrast (VNC) and true CECT images derived from spectral computed tomography (CT). A Pix2pixGAN framework integrating an attention mechanism and a 2.5-dimensional (2.5D) strategy was applied to synthesize Syn-CECT. The model was then validated on external test sets (82 patients) from two different CT vendors. A quantitative evaluation was conducted via objective image quality metrics. The vascular reconstruction performance within a 50-mm spherical region around pulmonary nodules was quantitatively assessed according to the local Dice similarity coefficient (LocalDice), fragmentation index (FragIndex), and 95th percentile Hausdorff distance (HD95), alongside subjective physician scoring. Results: In external testing, Syn-CECT showed favorable structural compared to original NCCT as indicated by the multiscale structural similarity index measurement. For the downstream 3D vascular segmentation task, Syn-CECT yielded measurable improvements over NCCT, reflected by a moderately higher LocalDice (0.89–0.90 vs. 0.85–0.89), a lower FragIndex (3.82–5.92 vs. 4.54–8.68), and a lower HD95 (0.08 vs. 0.09–0.12) (all P values <0.01), suggesting better structural continuity and boundary alignment. In terms of subjective evaluation, Syn-CECT received more favorable median vessel visibility scores (score of 4) than did NCCT (score of 3), offering improved visual clarity for evaluating local vasculature. Conclusions: Using a GAN model to synthesize CECT-like images from standard NCCT scans can moderately improve the continuity and accuracy of 3D pulmonary vascular reconstructions. This image translation strategy demonstrated preliminary clinical value as an adjunctive tool for sublobar resection planning and may serve as a practical alternative for obtaining enhanced vascular anatomical details, especially for patients with contraindications to iodinated contrast media.
Effect of smoking on the diagnostic performance of computational fluid dynamics-derived CT-derived fractional flow reserve: a cross-sectional study
Background: Previous studies have suggested that smoking may be associated with coronary microvascular dysfunction (CMD), which could theoretically impact computed tomography (CT)-derived fractional flow reserve (FFRct) reliability, but no direct evidence exists regarding FFRct performance in smoking populations. This study aimed to compare the diagnostic performance of FFRct between smokers and non-smokers using a personalized myocardial volume calibration approach in computational fluid dynamics (CFD) simulations. Methods: This sub-study of the HBFlows trial included 298 patients (106 smokers and 192 non-smokers) with suspected coronary artery disease (CAD) who underwent coronary CT angiography (CCTA), invasive fractional flow reserve (FFR), and FFRct assessment. Smoking status was determined from lifestyle records. FFRct was calculated using CFD simulations with myocardial volume [measured via three-dimensional (3D) left ventricular segmentation] as a boundary condition, solved via the Newton-Krylov-Schwarz (NKS) method. Diagnostic performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve (AUC), with invasive FFR as the reference standard. Results: Smokers had significantly larger myocardial volumes than non-smokers (193.4 vs. 157.9 mL, P<0.01), but no significant differences in FFR (0.85 vs. 0.85, P=0.496) or FFRct (0.86 vs. 0.86, P=0.466) values were observed between groups. FFRct showed strong correlation with invasive FFR in both smokers (r=0.730, P<0.0001) and non-smokers (r=0.726, P<0.0001). Diagnostic performance was comparable: sensitivity (87.50% vs. 90.00%), specificity (89.19% vs. 88.64%), accuracy (88.68% vs. 89.06%), and AUC (0.919 vs. 0.928) for smokers and non-smokers, respectively. Conclusions: Smokers had significantly larger myocardial volumes than non-smokers, but no significant differences in FFRct values or diagnostic performance were observed between groups. FFRct demonstrates consistent diagnostic accuracy in both smokers and non-smokers, supporting its utility for CAD assessment in smoking populations.
Impact of field number and monitor units per segment on magnetic resonance-guided hypofractionated stereotactic radiotherapy for brain metastases: plan quality, deliverability, and robustness trade-offs
Background: Magnetic resonance-guided linear accelerators (MR-Linacs) have recently been introduced for radiotherapy of brain metastases (BMs), including hypofractionated stereotactic radiotherapy (HSRT). However, optimal strategies for planning HSRT within MR-guided adaptive workflows remain to be established. This study aimed to evaluate the influence of field number and minimum monitor unit per segment (MU/segment) on plan quality, and robustness of dose calculations in MR-guided HSRT for patients with solitary medium-sized BMs. Methods: This retrospective study included 20 patients who underwent HSRT for solitary medium-sized BMs, receiving a prescription dose of 30 Gy in three fractions. Four intensity-modulated radiotherapy (IMRT) plans were systematically generated for each patient by varying the number of fields (9 vs. 15) and minimum MU/segment (15 vs. 5): 9FL-IMRT (9 fields, 15 MU/segment), 9FS-IMRT (9 fields, 5 MU/segment), 15FL-IMRT (15 fields, 15 MU/segment), and 15FS-IMRT (15 fields, 5 MU/segment). Plan quality, treatment efficiency, and delivery accuracy were assessed based on dose distributions optimized using structure-based bulk electron density assignment, with patient-specific quality assurance (QA) evaluated via global gamma passing rate (GPR) using 3%/2 mm, 2%/2 mm, and 2%/1 mm criteria. To assess robustness against density-related spatial uncertainties, each plan was reoptimized after simulating skull misalignment with random translational offsets within ±2 mm. These reoptimized plans were recalculated on original computed tomography (CT) datasets using voxel-based electron density as the reference standard. Robustness was quantified by comparing dose distributions between bulk density-based and voxel-based recalculations through GPR analysis using 3%/2 mm, 2%/2 mm, 2%/1 mm, and 1%/1 mm criteria. Results: All four IMRT configurations provided clinically acceptable plans with similar target coverage and no significant differences in conformity, gradient, or homogeneity indices (all P>0.05). Although some dose parameters for normal brain tissue reached statistical significance (overall P<0.001), absolute differences were minor (≤1 cm3) and clinically irrelevant. The 9FL-IMRT configuration yielded the lowest MU (2,037±174) and shortest beam-on time (BOT; 6.69±0.92 min), while the 15FL-IMRT configuration required only a minimal increase in BOT (7.59±1.18 min) and maintained fewer segments (33±7). Delivery accuracy was consistently high across all techniques, with mean GPR values ≥95% at 3%/2 mm, showing no significant inter-technique differences (P=0.67). However, under conditions of simulated skull displacement, notable robustness differences emerged under stricter criteria, with 15FL-IMRT consistently showing the highest GPR at 2%/1 mm (96.02%±2.93%, overall P=0.004) and 1%/1 mm (91.87%±3.55%, overall P<0.001), significantly outperforming 9FL-IMRT and 9FS-IMRT, indicating enhanced tolerance to density-related spatial uncertainties. Conclusions: The 15FL-IMRT configuration provided an optimal balance among plan quality, deliverability, and robustness of dose calculation, supporting its adoption as the preferred planning approach for MR-guided HSRT in patients with solitary medium-sized BMs.
Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features
Background: BRAF V600E mutation, the most prevalent driver alteration in papillary thyroid carcinoma, is associated with aggressive clinicopathological features, including macroscopic extrathyroidal extension, lymph node metastasis, and high-risk histological features. BRAF V600E mutation is determined by tissue biopsy/surgery and gene sequencing, which are invasive and costly. The study aimed to develop an interpretable prediction model based on clinical and ultrasound characteristics via radiomics and deep learning (DL) methods to noninvasively predict the BRAF V600E mutation in patients with thyroid cancer. Methods: A total of 6,703 ultrasound images from 1,257 lesions in 1,202 patients with thyroid cancer were retrospectively collected. Since multiple ultrasound images were available for each lesion, the lesion-level prediction was derived as the average of the image-level outputs. Univariate and multivariate logistic regression were adopted to construct the clinical model. Six machine learning models were compared to identify the optimal one. A ResNet50-32x4d model was fine-tuned to build the DL model. The extreme gradient boosting (XGBoost) algorithm was employed to integrate the optimal radiomics score (radscore), DL scores, and clinical factors for combined model construction. The Shapley additive explanations (SHAP) algorithm and gradient-weighted class activation mapping technique were applied for interpretability. Results: Multivariate analysis identified the significant predictive variables to be sex [odds ratio (OR) =0.61; 95% confidence interval (CI): 0.54–0.69; P<0.001], age (OR =1.01; 95% CI: 1.00–1.01; P<0.001), tumor size (OR =0.54; 95% CI: 0.50–0.58; P<0.001), and multifocality (OR =0.66; 95% CI: 0.57–0.75; P<0.001). Among the six machine learning models, the XGBoost model demonstrated the best performance, with an area under the curve (AUC) of 0.809 and 0.745 in the training and test sets at the lesion level, respectively. The DL model outperformed the XGBoost model, achieving an AUC of 0.807 in the test set at the lesion level. The combined model exhibited comparable performance to that of the DL model, with AUCs of 0.845 and 0.814 in training and test sets at the lesion level, respectively. SHAP analysis revealed that DL scores and radscores were key contributors in predicting mutation status. Conclusions: The combined model integrating clinical and ultrasound data can effectively predict BRAF V600E mutation status in patients with thyroid cancer.
Comparison between 99mTcO4− SPECT/CT and diagnostic 131I SPECT/CT for the detection of postoperative thyroid remnant in patients with differentiated thyroid cancer
Background: Accurate imaging evaluation of remnant thyroid tissue (RTT) is particularly important for precise radioiodine remnant ablation (RRA). However, the optimal imaging method to guide its management remains a subject of debate. This study compared the diagnostic value of 99mTcO4− single photon emission computed tomography/computed tomography (SPECT/CT; Tc-99m scan) and diagnostic 74 MBq 131I whole body scan with SPECT/CT (Dx scan) for detecting RTT in differentiated thyroid cancer patients after total thyroidectomy, to guide subsequent RRA strategy. Methods: A total of 505 patients undergoing radioactive iodine (RAI) therapy were enrolled and had no cervical lymph node metastases or distant metastasis in postablation 131I SPECT/CT (Rx scan). The thyroid bed area was divided into 8 regions on SPECT/CT. RTTs on Tc-99m scan and Dx scan were counted and analyzed. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Tc-99m scan and Dx scan for detecting RTT were compared. Associations of serum stimulated thyroglobulin (sTg), thyroglobulin antibody (TgAb), and sTg/thyroid-stimulating hormone (TSH) with findings across all scan modalities were analyzed. The ablation success rates in different groups were assessed. Results: Per-patient analysis showed that Dx scan had significantly higher sensitivity (95.3%) and NPV (34.3%) than Tc-99m scan (66.1% and 6.7%, respectively). Both modalities achieved 100% specificity and PPV. Rx SPECT/CT detected 1,057 radioiodine-uptake foci in 493 patients, with the majority of RTT situated in the superior anterior region (SAR) and posterolateral regions. In the per-site analysis, the overall sensitivity, specificity, PPV, and NPV of Tc-99m scan for detecting RTT were 47.3%, 100%, 100%, and 84.3%, respectively, compared with 86.7%, 100%, 100%, and 95.5% for Dx scan. sTg levels were significantly higher in both Tc-99m scan-positive and Dx scan-positive patients than those in negative patients (2.12 vs. 0.33 ng/mL, P<0.001; 1.43 vs. 0.08 ng/mL, P<0.001). SAR-positive patients had significantly higher median sTg than SAR-negative patients (1.96 vs. 0.61 ng/mL, P<0.001). The ablation success rate was significantly higher in the 100 mCi group than it was in the 50 mCi group (97.7% vs. 90.0%, P=0.021). For SAR-positive patients, 100 mCi was superior to 50 mCi (96.8% vs. 88.2%, P=0.044), confirmed after propensity score matching [odds ratio (OR) =9.098, P=0.037]. No dose difference was seen in SAR-negative patients. Conclusions: The Dx scan is superior to the Tc-99m scan for RTT assessment. To optimize RAI management, a Dx scan using 74 MBq 131I may be recommended before the first individualized RAI therapy.
Prognostic value of lesion dissemination on 18F-FDG PET/CT in patients with nasal-type extranodal natural killer/T-cell lymphoma
Background: Recent research suggests that the maximum distance between the two farthest tumor lesions (Dmax)—an indicator reflecting the degree of tumor dispersion—has prognostic value in certain lymphoma subtypes. This study aimed to explore the prognostic value of positron emission tomography/computed tomography (PET/CT) imaging features, particularly Dmax, in patients with advanced-stage nasal-type extranodal natural killer/T-cell lymphoma (ENKTL). Methods: A retrospective analysis was conducted. Using LIFEx software (version 7.3.0), imaging parameters, including the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and whole-body tumor Dmax, were measured. The optimal cut-off values for each parameter in predicting prognosis were determined using X-tile software. Cox regression analysis and the log-rank test were used to evaluate the prognostic value of each imaging parameter and clinical characteristic [gender, B symptoms, and Prognostic Index of Natural Killer cell lymphoma (PINK) score], and to identify independent prognostic factors. Results: A total of 104 patients with stage III/IV nasal-type ENKTL were enrolled in the study, which had a median follow-up duration of 98.6 months (interquartile range, 42.8–119.8). During follow-up, 70 patients experienced disease progression, and 56 died. Multivariate Cox regression analysis revealed that TLG was an independent risk factor for both progression-free survival (PFS) [hazard ratio (HR) =2.87, P=0.009] and overall survival (OS) (HR =2.66, P=0.044). After excluding TLG and MTV, which are difficult to obtain in routine clinical practice, Dmax remained an independent risk factor for PFS (HR =1.78, P=0.040) and OS (HR =1.83, P=0.044). Conclusions: When all the 18F-fluorodeoxyglucose (FDG) PET/CT parameters were analyzed together, Dmax was not an independent prognostic factor in patients with advanced-stage nasal-type ENKTL. However, after excluding MTV and TLG, which are difficult to obtain in routine clinical practice, Dmax independently predicted patient prognosis.
Associations between skeletal muscle parameters and metabolic markers in metabolic syndrome: an exploratory cross-sectional study using photon-counting computed tomography
Background: Metabolic syndrome (MetS) is associated with altered body composition, but the relationship between skeletal muscle parameters and metabolic markers remains incompletely understood. This exploratory cross-sectional study aimed to investigate associations between skeletal muscle quantity and quality, measured by photon-counting computed tomography (PCCT), and circulating metabolic markers in individuals with and without MetS. Methods: A total of 117 participants [38 with MetS and 79 healthy controls (HCs)] underwent abdominal non-contrast PCCT. Skeletal muscle area (SMA), skeletal muscle density (SMD), and skeletal muscle index (SMI) were measured at the third lumbar vertebral level. Fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and lipid profiles were collected. Spearman correlation, multivariable regression, and interaction analyses were performed. False discovery rate (FDR) adjustment was applied to exploratory subgroup correlations. Results: The MetS group had significantly higher SMI (median, 42.73 vs. 39.16 cm2/m2; P<0.001) and a trend toward lower SMD [30.54 vs. 35.22 Hounsfield units (HU); P=0.074]. SMA was not associated with FPG in controls (ρ≈0.001) but showed a positive correlation in the MetS group (ρ=0.411, FDR-adjusted P=0.035). Fisher’s r-to-z test suggested a between-group difference (P=0.033). However, this association was no longer statistically significant after adjustment for age, sex, and body mass index (BMI) (P=0.451). A positive correlation between SMI and triglycerides was observed only in controls (ρ=0.289, FDR-adjusted P=0.024). Conclusions: MetS was associated with higher SMI and a trend toward lower SMD. Exploratory differences in association patterns between skeletal muscle quantity and glycemic markers were observed but were not independent of BMI. These hypothesis-generating findings require confirmation in larger, well-powered prospective studies.
Preliminary exploration of quantitative analysis parameters of renal cortical blood perfusion by contrast-enhanced ultrasound in hypertensive patients
Background: Evaluation of renal blood perfusion is important for patients with hypertension. Contrast-enhanced ultrasound (CEUS) is a safe and non-invasive technique that allows semi-quantitative assessment of renal cortical perfusion. Using CEUS quantitative analysis, we aimed to employ the characteristics of renal cortical blood perfusion (RCBP) parameters in hypertension patients, providing objective evidence for quantifying the hemodynamic features of renal microcirculation. Methods: It was a single-center retrospective study that included data from 83 hypertensive patients without renal artery stenosis who underwent renal CEUS at the Department of Ultrasound Medicine, Beijing Hospital between October 2020 and June 2023. The study cohort comprised 45 males and 38 females. Data collected included patient height, weight, systolic and diastolic blood pressure, estimated glomerular filtration rate (eGFR), RCBP parameters, and other historical records. Through stratified analysis, the characteristics of RCBP parameters were analyzed for the left/right sides, different sexes, and elderly versus non-elderly individuals. Results: There were no statistically significant differences in all cortical blood flow perfusion parameters [peak intensity (PI); rise time (RT); mean transit time (MTT); area under the curve (AUC); wash-in area under the curve (iAUC); wash-out area under the curve (oAUC); time to peak (TTP); PI/MTT] between the right and left kidneys of the patients (P>0.05). The iAUC and TTP were greater in males than in females, with marginal statistical differences (both P<0.05). The MTT and TTP were greater in the elderly group than in the non-elderly group, whereas PI/MTT was greater in the non-elderly group, with marginal statistical differences (both P<0.05). The intraclass correlation coefficients (ICCs) and Bland-Altman plots demonstrated good inter-observer agreement for all cortical blood flow perfusion parameters, especially for time-related parameters. Conclusions: This study preliminarily investigated the characteristics of RCBP parameters in hypertensive patients, further validating good reproducibility of the quantitative analysis technique of contrast-enhanced ultrasound.
Deep learning-based high-resolution united compressed sensing for gadoxetic acid-enhanced liver magnetic resonance imaging in the detection of colorectal liver metastases
Background: The hepatobiliary phase (HBP) of gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) is important for detecting colorectal liver metastasis (CRLM), but image quality may be limited. This study evaluated whether deep learning-based reconstruction united compressed sensing (DR-uCS) and deep learning-based reconstruction high-resolution united compressed sensing (DR-HR-uCS) improve image quality and lesion detection in CRLM. Methods: This retrospective study included 86 patients with 116 CRLM lesions (71 lesions ≥1 cm and 45 lesions <1 cm) who underwent 3.0-T gadoxetic acid-enhanced liver MRI. A standard-resolution HBP acquisition was reconstructed into conventional united compressed sensing (uCS) and DR-uCS from the same raw k-space data, while a separate high-resolution acquisition generated DR-HR-uCS images. Two radiologists independently assessed subjective image quality, artifact severity, liver edge/vessel clarity, and lesion conspicuity. Quantitative metrics [liver signal-to-noise ratio (SNR), lesion SNR, and contrast-to-noise ratio (CNR)] were measured by standardized region-of-interest analysis. Diagnostic performance for lesions ≥1 and <1 cm was evaluated using pathology or multidisciplinary consensus. Diagnostic time was recorded across three reader experience levels. Results: Both DR-uCS and DR-HR-uCS significantly improved overall image quality compared with uCS (median score: 5 vs. 4, both P<0.001) and significantly reduced image artifacts (both P<0.001). DR-uCS achieved the highest liver SNR and CNR, while the lesion SNR was comparable across methods (P=0.03 and P=0.001, respectively). For lesions ≥1 cm, conspicuity and diagnostic performance were similar (all P>0.05). For lesions <1 cm, DR-HR-uCS demonstrated higher conspicuity and sensitivity (87.1%) than uCS (72.4%) and DR-uCS (78.6%) (adjusted P<0.05), with comparable specificity. Diagnostic time for sub-centimeter lesions was significantly shorter with DR-HR-uCS (P<0.001), and differences among readers were reduced. Conclusions: DR-uCS improves HBP image quality, while DR-HR-uCS further enhances the detection efficiency and conspicuity of sub-centimeter CRLMs. Its advantage likely reflects the combined effects of high-resolution acquisition and deep learning-based reconstruction.
Quantifying the impact of slice thickness on cardiovascular risk stratification in lung cancer screening: a multi-center “RESCUE” study
Background: Patients undergoing routine non-gated chest computed tomography (CT) for health checkups or atypical chest discomfort often present with a coronary artery calcium (CAC) score of zero on standard 5.0 mm reconstructions. We hypothesized that these thick slices obscure mild calcification due to partial volume effects (PVEs), which could be recovered by retrospective analysis of native thin-slice images. This study aimed to quantify the rate of unrecognized coronary calcification on standard thick-slice CT by comparing paired thin- and thick-slice reconstructions. Methods: We analyzed data of 2,914 patients across four datasets: Stanford Coronary Calcium and chest CT’s (COCA) (n=651) for reference validation; an internal cohort evaluated by invasive angiography for early-onset coronary artery disease (CAD) (n=766) and National Lung Screening Trial (NLST) (n=852) with paired thin (1.0–2.0 mm) vs. standard (5.0 mm) scans; and TotalSegmentator (n=645) for robustness. A validated deep learning algorithm quantified CAC. The primary outcome was the “RESCUE” rate: patients reclassified from CAC =0 on 5.0 mm to CAC >0 on thinner slices. Results: In the internal cohort, 19.0% were reclassified from CAC =0 on 5.0 mm scans to CAC >0 on 1.0 mm scans. Similarly, 10.2% of NLST participants were reclassified using 2.0 mm scans. Most reclassified patients (91–99%) fell into the mild risk category (Agatston 1–99). Crucially, 31% of symptomatic patients with CAC =0 on standard scans had obstructive CAD (>50% stenosis); many were “rescued” to a positive CAC status by thin-slice analysis. Risk categorization showed strong agreement (weighted kappa 0.705–0.816). Artificial intelligence (AI) correlated strongly with expert annotations (r=0.956). Conclusions: Standard 5 mm reconstructions cause significant false negative CAC assessments. Analyzing routinely available thin slice reconstructions improves sensitivity for early subclinical atherosclerosis without additional radiation, supporting their use in opportunistic screening.
Exploring the diagnostic performance of low-dose CT for colorectal cancer assessment: feasibility of artificial intelligence iterative reconstruction
Background: Contrast-enhanced long-range abdominal computed tomography (CT) plays a crucial role in the diagnosis and staging of colorectal cancer (CRC) yet may involve excessive radiation exposure, especially for patients requiring multiple examinations. This raises the need to reduce radiation dose while maintaining image quality. Given the potential of a novel artificial intelligence iterative reconstruction (AIIR) algorithm to improve image quality in low-dose (LD) CT, this study aimed to investigate the feasibility of applying AIIR to LD abdominal CT for CRC diagnosis. Methods: In this prospective study, 203 patients with pathology-confirmed CRC underwent abdominal CT, including a routine-dose (RD) scan (120 kVp, 200 mAs) followed by a LD scan (120 kVp, 20 mAs) at the portal venous phase (PVP). RD images were reconstructed with hybrid iterative reconstruction (HIR, RD-HIR) and AIIR (RD-AIIR), whereas LD images were reconstructed with AIIR (LD-AIIR). Diagnostic performance for assessing visceral peritoneal invasion (VPI) and regional lymph node metastasis (RLNM) was characterized using receiver operating characteristic (ROC) analysis. Qualitative image quality was rated using a five-point scale, and tumor contrast-to-noise ratio (CNR) was measured. Results: The mean effective dose (ED) of LD scan was 90.3% lower than that of RD scan (1.5±0.2 vs. 14.9±2.4 mSv). RD-AIIR achieved significantly higher area under the curve (AUC) and accuracy for diagnosing VPI (0.89 and 91.13%, respectively) and RLNM (0.72 and 71.92%, respectively) compared to the other reconstructions (all P<0.05). LD-AIIR showed comparable AUC and accuracy to RD-HIR for VPI (0.81 vs. 0.80 and 82.96% vs. 78.33%, respectively; both P>0.05), but demonstrated inferior performance for RLNM (0.65 vs. 0.68 and 66.51% vs. 68.47%, respectively; P<0.05). AIIR significantly improved tumor CNR (P<0.05) and the qualitative image quality was comparable between LD-AIIR and RD-HIR (P>0.05). Conclusions: AIIR offers superior image quality compared to HIR. AIIR allows up to 90.3% dose reduction for reliable VPI assessment in CRC while maintaining comparable image quality to that of RD-HIR on abdominal CT.
Ultrasound features for pediatric testicular lesions: 20 years of ultrasound practice interpreted through the 2022 WHO Classification
Background: Testicular lesions are rare in pediatric populations. The World Health Organization standardized the classification of testicular tumors for urological and male genital tumors in 2022. Ultrasound is highly sensitive in detecting testicular lesions in children; however, the ultrasound features that distinguish malignant from benign lesions remain unclear. We aimed to identify ultrasound features that differentiate benign and malignant testicular lesions in pediatric patients. In addition, we evaluated the role of ultrasound in pediatric testicular tumors based on the new guidelines. Methods: We retrospectively reviewed the clinical data and ultrasound images of 84 testicular lesions in 79 pediatric patients aged 0–18 years (≤216 months), who underwent testicular sonography between April 2004 and October 2023 at West China Hospital. Prepuberty was defined as 0–12 years of age. Univariate and multivariate logistic regression analyses were conducted to compare ultrasound features between benign and malignant tumors across all 84 lesions, 66 primary tumors, and 65 prepubertal lesions. Continuous variables were compared between groups using the Mann-Whitney U test, and Fisher’s exact test was used for categorical variables. Results: Univariate analysis showed that among the 84 total lesions, the malignant group exhibited a more diffuse lesion range compared with the benign group (P<0.001). Among 84 total lesions or 66 primary tumor lesions, the malignant group exhibited more hypoechoic and isoechoic echoes, unclear margins, absence of calcification, and increased vascularity (P<0.001). Furthermore, in 66 primary tumor lesions, the malignant group also exhibited non-complex echo and the small hypoechoic nodule structures (P<0.001). There were no significant differences in microlithiasis among all groups (all P>0.05). In the multivariate analysis, among the total lesions group or primary tumor group, hypoechoic and isoechoic patterns, as well as absence of calcification, remained important features of malignant manifestations (all P<0.001). Margin circumscription was statistically significant only in the total lesions group (P=0.001), whereas lesion size was statistically significant only in the primary tumor group (P=0.006). In 65 prepubertal lesions, malignant lesions presented more hypoechoic and isoechoic echoes (P<0.01), and clear margins (P=0.034) were associated with a reduced risk of malignancy. Among 84 total lesions, the predictive power of echo pattern was the highest [area under the curve (AUC) =0.815], followed by calcification (AUC =0.795) and lesion size (AUC =0.712). The predictive power of echo patterns and calcification remained good in 66 primary tumors (AUC =0.818 and 0.838, respectively) and 65 prepubertal lesions (AUC =0.842 and 0.820, respectively). Conclusions: The echo pattern shows the highest predictive value in total lesions and prepubertal lesions, while calcification is more important for primary tumors. Different imaging features may be required to optimize the prediction of benign and malignant testicular lesions.
Intra- and interobserver variability in renal multiparametric magnetic resonance imaging: T1 mapping, diffusion-weighted imaging, arterial spin labeling, blood oxygen level-dependent and renal artery flow measurements utilizing manual segmentation
Background: Multiparametric MRI (mpMRI) offers non-invasive characterization of renal microstructure and function, but its clinical translation is hindered by the lack of standardized post-processing. Manual segmentation is widely used but inherently observer-dependent. This study aimed to quantify the intra- and interobserver variability in manual segmentation of a comprehensive renal mpMRI protocol in a diverse cohort of patients with biopsy-confirmed chronic kidney disease (CKD). Methods: Seventy-two patients scheduled for diagnostic kidney biopsy were prospectively enrolled. Participants underwent mpMRI on a 1.5-T system prior to biopsy, including kidney volume assessment, T1 mapping, T2* [blood oxygen level-dependent (BOLD)], diffusion-weighted imaging [apparent diffusion coefficient (ADC)], arterial spin labeling (ASL), phase-contrast renal artery flow, and T1-rho mapping. Manual segmentation of the renal cortex and medulla was performed on three representative coronal slices. Two trained independent observers (one nephrologist, one radiologist) analyzed the cohort to assess interobserver variability. One observer repeated the full analysis after a six-month washout period to assess intraobserver variability. Agreement was evaluated using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Results: Complete datasets were available for 64 patients (mean estimated glomerular filtration rate 44 mL/min/1.73 m2; range, 3–132 mL/min/1.73 m2). Intra- and interobserver ICCs indicated good-to-excellent agreement for T1, T2*, and ADC in both cortical and medullary compartments (ICC range, 0.84–0.99). ASL showed excellent intraobserver reliability (ICC =0.94), but interobserver agreement was only good (ICC =0.78). Renal artery flow measurements also demonstrated good intra- and interobserver agreement (ICC =0.82 for both). T1-rho showed moderate-to-good intraobserver agreement (ICC range, 0.73–0.81) but was limited by image quality. Conclusions: Manual segmentation of renal cortex and medulla yields highly reproducible measurements for T1, T2*, ADC, renal artery flow and ASL in a mixed CKD population, supporting their validity for clinical research. However, the proportional bias in the Bland-Altmann plots for ASL and renal artery flow highlights the necessity for rigorous standardization of vessel selection and potential automation to ensure reliability across different readers.
A predictive model integrating three-dimensional displacement for osteonecrosis of femoral head following femoral neck fractures: a retrospective cohort study
Background: Fracture displacement is a recognized risk factor for osteonecrosis of the femoral head (ONFH) after femoral neck fractures (FNFs), yet the relationship between ONFH and three-dimensional (3D) displacement has not been clearly quantified. This study aims to develop a computed tomography-based method to measure 3D displacement at the FNF site and to build a predictive model that integrates these parameters. Methods: We retrospectively analyzed 302 FNF patients treated with closed reduction and internal fixation within 3 years. Univariate analysis and multivariable logistic regression were used to assess the association between ONFH and multiple variables, including 3D displacement. The prediction model for ONFH was evaluated using receiver operating characteristic curve, a nomogram, calibration curve, and decision curve analysis (DCA). Results: During the 3-year follow-up period, ONFH was identified in 68 cases (incidence rate 22.5%). Univariate analysis showed that Garden classification, age, time to surgery, osteoporosis, comminution, fixation removal, and displacement parameters (vertical, separation, and comprehensive) were significantly associated with ONFH (P<0.05). Multivariable logistic analysis confirmed that vertical displacement, separation displacement, age, and time to surgery were independent predictors [P<0.05; odds ratio (95% confidence interval): 1.20 (1.01–1.42), 1.19 (1.08–1.30), 0.92 (0.88–0.95), 1.87 (1.41–2.49), respectively]. The model showed an area under the curve >0.91, sensitivity >81%, specificity >89%, and a Youden index of 0.769. Calibration curves showed good agreement between predicted and actual probabilities (Hosmer-Lemeshow test, P>0.05), while DCA indicated clinical usefulness within a risk threshold of 0.10–0.90. In the nomogram, separation displacement contributed the highest score. Conclusions: ONFH is related to factors such as Garden classification, age, time to surgery, osteoporosis, comminuted status, fixation removal, and vertical, separation, and comprehensive displacement. Among these, age, time to surgery, and vertical and separation displacement are independent risk factors. The predictive model that incorporates 3D displacement can quantify the risk of postoperative ONFH and shows good predictive ability in FNF patients. However, further external validation is still required.
Normal reference values and determinants of right heart strain derived from speckle tracking echocardiography: a systematic review and meta-analysis
Background: Normal ranges for speckle tracking echocardiography (STE)-derived right atrial (RA) and right ventricular (RV) strain indices have not yet been fully standardized. This study aimed to quantitatively summarize available evidence regarding normal RV and RA deformation parameters in healthy adults and to investigate factors contributing to measurement variability. Methods: PubMed, Embase, and Web of Science were systematically searched for studies published until July 1, 2025 that reported STE-derived right-heart strain and strain-rate measurements in healthy adults. A random-effects model was applied to generate pooled estimates for RV global longitudinal strain (RV-GLS), RV peak systolic and diastolic longitudinal strain rates (RV-PSSRL and RV-PDSRL), RA reservoir, conduit, and booster strain (RA-εs, RA-εe, and RA-εa), as well as RA peak positive, early negative, and late negative strain rates (RA-SRs, RA-SRe, and RA-SRa). Subgroup and meta-regression analyses assessed the effects of race, sex, age, feature tracking (FT) software, STE vendor and STE imaging mode [two-dimensional (2D)-and three-dimensional (3D)-STE]. Heterogeneity was assessed using the I2 statistic, and publication bias was examined by Egger regression analysis. Results: The pooled means for RV-GLS, RV-PSSRL, and RV-PDSRL were −25.62%, −1.50 s−1, and 1.69 s−1, respectively. RA-εs, RA-εe, and RA-εa were 42.95%, 20.86%, and 19.30%, respectively; while RA-SRs, RA-SRe, and RA-SRa were 2.03 s−1, −2.14 s−1, and −1.80 s−1, respectively. Variability in right-heart deformation measurements was significantly associated with race, sex, age, FT software, and STE imaging mode (all P<0.05), whereas the influence of STE vendor was generally limited (most P>0.05). Conclusions: This study established pooled reference values for STE-derived RV and RA strain and strain-rate parameters in healthy adult populations. STE demonstrates strong potential as a practical and reliable technique for the quantitative assessment of right heart function in routine clinical practice.
Benchmarking YOLOs in breast ultrasound lesion segmentation
Background: Breast ultrasound (BUS) is widely used for breast cancer (BC) screening and diagnosis, yet accurate breast lesion segmentation remains challenging. Although You Only Look Once (YOLO) and its variants have shown strong performance in object segmentation, their effectiveness on BUS lesion segmentation has not been systematically explored. This study aims to benchmark twelve YOLO variants from four families (YOLOv5, YOLOv8, YOLOv9, and YOLO11) for BUS lesion segmentation under same-database and cross-database settings. Methods: Twelve YOLO variants spanning nano to extra-large scales were fine-tuned and evaluated on two public BUS datasets, the Breast Ultrasound Images (BUSI) dataset (n=647) and the breast ultrasound lesion segmentation dataset from the University of Castilla-La Mancha (BUS-UCLM) dataset (n=264). Each dataset was split into training (80%), validation (10%), and testing (10%) subsets with stratified random partitioning, and experiments were repeated across eight random seeds. Six evaluation metrics, including Dice coefficient, intersection over union (IoU), precision, recall, F1 score (F1S), and mean average precision at IoU threshold 0.5 (mAP@0.5), were used. In addition, U-Net and DeepLabV3+ were compared under the same protocol. Results: Under same-database evaluation, all variants achieved strong performance, with mean Dice scores ≥0.81 on BUSI and ≥0.87 on UCLM. On BUSI, yolov5s achieved the highest mean Dice (0.93±0.012) and IoU (0.88±0.014). On UCLM, yolov8m attained the highest mean Dice (0.98±0.006) and IoU (0.96±0.007). In cross-database evaluation, however, performance degraded substantially, with Dice scores decreasing by approximately 0.20 or more. For BUSI→UCLM, yolov5s achieved the highest mean Dice (0.71±0.032); and for UCLM→BUSI, yolov9c achieved the highest mean Dice (0.60±0.038). Among all variants, yolo11n demonstrated competitive performance across both same- and cross-database evaluations (BUSI Dice 0.91±0.014, and BUSI→UCLM Dice 0.69±0.034; UCLM Dice 0.96±0.009, and UCLM→BUSI 0.59±0.039) while maintaining low computational cost (training time <290 s; inference latency ~64 ms). Notably, yolo11n substantially outperformed U-Net and DeepLabV3+ under both same- and cross-database settings. Conclusions: Fine-tuned YOLO variants achieve strong same-database performance for BUS lesion segmentation, with yolo11n offering the most favorable balance among segmentation accuracy, cross-database competitiveness, and computational efficiency. However, the substantial performance degradation in cross-database settings highlights the critical need for improving domain generalization performance in future work.
A nomogram for predicting high-risk endometrial cancer based on the transvaginal ultrasonography and contrast-enhanced ultrasonography
Background: The high-risk endometrial carcinoma (EC) constitutes a significant threat to the survival of women. However, existing diagnostic modalities exhibit inherent limitations. Contrast-enhanced ultrasonography (CEUS) and transvaginal ultrasonography (TVUS) have demonstrated considerable potential for oncological assessment owing to their diagnostic accuracy and operational simplicity. Therefore, this study aimed to construct a comprehensive diagnostic model for high-risk EC by synergistically integrating TVUS and CEUS parameters. Methods: Patients pathologically diagnosed with EC were enrolled and categorized into low-risk and high-risk groups based on pathological risk factors. Demographic information, TVUS, and CEUS examination results were collected. Intergroup comparisons were executed via Chi-square tests, independent samples t-tests, and Mann-Whitney U tests. Univariate logistic regression was employed to identify parameters associated with high-risk EC. Predictors were refined by first excluding collinear variables [variance inflation factors (VIF) ≥5], followed by least absolute shrinkage and selection operator (LASSO) regression-based feature selection. A nomogram model was developed, and its performance assessed using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and internal validation. Results: A total of 128 patients with EC were enrolled in this study, among whom 62 were classified as high-risk cases. Significant differences were observed between high-risk (n=62) and low-risk (n=66) EC patients across multiple clinical characteristics and imaging parameters. Univariate logistic regression analysis revealed 18 variables significantly associated with high-risk EC. Multicollinearity assessment (VIF threshold: 5.0) identified 6 variables with severe collinearity. LASSO regression analysis identified CEUS-derived area under the curve (AUC), enhancement degree (ED), perfusion mode (PM), endometrial-myometrial border (EMB), tumor anterior-posterior diameter (TAP) and menopausal status as independent predictive factors, which were incorporated into the nomogram model. This model achieved an AUC of 0.9213, with substantial clinical net benefit and robust stability. Conclusions: This study constructed and validated an efficient nomogram for predicting high-risk EC, by integrating TVUS and CEUS parameters. These findings furnished guidance for the precise identification and timely management of high-risk patients.
PAC-P2T: pyramid atrous convolution with pyramid pooling Transformer for polyp segmentation
Background: Colonoscopy is the gold standard method for investigating the gastrointestinal tract. Localizing the polyps in colonoscopy images is crucial for colonoscopy screening, and it also plays a significant role in the following treatment, e.g., polyp resection. Although several Convolutional Neural Networks (CNNs) and Transformer-based methods have achieved substantial progress in polyp extraction, they still face difficulties in effectively addressing challenges like morphological variability and blurred edges in polyp segmentation, which limits their clinical application. This study aims to propose a more effective polyp segmentation method for clinical use by effectively combining the advantages of pyramid pooling and atrous convolution. Methods: We propose a pyramid atrous convolution (PAC) with pyramid pooling Transformer (P2T) for polyp segmentation, namely PAC-P2T. Considering the effectiveness of P2T in contextual feature extraction, we first adopt P2T as the encoder to extract powerful features. Leveraging the ability of atrous convolution to extract features at the same scale but with varying receptive fields, we introduce a multi-layer PAC feature extraction module (MPAF) combined with a channel attention mechanism, thereby enhancing information flow in the decoder branch. In addition, to progressively expand the receptive fields while preserving image details, we integrate the single-level atrous convolution feature fusion module (SLAF) into each encoder side branch, promoting hierarchical feature propagation to subsequent lower-level branches. Results: Experimental results conducted on five public colorectal polyp segmentation datasets demonstrate that PAC-P2T outperforms several state-of-the-art polyp extraction networks, which verify the effectiveness of the mechanism of simultaneously combining the pyramid pooling and PAC for polyp segmentation. In particular, compared with PraNet, PAC-P2T improved mean Dice coefficient/mean intersection over union (mDice/mIoU) by 18.7%/17.7% on ETIS and by 10%/9.6% on CVC-ColonDB. Conclusions: By incorporating atrous convolution as a key feature extraction unit and integrating PAC into the Pyramid Pooling structure, the proposed PAC-P2T enhances the robustness of polyp region extraction, providing strong support for computer-aided polyp segmentation in clinical applications.
Associations between altered morphometric inverse divergence networks, choroid plexus volume, and perivascular space network in patients with Parkinson disease: a cross-sectional study
Background: Recent evidence suggests that Parkinson disease (PD) involves structural alterations in gray-matter volume, cortical surface area, and cortical thickness. Clinical symptoms of PD have also been associated with the choroid plexus (CP) volume and perivascular space (PVS) network parameters. However, the relationship between morphometric inverse divergence (MIND) network alterations and CP volume or PVS network parameters in PD remains unclear. This study aimed to clarify these associations via multimodal imaging analyses. Methods: This retrospective study included healthy controls (HCs; n=82) and patients with PD (n=99) who underwent 3.0-T magnetic resonance imaging (MRI). MIND network analysis quantified interregional structural similarity through use of multiple anatomical MRI measures across brain regions. CP volume and PVS network parameters, including extracellular free water (FW) fraction, diffusion tensor imaging-analysis along the PVS (DTI-ALPS) index, and PVS volume fraction (PVSVF), were calculated. Pearson correlation coefficients were used to assess statistical associations. Results: MIND values were predominantly lower in the frontal and occipital cortical regions and higher in the cingulate and insular cortical regions in patients with PD (P<0.05). In patients with PD, regional MIND values were positively associated with CP volume (R=0.29; P<0.05) and FW fraction (R=0.28 to 0.60; P<0.05), negatively associated with the DTI-ALPS index (R=−0.36 to −0.37: P<0.05), and not significantly correlated with PVSVF (P>0.05). Conclusions: These findings enhance the understanding of macrostructural alterations in PD and provide insight into the associations among brain structure, CP volume, and the PVS network.
Quantitative T2 mapping of junctional zone alterations in endometrial fibrosis: comparison with diffusion-weighted imaging
Background: Endometrial fibrosis can disrupt the uterine junctional zone and lead to infertility. Accurate and noninvasive evaluation of junctional zone alterations is important to clinical treatment. Although conventional T2-weighted imaging provides only qualitative information, quantitative techniques such as diffusion-weighted imaging (DWI) and T2 mapping may better capture fibrosis-related microstructural changes. This study aimed to use T2 mapping to quantitatively assess junctional zone alterations in endometrial fibrosis using, compare its performance with the apparent diffusion coefficient (ADC), and evaluate its diagnostic value and reproducibility. Methods: This prospective study included 86 patients with severe endometrial fibrosis (SEF), 40 patients with mild-to-moderate endometrial fibrosis (MMEF), and 55 healthy women. Junctional zone thickness (JZT), outer myometrial thickness (OMT), T2 relaxation times of the junctional zone (T2JZ) and outer myometrium (T2OM), and ADC values of the junctional zone (ADCJZ) and outer myometrium (ADCOM) were measured and analyzed. Statistical analyses included Kruskal-Wallis one-way analysis of variance, Spearman correlation analysis, binary logistic regression, receiver operating characteristic curves, and intraclass correlation coefficients. Results: JZT, T2JZ, and ADCJZ values were significantly higher in patients with SEF (6.0 mm, 81.3 ms, 1.04×10−3 mm2/s, respectively) and patients with MMEF (5.1 mm, 71.2 ms, and 0.98×10−3 mm2/s, respectively) than in healthy women (3.8 mm, 65.2 ms, and 0.81×10−3 mm2/s, respectively) (all P values <0.05). T2JZ was substantially elevated in patients with SEF compared with those with MMEF (P<0.001). OMT, T2OM, and ADCOM were significantly higher in patients with SEF compared to healthy women (all P values <0.05). T2JZ was strongly correlated with the degree of endometrial fibrosis (ρ=0.728), whereas JZT, ADCJZ, OMT, T2OM, and ADCOM showed weak-to -moderate correlations (ρ=0.254–0.550; all P values <0.05). Junctional zone and outer myometrial magnetic resonance imaging parameters and the multivariable model accurately distinguished patients with MMEF or those with SEF from healthy women (areas under the curve >0.600). The intra- and interobserver agreement for T2 values was excellent and higher than that for ADC values. Conclusions: T2 mapping is more effective than ADC in accurately assessing the junctional zone alterations associated with endometrial fibrosis.
Carotid stiffness quantified through shear wave elastography and pulse wave velocity by ultrafast ultrasound for assessing cardiovascular risk
Background: Carotid arterial stiffness is closely linked to cardiovascular (CV) events. Traditional evaluation methods have limitations in detecting early vascular lesions and do not adequately capture regional elastic changes. Real-time shear wave elastography (RT-SWE) can quantify local carotid stiffness, while ultrafast pulse wave velocity (ufPWV) provides regional stiffness assessment. However, the value of integrating these two modalities for improving CV risk assessment has not been established. Therefore, this study aimed to improve CV risk assessment by integrating RT-SWE and ufPWV carotid artery parameters. Methods: We divided 88 patients into control [no major cardiovascular risk factors (CVRFs); n=20] and CV risk groups (hypertension, diabetes, dyslipidemia, or smoking; n=68). Carotid intima-media thickness (cIMT), pulse wave velocity (PWV) at systole beginning (PWV-BS) and end (PWV-ES), and elastic moduli (Emean, Emax, Emin, Esd) were measured via ultrasound. Between-group comparisons, correlation analysis, analysis of covariance (ANCOVA) for age adjustment, univariate and multivariable logistic regression, and receiver operating characteristic (ROC) analysis were performed. Results: Carotid elasticity parameters in RT-SWE and ufPWV (PWV-BS, PWV-ES, Emax, and Esd) were age-related (r=0.245–0.338, P<0.05). PWV-ES, Emean, Emax, and Esd were significantly higher in the CV risk group (P<0.05), with Emean, Emax, and PWV-ES remaining significantly elevated after age adjustment using ANCOVA (P<0.05). Univariate analysis showed that PWV-ES, Emean, Emax, and Esd all predicted CV risk (all P<0.05). PWV-ES yielded the highest predictive efficacy, with an area under the curve (AUC) of 0.753. The combined model (PWV-ES + Emean + Emax) achieved an AUC of 0.863, significantly outperforming single-parameter models. Conclusions: Carotid elasticity measured by RT-SWE is closely associated with carotid stiffness measured by ufPWV. A combined model integrating RT-SWE and ufPWV provides a promising noninvasive approach for CV risk stratification. Notably, following age adjustment using ANCOVA, Emax (P=0.009), Emean (P=0.047), and PWV-ES (P=0.046) retained independent associations with CV risk, underscoring their incremental prognostic value beyond the effects of vascular aging.
Clinical value of dual low-dose computed tomography angiography incorporating artificial intelligence iterative reconstruction for assessing arteriovenous fistulas/grafts among hemodialysis patients
Background: Upper extremity computed tomography angiography (CTA) plays an important role in evaluating arteriovenous fistula or arteriovenous graft (AVF/AVG) dysfunction in hemodialysis patients, but conventional protocols are associated with substantial radiation exposure and contrast load, raising concerns in this vulnerable population requiring repeated imaging. The purpose of this study was to investigate the clinical value of dual low-dose upper extremity CTA incorporating artificial intelligence iterative reconstruction (AIIR) for AVF/AVG assessment in hemodialysis patients. Methods: One hundred and two patients (mean age, 60.33±13.60 years, 52 men) with suspected AVF/AVG dysfunction were prospectively enrolled and randomly assigned to undergo either routine-dose CTA (Group A, 100 kVp, 1.0 mL/kg contrast, hybrid iterative reconstruction; n=51) or low-dose CTA (Group B, 80 kVp, 0.6 mL/kg contrast, AIIR; n=51). Qualitative and quantitative results were analyzed and compared. Among these patients, only 44 patients (176 segments) underwent digital subtraction angiography (DSA) serving as the reference standard. The diagnostic performance for detecting significant stenosis (>50%) was evaluated using receiver operating characteristic analysis, intraclass correlation coefficient (ICC), and generalized estimating equations (GEEs). Results: Demographic characteristics were comparable between two groups (all P>0.05). Group B reduced radiation and contrast doses by 52.2% and 40.5%, respectively, compared to Group A. Qualitative scores showed no significant difference between the two groups (all P>0.05). Quantitatively, Group B exhibited approximately 165% and 162% improvement in signal-to-noise ratio and contrast-to-noise ratio over Group A, respectively (all P<0.001). Per-segment and per-patient analyses demonstrated high diagnostic accuracy (98% and 95%, respectively), with excellent CTA-DSA agreement for stenosis grading (ICC >0.90 across all evaluated segments). GEE analysis showed no significant difference between groups in detecting significant stenosis (odd ratio =0.99; 95% confidence interval: 0.86–1.14; P=0.899). Conclusions: Dual low-dose CTA with AIIR significantly reduces radiation and contrast doses, while providing superior image quality and preserving diagnostic confidence for evaluating AVF/AVG stenosis.
Development and validation of intratumoral and peritumoral CT-based radiomics nomograms for predicting malignancy and invasiveness of pulmonary ground-glass nodules: a retrospective study
Background: Currently, it is still very challenging for radiologists to visually distinguish between benign and malignant ground-glass nodules (GGNs) and predict the degree of invasiveness of GGNs directly from computed tomography (CT) images. Radiomics is a newly emerging technique that can transform digital medical images into numerous quantitative features revealing pathophysiology. The present study aimed to evaluate the predictive value of intratumoral and peritumoral CT-based radiomics and the radiomics nomogram for determining the benign or malignant status of pulmonary GGNs and the invasiveness of malignant GGNs. Methods: A total of 1,372 GGNs from 1,280 patients with preoperative CT scans were included in this retrospective study. Among these, 212 GGNs were diagnosed with inflamed or infected benign lesions, 31 with atypical adenomatous hyperplasia (AAH), 257 with adenocarcinoma in situ (AIS), 559 with minimally invasive adenocarcinoma (MIA), and 313 with invasive adenocarcinoma (IAC). Malignant GGNs were classified according to clinical management strategies, with AAH, AIS, and MIA categorized as non-invasive lesions, and IAC as an invasive lesion. Two types of regions of interest (ROIs) were annotated from the CT images: gross tumor volume (GTV) and peritumoral volume (5 and 10 mm around the tumor, PTV1 and PTV2). Radiomics models, a clinical factor model, and a combined radiomics nomogram integrating the radiomics score with independent clinical predictors were constructed and compared. Results: For malignancy predictions, the GTV + PTV1 + PTV2 radiomics model and the PTV1 radiomics model achieved the highest performance in the training set and testing set, with areas under the curve (AUCs) of 0.840 and 0.796, respectively. The combined radiomics nomogram demonstrated excellent discrimination in both the training set and testing set, with AUCs of 0.870 and 0.817, respectively. Moreover, the DeLong test showed that in both the training and validation cohorts, the AUC values of the comprehensive nomogram were significantly different from those of the clinical factor model, GTV radiomics model, PTV2 radiomics model, and GTV + PTV1 + PTV2 radiomics models (P<0.05). For invasiveness predictions, the GTV + PTV1 + PTV2 radiomics model exhibited the highest performance in the training set and testing set, with AUCs of 0.956 and 0.954, respectively. Similarly, the combined radiomics nomogram showed excellent discriminatory performance in both the training set and testing set, with AUCs of 0.962 and 0.957, respectively. Furthermore, the DeLong test demonstrated that the AUC values of the comprehensive nomogram were significantly different from those of the clinical factor model and the PTV2 radiomics model in both cohorts (P<0.05). Conclusions: The radiomic signatures of both GTV and PTV1 show strong predictive capabilities for the malignancy and invasiveness of GGNs, with the PTV1 radiomics model performing comparably to, or even surpassing, that of the GTV radiomics model. The proposed nomogram demonstrated the best performance in assessing malignancy and invasiveness in patients with GGNs. It may serve as a powerful tool to assist clinicians in formulating personalized treatment strategies.
The value of non-enhanced magnetic resonance imaging based on T2-weighted imaging and apparent diffusion coefficient in the study of three-tiered HER2 status in patients with breast cancer
Background: Preoperative assessment of human epidermal growth factor receptor 2 (HER2) status relies on invasive biopsy, which is subject to sampling limitations. Meanwhile, conventional contrast-enhanced magnetic resonance imaging (MRI) and radiomics face barriers to widespread adoption due to high costs, complexity, and poor reproducibility. In contrast, non-contrast MRI based on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) offers a non-invasive, cost-effective, and safe alternative. This study aimed to evaluate whether non-contrast T2WI combined with ADC can non-invasively stratify HER2-zero, HER2-low, and HER2-overexpressing invasive breast cancer. Methods: In this retrospective single-center study, 361 women with surgically confirmed invasive breast cancer (February 2015–December 2023) were stratified into HER2-zero expressing (n=63), HER2-low expressing (n=181), and HER2-overexpressing (n=117) cohorts. All underwent preoperative 3 Tesla (T) breast MRI including fat-suppressed (FS) T2WI and DWI. Two readers assessed breast edema score (BES 1–4) and lesion-to-muscle T2 signal ratio (L/M). ADC maps were generated based on the b values of 50 and 800 s/mm2 or 0 and 1,000 s/mm2 DWI MRI data, and clinicopathologic variables were integrated into a predictive model. Independent predictors were identified by multivariable logistic regression and receiver operating characteristic (ROC) analysis. Results: Tumor edema extent differed significantly between HER2-zero and HER2-low (P=0.017) and between HER2-low and HER2-overexpressing (P=0.021). T2 signal intensity (SI) also differed between HER2-zero and HER2-low (P=0.012) and between HER2-low and HER2-overexpressing (P=0.001). Multivariate logistic regression analysis revealed that intratumoral T2FS hyperintensity (P=0.001) and L/M ratio (P=0.001) were independent predictors for differentiating the HER2-zero group from the HER2-low group, whereas ADC values (P<0.001), L/M ratio (P=0.028) and Ki-67 levels (P<0.001) were independent predictors for distinguishing the HER2-low group from the HER2-high group. The predictive model achieved areas under the curve (AUCs) of 0.737 for differentiating HER2-zero versus HER2-low and 0.761 for differentiating HER2-low versus HER2-overexpressing. Conclusions: T2WI-derived edema and SI, together with ADC, may serve as valuable noninvasive MRI biomarkers to complement existing approaches for HER2 level assessment, offering a simple and reproducible adjunctive screening method in clinical practice.
Dual perfusion imaging with integrated positron emission tomography-magnetic resonance imaging for the detection of coronary microvascular dysfunction in patients with ischemia with no obstructive coronary artery disease: a study on myocardial perfusion imaging and T1 mapping
Background: Coronary microvascular dysfunction (CMVD) is a common cause of ischemia with no obstructive coronary arteries (INOCAs), a condition previously considered benign but now recognized to confer an increased risk of adverse cardiovascular events. The aim of this study was to compare the diagnostic performance of T1 mapping and myocardial perfusion reserve index (MPRI) in detecting CMVD in patients with INOCA, with positron emission tomography (PET) serving as the reference standard. Methods: Sixty-six patients with INOCA (mean age 55±9 years; 50% female) were prospectively enrolled from August 2024 to February 2026 and underwent an integrated 3.0-T PET-magnetic resonance imaging (MRI) examination. MRI-derived left ventricular function, native T1, postcontrast T1, extracellular volume (ECV), T2 mapping, and regadenoson stress/rest MPRI were obtained and compared between the CMVD group and the non-CMVD group. CMVD was defined as a PET-derived myocardial flow reserve (MFR) <2.0. Results: Patients with CMVD (n=28), as compared with patients without CMVD (n=38), exhibited higher native T1 (1,239±23 vs. 1,205±17 ms; P<0.001) and a lower MPRI (1.66±0.36 vs. 2.15±0.38; P<0.001), while no significant intergroup differences were observed in left ventricular ejection fraction (LVEF) or ECV (all P values >0.05). MFR was correlated with both native T1 (r=−0.513; P<0.001) and MPRI (r=0.577; P<0.001), which were identified as independent predictors of impaired MFR (P=0.015 and P=0.002, respectively). At a cutoff of 1,225 ms, native T1 yielded a sensitivity of 75%, a specificity of 100%, and an accuracy of 89.39%, while MPRI, at a cutoff <1.8, yielded a sensitivity of 75%, a specificity of 81.58%, and an accuracy of 78.79%. Conclusions: Native T1 and MPRI showed nonsignificant differences in their ability to detect CMVD, with consistent and complementary diagnostic value. As a contrast-free method, native T1 may serve as a potential initial screening option, particularly for patients with renal dysfunction.
Left ventricular deformation in chronic schizophrenia—a detailed analysis from the three-dimensional speckle-tracking echocardiographic MAGYAR-Path Study
Background: Schizophrenia (SCH) is a severe mental disorder and one of the top 20 causes of disability, associated with a reduction in life expectancy of more than 15 years. Excess mortality rates in SCH are mainly due to complex vascular and cardiometabolic causes. However, the exact nature of the detailed structural and functional alterations in myocardial mechanics, particularly regarding left ventricular (LV) function, remains insufficiently characterized in the chronic stage of SCH (CS-SCH). The novel three-dimensional (3D) speckle-tracking echocardiography (3DSTE) enables detailed volumetric and strain analysis of the LV from a single acquired 3D echocardiographic dataset. The present study aimed to evaluate LV deformation using 3DSTE in patients with CS-SCH and to compare their data to age-, gender- and body mass index (BMI)-matched healthy controls. Methods: In a cross-sectional case-control design, after the initial screening of 74 CS-SCH outpatients, due to non-compliance and suboptimal image quality, 23 patients (mean age: 45.9±9.3 years, 13 males, BMI: 30.4±5.1 kg/m2) were included in the final analysis and compared to 59 healthy individuals matched for age, gender and BMI (mean age: 46.7±9.4 years, 33 men, BMI: 30.1±4.5 kg/m2). All patients and controls underwent routine two-dimensional Doppler echocardiography extended with detailed 3DSTE analysis. Results: Increased LV volumes were associated with reduced LV ejection fraction (EF) in patients with CS-SCH as compared to controls. All global and mean segmental LV strains were reduced in CS-SCH patients. From regional LV strains, only midventricular LV strains were reduced, whereas basal and apical LV strains did not differ between the two groups. Conclusions: Detailed 3DSTE-derived LV deformation analysis revealed significant abnormalities, including reduced LV-EF and reduced midventricular LV strains, indicating subclinical cardiac damage in the chronic stage of CS-SCH. These findings confirm the importance of cardiovascular assessment and management in this severe mental condition.
Vendor-agnostic versus vendor-specific boost methods in low-concentration contrast-enhanced computed tomography of the abdomen
Background: Contrast media-related adverse events pose serious risks, not only due to allergic reactions but also for patients with renal failure, older age, heart failure, and diabetes mellitus. Various efforts have been made to maximize vascular contrast enhancement (CE) using low-concentration contrast media. This study aimed to compare quantitative and qualitative image characteristics between vendor-agnostic and vendor-specific CE boost methods in low-concentration contrast medium settings against standard-concentration contrast medium settings. Methods: This retrospective study including 160 patients compared two concentrations of iodinated contrast media—350 mg I/mL (standard-concentration group) and 270 mg I/mL (low-concentration group) who underwent contrast-enhanced abdominal computed tomography (CT). Vendor-agnostic and vendor-specific boost methods were applied to the CT images of the low-concentration group. Quantitative [image noise, CT attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise power spectrum, and image sharpness] and qualitative (Likert scale rating by two radiologists) measures of image quality were evaluated in abdominal vessels and liver lesions. Results: The present study included 80 patients in the standard-concentration and 80 patients in the low-concentration group, respectively. There were no significant differences in age (P=0.75), sex (P=0.99), and body mass index (P=0.98) between the two groups. Both vendor-agnostic and vendor-specific CE-boost methods significantly reduced image noise (P<0.001) and improved CT attenuation, SNR, and CNR in both vessels and liver lesions (all P<0.001) in the low-concentration group compared to the corresponding values in the standard-concentration group. The vendor-specific boost method was more effective in suppressing high-frequency noise (P<0.001), whereas the vendor-agnostic boost method yielded sharper images (0.31±0.02 vs. 0.29±0.01; P<0.001). Subjective image analysis revealed significantly higher image quality (P<0.001) for both boost methods compared with standard-concentration contrast medium groups. Conclusions: Although vendor-agnostic and vendor-specific CE-boost methods effectively enhanced image quality and CT attenuation in low-concentration contrast medium settings, subjective image analysis revealed lower ratings due to increased artificial appearance. The vendor-agnostic method provided superior image sharpness, whereas the vendor-specific method more effectively reduced structured noise and achieved higher subjective ratings for overall image quality, artificial suppression, and natural image appearance.
Effect of the dorsiflexion and plantarflexion angles of the foot on the fifth metatarsal bone: finite element analysis
Background: Fractures of the fifth metatarsal bone are frequent in athletes, yet the mechanical conditions that increase stress in this region are not fully understood. Understanding how foot positioning influences local stress may help clarify potential risk factors related to sports activities. The aim of this study was to evaluate how different foot loading directions and ankle positions affect the stress, strain, and deformation of the fifth metatarsal bone using a three-dimensional (3D) computational model. Methods: A 3D model of the human foot was created from computed tomography (CT) imaging and included cortical bone, trabecular bone, and cartilage structures. Six loading conditions representing different ankle positions between forty and ninety degrees were simulated. A load equivalent to approximately three times body weight was applied to replicate impact during unilateral landing. Stress, strain, and deformation of the fifth metatarsal bone were quantified for each loading condition. Results: The highest stress value in the fifth metatarsal bone occurred at 60° of dorsiflexion, with a stress magnitude of 3.18 MPa, a deformation of 0.071 mm, and a strain of 1.46×10−4. The lowest stress value occurred when the load was applied vertically at 90°, with a stress of 0.66 MPa, a deformation of 0.009 mm, and a strain of 2.72×10−5. The first and fifth metatarsal bones consistently showed the greatest concentration of mechanical stress across all load conditions. Conclusions: Changes in foot position substantially influence the mechanical response of the fifth metatarsal bone. Dorsiflexion at sixty degrees generated the highest stress values and may represent a potentially critical condition for this region during high-impact sports movements.
Reference values and cut-points for trunk myopenia and myosteatosis in Chinese adults: a secondary analysis of the China Action on Spine and Hip study
Background: Sarcopenia is a prevalent condition associated with adverse health outcomes, necessitating accurate assessment of skeletal muscle quantity and quality. Computed tomography (CT) is the gold-standard imaging modality for evaluating trunk skeletal muscle area (TrSMA) and density (TrSMD), with opportunistic CT offering a means to assess these metrics without additional radiation exposure. However, the clinical application of CT-based sarcopenia assessment in Chinese populations is hindered by the absence of large-scale, population-specific reference values and cut-off points, particularly for both myopenia (low muscle mass) and myosteatosis (fatty infiltration). This study aims to establish sex- and age-specific percentile reference values and diagnostic cut-off points for CT-derived TrSMA and TrSMD at the L1 and L3 vertebral levels with a large cohort of community-dwelling Chinese adults. Methods: We retrospectively analyzed abdominal CT scans from 4,016 community‑dwelling Chinese adults (21–80 years) across 12 regions. TrSMA and TrSMD at L1 and L3 were segmented using OsiriX. Sex- and age-specific percentile curves were modeled using the lambda-mu-sigma (LMS) method within the generalized additive models for location, scale, and shape (GAMLSS) framework. Estimated age-related differences in TrSMA and TrSMD from ages 25 to 75 years were calculated using LMS-derived medians. Sex‑specific cut‑off points for myopenia and myosteatosis were defined in the 21- to 40-year subgroup using the mean minus two standard deviations (M‑2SD) approach. Results: From ages 25 to 75 years, LMS-derived L1/L3-TrSMA values were lower by 0.45%/0.55% per year in men and 0.33%/0.41% per year in women; corresponding differences in L1/L3-TrSMD were 0.38%/0.35% and 0.55%/0.52% per year. More pronounced age-related decreases were observed in peri- and postmenopausal women and older men. M‑2SD cut‑offs at L1 were 96.0 cm2 and 29.8 Hounsfield Unit (HU) (men), 63.1 cm2 and 26.4 HU (women); at L3 were 122.3 cm2 and 31.7 HU (men), 75.4 cm2 and 27.5 HU (women). Conclusions: We provide the first CT‑based, sex‑ and age‑specific reference values and cut‑points for trunk myopenia and myosteatosis in Chinese adults, facilitating standardized sarcopenia diagnosis and research.
Alterations in macular microvasculature in chronic hepatitis B patients measured by optical coherence tomography angiography
Background: Chronic hepatitis B (CHB) affects approximately 250 million people worldwide and can lead to extrahepatic manifestations, including ocular disorders. However, whether macular microvascular alterations occur in non-cirrhotic CHB patients remains unexplored. This study aimed to quantify superficial macular microvascular density in non-cirrhotic CHB patients utilizing optical coherence tomography angiography (OCTA) and to explore its correlation with clinical characteristics, including liver stiffness measurement (LSM) and virological markers. Methods: This retrospective cross-sectional study enrolled patients with non-cirrhotic CHB who visited the Ophthalmology Department of Xiangya Hospital, Central South University, from January 2015 to August 2021. Healthy individuals matched for age and gender were enrolled as controls. All participants underwent 6×6 mm OCTA imaging focused on the macula. Superficial retinal vessel length density (VLD) and vessel perfusion density (VPD) were compared between the two groups for the central, inner, outer, and full macular zones. Foveal avascular zone (FAZ) parameters and signal strength index (SSI) were also evaluated. Results: A total of 24 eyes from 24 non-cirrhotic CHB patients and 24 eyes from 24 age-matched healthy controls (HC) were evaluated. Patients with non-cirrhotic CHB exhibited significantly higher VLD and VPD in the outer zone (OZ) and outer superior zone (OSZ) of the macula compared to the control group. Subsequent Spearman’s rank correlation analysis revealed that, within the non-cirrhotic CHB group, both VLD and VPD in the OZ showed a negative association with serum hepatitis B e antigen (HBeAg) and hepatitis B e antibody (HBeAb) levels, while being positively related to LSM. Additionally, VLD in the OSZ was also found to be negatively correlated with HBeAg levels. Conclusions: OCTA parameters can be used as non-invasive biomarkers reflecting systemic and liver inflammation related to non-cirrhotic CHB. This method provides a promising approach for early risk stratification in non-cirrhotic CHB management. Future research can further explore the utility of OCTA in tracking disease progression and monitoring the response to anti-inflammatory treatment in this population.
Guidelines for the prevention and management of mother-to-child transmission of tuberculosis
Tuberculosis (TB) during pregnancy poses substantial risks to both the mother and the fetus/infant. Protecting maternal and child health and interrupting mother-to-child transmission (MTCT) are therefore critical components of TB control. However, the evidence base remains limited, and practical guidance across the preconception, pregnancy, postpartum, and neonatal periods is still fragmented. To address this gap, a multidisciplinary panel of experts in TB, obstetrics and gynecology, pediatrics, infectious diseases, imaging, and disease control and prevention was convened. This panel developed standardized, evidence-based recommendations guided by a structured methodology. The guideline was registered on the Practice Guideline Registration and Transparency (PREPARE) platform, developed with reference to the World Health Organization (WHO) Handbook for Guideline Development, reported according to the Reporting Items for Practice Guidelines in Health Care (RIGHT) checklist, and informed by systematic evidence retrieval and Grading of Recommendations Assessment, Development and Evaluation (GRADE)-based assessment. This guideline addresses preconception TB screening, diagnosis and treatment of TB during pregnancy, neonatal assessment, breastfeeding management, and infection prevention and control. Imaging plays a pivotal role in TB screening and diagnosis; accordingly, the guideline systematically outlines the appropriate use and selection of chest radiography, ultrasonography, hysterosalpingography, computed tomography (CT), and magnetic resonance imaging (MRI) for TB evaluation across the preconception, pregnancy, postpartum, and neonatal periods. It further emphasizes the role of imaging in risk stratification, early diagnosis, assessment of disease extent, and neonatal evaluation, and includes pregnancy-specific recommendations on the use of non-contrast MRI. At the same time, the guideline provides evidence-informed recommendations for women with TB infection before conception and for infertile women planning assisted reproductive technologies. Through expert consensus, 15 core recommendations were formulated to support clinical decision-making for healthcare professionals involved in TB care and maternal-child health.
Mediastinal adipose tissue as an active player in cardiovascular disease: a multimodality imaging narrative review
Background and Objective: Mediastinal adipose tissue comprises several distinct fat depots, including epicardial, pericoronary, pericardial, and paracardial adipose tissue, which surround the heart and great vessels and actively contribute to cardiovascular pathophysiology. Under physiological conditions, these adipose compartments exert protective metabolic and mechanical functions; however, in pathological states, their expansion and remodeling promote inflammation, endothelial dysfunction, fibrosis, and atherosclerotic progression. This narrative review aims to summarize the current evidence regarding the anatomical distribution, imaging assessment, and clinical implications of mediastinal adipose tissue in cardiovascular disease. Methods: A narrative review was conducted using PubMed, Scopus, and Web of Science databases, covering publications up to January 2026. Only articles published in English were considered. Original research articles and relevant reviews focusing on imaging, pathophysiology, and clinical implications of mediastinal adipose tissue were included. Reference lists of selected studies were additionally screened to identify additional relevant publications. Key Content and Findings: Computed tomography (CT) enables highly reproducible volumetric and qualitative assessment of cardiac adipose tissue, including inflammatory markers such as the fat attenuation index (FAI). Magnetic resonance imaging (MRI) provides excellent tissue characterization without ionizing radiation, whereas echocardiography represents a simple and widely available screening tool. Increased epicardial, pericoronary, and pericardial adipose tissue (PAT) burden has been consistently associated with coronary artery disease (CAD), atrial fibrillation (AF), and heart failure (HF), particularly HF with preserved ejection fraction (HFpEF). Emerging evidence also supports the potential role of artificial intelligence (AI) and radiomics for automated quantification and cardiovascular risk stratification. Conclusions: Mediastinal adipose tissue is increasingly recognized as an active biomarker and potential therapeutic target in cardiovascular disease. Multimodality imaging plays a central role in its evaluation by providing complementary anatomical, functional, and inflammatory information. Further standardization of imaging methodologies and validation of treatment-related changes are needed to support future clinical applications and personalized cardiovascular risk assessment.
Prenatal sonographic clues suggestive of hypohidrotic ectodermal dysplasia in a fetus: a case description
Prenatal diagnosis of a fetus with cri-du-chat syndrome accompanied by 18q duplication: a case description
Multimodality imaging in the diagnosis of a right atrial lipoma with normal electrocardiogram: a case description
Infectious internal iliac artery pseudoaneurysm secondary to perforated infantile appendicitis: a case description
Imaging manifestations of nuclear protein in testis carcinoma in the maxillary sinus with elevated alpha-fetoprotein: a case description and literature analysis
Missing incremental diagnostic value analysis: does NH-IMRangio add anything to readily available clinical and cardiac magnetic resonance metrics?
Three-dimensional sonographic diagnosis of diamniotic conjoined twins
A case of fetal decapitation with intrauterine survival: prenatal diagnosis by three-dimensional transvaginal ultrasound
False-positive and true-positive orbital iodine-131 uptake after radioiodine therapy: two contrasting cases clarified by SPECT/CT
Echocardiographic diagnosis of Libman-Sacks endocarditis in systemic lupus erythematosus: description of two cases
Erratum: U-fiber analysis: a toolbox for automated quantification of U-fibers and white matter hyperintensities
Erratum: Association between a novel ultrasound-based measure of plaque burden and cardiovascular risk: the P-SONAR study