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 Manuscripts submitted must be the original work of the author(s) and must not be published previously or under consideration for publication elsewhere.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. 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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. Address: Hong Kong branch office: Flat/RM C 16F, Kings Wing Plaza 1, NO. 3 on Kwan Street, Shatin, NT, Hong Kong, China. Updated on July 10, 2026
Adaptive deep learning for quantification and spatial distribution of abdominal adipose tissue from magnetic resonance imaging proton density fat fraction in adults with a body mass index ≥24 kg/m2
Background: Abdominal adipose tissue volume and spatial distribution are closely associated with a variety of metabolic diseases and reflect individual metabolic risk and health status. This study aimed to evaluate the feasibility of an adaptive deep learning framework for the automated volumetric quantification and spatial mapping of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) on magnetic resonance imaging (MRI) proton density fat fraction (PDFF) in adults with a body mass index (BMI) ≥24 kg/m2. Methods: In this single-center retrospective study, a total of 238 abdominal MRI PDFF datasets from adults with a BMI ≥24 kg/m2, acquired with a 3.0-T mDixon sequence, were analyzed. A no new U-net (nnU-Net)-based adaptive deep learning model was trained to automatically segment abdominal VAT and SAT and to generate volumetric measurements across all slices. Spatial adipose distribution maps were constructed and stratified by sex and age. Results: The adaptive deep learning-based segmentation model achieved high segmentation performance on the internal independent test set, with a mean Dice coefficient >0.966 and a mean relative volume error of −1.0335%. The framework enabled rapid and automated abdominal fat quantification, requiring approximately 40 seconds per case for automatic segmentation, much shorter than the approximately 1–2 hours per case for manual segmentation. Regional spatial distribution analysis showed pronounced heterogeneous distribution patterns of VAT and SAT across different abdominal anatomical regions, with distinct differences in spatial distribution patterns across sex and age groups. Specifically, VAT volume was significantly higher in men than in women (P<0.001). In the age-stratified analysis, SAT volume differed significantly across age groups (P<0.001), whereas the difference in VAT volume did not reach statistical significance (P=0.096). In the BMI category-stratified analysis, both SAT and VAT volumes differed significantly across BMI categories (both P<0.001). Within the same BMI categories, VAT showed greater relative variability than did SAT, with coefficients of variation ranging from 36.93% to 56.67% and from 23.52% to 33.30%, respectively. Conclusions: An adaptive deep learning framework based on nnU-Net enables automated quantification and spatial distribution characterization of abdominal adipose tissue in overweight and obese Chinese individuals and may thus serve as methodological platform for subsequent multicenter external validation and clinical translation studies.
Preoperative assessment of recurrence-prone subregions within the peritumoral region of glioblastoma: a comparison between diffusion kurtosis and diffusion tensor imaging
Background: Glioblastoma (Gb) is characterized by extensive infiltration beyond the contrast-enhancing tumor margin, and most recurrences occur within the peritumoral region. Preoperative identification of recurrence-prone subregions within the peritumoral area may help optimize local treatment strategies. Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) can provide quantitative information on tissue microstructure, but their value in distinguishing recurrence-prone and non-recurrent subregions within the peritumoral region of Gb remains unclear. This study aimed to evaluate and compare the value of preoperative DKI- and DTI-derived histogram parameters in distinguishing recurrence-prone from non-recurrent subregions within the peritumoral region of Gb. Methods: This retrospective study included 55 patients with Gb who subsequently developed recurrence and underwent preoperative magnetic resonance imaging (MRI), including conventional sequences, DKI, and DTI. DKI data were processed to generate DKI- and DTI-derived parametric maps. All preoperative structural images and post-recurrence contrast-enhanced images were automatically coregistered to the preoperative diffusion parametric map space using ITK-SNAP. The edema region on preoperative fluid-attenuated inversion recovery (FLAIR) images and the contrast-enhancing lesion at first recurrence were initially segmented using an nnU-Net model and then jointly reviewed by two radiologists. The overlap between the preoperative FLAIR-hyperintense region and the contrast-enhancing lesion at first recurrence was defined as the recurrence-prone region, whereas the remaining FLAIR-hyperintense area was defined as the non-recurrent region. Histogram parameters were extracted from both regions. Differences in diffusion parameters were analyzed using paired-samples t-tests. P values for parameters showing significant differences were further adjusted using the Benjamini-Hochberg procedure. Receiver operating characteristic (ROC) analysis was performed, and the area under the curve (AUC) was calculated. Results: In the DKI model, maximum radial kurtosis (RKmax) demonstrated the highest discriminatory power between recurrence-prone and non-recurrent regions, with an AUC of 0.868, sensitivity of 0.891, and specificity of 0.746. In the DTI model, maximum fractional anisotropy (FAmax) showed the best diagnostic performance, with an AUC of 0.834, sensitivity of 0.818, and specificity of 0.746. In this cohort, RKmax showed a higher AUC than FAmax. Conclusions: Preoperative DKI histogram analysis may help identify recurrence-prone subregions within the peritumoral region of Gb. In this cohort, the best-performing DKI-derived parameter showed the highest AUC compared with the best-performing DTI-derived parameter. These findings are exploratory and require validation in larger studies.
Clinical application of point-of-care ultrasound during cardiopulmonary resuscitation: a systematic review and meta-analysis
Background: Point-of-care ultrasound (PoCUS) is now more commonly being utilized in cardiopulmonary resuscitation to offer immediate evaluation of cardiac motion and to direct management. However, the evidence is still heterogeneous. Therefore, the purpose of this systematic literature review and meta-analysis is to assess the clinical utility of PoCUS in terms of its effects on resuscitation outcomes. Methods: A systematic review and meta-analysis were performed by searching MEDLINE, EMBASE, Cochrane Central, Web of Science, and Scopus databases. Studies on adult patients with in-hospital cardiac arrest or out-of-hospital cardiac arrest (OHCA) were included following predefined Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) criteria. Two reviewers independently screened, extracted data, and appraised quality using Risk of Bias 2 (RoB 2) tool for randomized trials and the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tools. Subgroup and sensitivity analyses were performed to assess robustness and the presence of publication bias was evaluated. Results: Pooled results of all included studies involving patient populations showed that PoCUS during cardiopulmonary resuscitation (CPR) increased the rate of the return of spontaneous circulation (ROSC) (43% vs. 28%). Survival to hospital discharge was also greater in the PoCUS group (19 % vs. 12 %). Subgroup and sensitivity analyses confirmed the robustness of the results, and no significant publication bias was found. Conclusions: This systematic review and meta-analysis indicates that performing PoCUS during CPR is associated with better clinical outcomes, including increased ROSC and survival to hospital discharge, further highlighting the potential advantages of PoCUS to assist in cardiac arrest management as well as making clinical decisions.
Characterization of carotid remodeling in gestational hypertension using radiofrequency data-based vessel stiffness analysis and vector flow imaging
Background: Gestational hypertension (GH) is associated with maternal vascular remodeling; however, the biomechanical and hemodynamic features related to increased carotid intima-media thickness (CIMT) remain insufficiently characterized. This study aimed to evaluate carotid remodeling in women with GH using radiofrequency data-based quantitative vessel stiffness (R-QVS) analysis and vector flow imaging (VFI). Methods: This prospective observational study included 347 pregnant women, comprising 108 normotensive controls and 239 women with GH. The GH group was further divided into normal-CIMT and increased-CIMT subgroups based on a mean CIMT threshold of 1.0 mm. All participants underwent standardized carotid ultrasonography. Stiffness-related parameters, including the hardness coefficient (HC) and pulse wave velocity (PWV), and flow-related parameters, including maximum wall shear stress (WSSmax) and mean wall shear stress (WSSmean), were measured. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis, and the factors associated with increased CIMT were evaluated using logistic and linear regression analyses. Results: Carotid stiffness increased and wall shear stress (WSS) decreased progressively from the control group to the GH normal-CIMT and GH increased-CIMT groups. The GH increased-CIMT group had the highest PWV and the lowest WSSmean. The PWV values were 5.87±0.68, 7.05±0.77, and 8.11±0.85 m/s in the control, GH normal-CIMT, and GH increased-CIMT groups, respectively, while the WSSmean values were 1.30±0.38, 0.89±0.32, and 0.65±0.27 Pa, respectively; all group differences were statistically significant. The HC and PWV showed strong diagnostic performance in the detection of increased CIMT, with areas under the curve (AUCs) of 0.909 and 0.885, respectively. In the multivariable logistic regression analysis, pre-pregnancy body mass index (BMI), systolic blood pressure (SBP), triglycerides (TG), and PWV were independently associated with increased CIMT, while WSSmean was higher WSSmean was independently associated with lower odds of increased CIMT. In the linear regression analysis, the same key predictors were associated with CIMT as a continuous outcome, and the model explained 56.8% of variance in CIMT, with an adjusted R2 of 0.553. Conclusions: Women with GH exhibited a progressive high-stiffness and low-shear carotid phenotype, particularly in the presence of increased CIMT. Integrating R-QVS analysis and VFI may improve vascular risk stratification in hypertensive pregnancies.
Deep learning-based computed tomography detection of early lymph node metastasis in head and neck cancer
Background: Cervical lymph node metastasis (LNM) significantly influences the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). However, conventional computed tomography (CT) diagnostics are susceptible to high false-negative rates for small lesions, exhibit considerable inter-reader variability, and are labor-intensive due to the requirement for manual evaluation by radiologists. This study aimed to develop and validate a deep learning-based model incorporating an attention mechanism, termed the deep learning-based cervical lymph node metastasis detection model (DL-CervLNM), to automatically detect LNM in contrast-enhanced CT scans. This model was designed to address challenges related to the detection of small lesions, inter-observer variability, and diagnostic efficiency. Methods: A total of 6,860 contrast-enhanced CT scans from 481 patients with HNSCC at Fujian Cancer Hospital between 2020 and 2024 were retrospectively collected and analyzed for model development. An asymmetric context-aware cascade detection (ACCD) framework was introduced, consisting of two principal modules: (I) an attention-enhanced You Only Look Once version 8 (YOLOv8) stage, specifically designed to generate candidate regions with high recall rates; and (II) a region-based refinement stage that incorporates the Faster region-based convolutional neural network (R-CNN) algorithm to enable context-aware suppression of false positives. The efficacy of the ACCD framework was rigorously assessed in comparison to board-certified radiologists and state-of-the-art baseline models. The primary evaluation metrics included the mean average precision (mAP) at an intersection-over-union (IoU) threshold of 0.5 (mAP@0.5), the area under the receiver operating characteristic curve (AUC), the F1-score, and computational efficiency metrics. Results: In the context of LNM detection using CT imaging, DL-CervLNM achieved a mAP@0.5 of 94.9% and an AUC of 0.980, outperforming both current state-of-the-art models and experienced radiologists. The model exhibited enhanced sensitivity (90.6%) and specificity (94.5%), with a notable proficiency in identifying small lesions, achieving an accuracy of 93.2% compared to 76.4% for radiologists. In addition, the system improved clinical workflow efficiency by enabling real-time processing at 38.5 frames per second (FPS), thereby significantly reducing interpretation and reporting times by 51.7% and 98.6%, respectively. Conclusions: DL-CervLNM exhibited enhanced accuracy and reliability in detecting cervical LNM on CT scans compared to current methodologies. This model has the potential to improve diagnostic efficiency and consistency, thereby aiding radiologists in the early identification of metastatic lymph nodes.
Application of numerical simulation of upper airway flow field in pediatric obstructive sleep apnea with adenoid hypertrophy
Background: At present, there is a lack of tools in clinical practice to evaluate the impact of obstructive sleep apnea (OSA) with adenoid hypertrophy (AH) on the upper airway (UA) flow field. This study was designed to analyze the UA aerodynamic parameters in pediatric OSA with AH, and explore its utility in diagnosing, and evaluating prognosis after adenoidectomy (AT). Methods: Based on high-resolution computed tomography (CT) image data from normal children, pre- and post-surgical AH children, we constructed three-dimensional (3D) numerical models of the UA incorporating anatomical features. Computational fluid dynamics (CFD) was employed to simulate and analyze the dynamic characteristics of the UA flow field. Results: AH led to a significant 68.3% reduction in nasopharyngeal airway cross-sectional area (CSA, normal: 302.3 mm2, AH: 95.8 mm2), which in turn resulted in an 81.1% maximum pressure (Pmax) increase within the stenotic region, a marked elevation in flow velocity (with a near 210% maximum increase), a 22.2% maximum rise in wall shear stress (WSS), and a 69.5% maximum decrease in volumetric flow rate. CFD analysis following AT demonstrated that nasopharyngeal stenosis had been alleviated, with airway pressure, flow velocity, and WSS all returning to near-normal physiological levels. Conclusions: Numerical simulation of the UA flow field provided a new perspective to unravel the mechanism by which AH affects inspiratory airflow. Additionally, it holds broad clinical application prospects in aided disease diagnosis, treatment regimen optimization, and therapeutic efficacy evaluation.
Comparative study of transabdominal ultrasound alone versus combined transabdominal and transvaginal ultrasound in gynecological acute abdomen
Background: Gynecological acute abdomen is a common emergency condition requiring rapid and accurate diagnostic imaging to guide timely clinical decision-making. This study evaluated the diagnostic value of transabdominal ultrasound (TA-US), transvaginal ultrasound (TV-US), and their combination in patients with suspected gynecological acute abdomen. Methods: This single-center retrospective study analyzed 120 patients with suspected gynecological acute abdomen, divided into three groups (n=40 each) based on the ultrasound examination received: TA-US, TV-US, and combined ultrasound. Detection rates for different types of gynecological acute abdomen were compared, along with diagnostic accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Kappa test was used to evaluate consistency with comprehensive clinical diagnoses. Results: Comprehensive clinical diagnosis identified 65 cases of ectopic pregnancy and 39 cases of corpus luteum rupture. Combined TA-US and TV-US achieved the highest detection rates for ectopic pregnancy (100.00%) and corpus luteum rupture (93.33%), outperforming TA-US (69.57%, 75.00%) and TV-US (77.27%, 83.33%) alone (P<0.05). Overall detection rates for TA-US, TV-US, and combined were 70.00%, 77.50%, and 95.00%, respectively, with combined being the highest (P<0.05). The kappa coefficients for combined, TA-US, and TV-US were 0.771, 0.186, and 0.343, indicating good consistency between combined examination and clinical diagnosis (P<0.05). Combined examination achieved accuracy (95.00%), sensitivity (97.14%), specificity (80.00%), PPV (97.14%), and NPV (80.00%). Its accuracy and sensitivity were significantly superior to TA-US (70.00%, 71.43%) and TV-US (77.50%, 79.41%), and its NPV was also higher than TA-US (all P<0.05). Conclusions: TA-US and TV-US each have diagnostic value for gynecological acute abdomen, but their combined use improves accuracy and sensitivity, providing a more reliable clinical assessment.
Peak filling rate assessed by myocardial perfusion imaging predicts adverse outcomes in women with chronic coronary syndromes
Background: Diastolic dysfunction may represent an important risk dimension beyond myocardial ischemia and systolic impairment, particularly in women with chronic coronary syndromes (CCS). However, the prognostic value of myocardial perfusion imaging (MPI)-derived diastolic parameters and their potential sex-specific differences remain uncertain. Therefore, this study aimed to evaluate the prognostic significance of single photon emission computed tomography (SPECT) MPI-derived diastolic parameters in patients with CCS without reduced ejection fraction (EF <40%). Methods: In this retrospective cohort study, data of 1,446 patients undergoing resting MPI were analyzed at the First Hospital of Shanxi Medical University [2016–2021]. The diastolic parameters obtained from MPI included peak filling rate (PFR), mean filling rate over the first third of the end-systole to end-diastole phase (MFR/3), and time to peak filling from end-systole (TTPF). Propensity score matching balanced baseline clinical characteristics between sexes. Multivariable Cox regression evaluated the associations between sex, diastolic parameters, and major adverse cardiovascular events (MACEs). These analyses were further differentiated by total perfusion deficit (TPD) ≥10% and EF ≥50% groups. Results: (I) Over a median 3.05-year follow-up, 519 patients (35.9%) experienced MACE. (II) No statistically significant differences were observed in the predictive value for MACE among PFR, MFR/3, TTPF, and the combination of these three parameters (P=0.956). (III) Reduced PFR independently predicted MACE in women [hazard ratio (HR) 0.61, P=0.028] but not in men (P=0.167). This sex difference persisted in patients with TPD ≥10% (HR 0.36) and EF ≥50% groups (HR 0.57, all P<0.05). Conclusions: In patients of CCS without reduced EF, MPI-derived PFR demonstrates sex-specific prognostic value, serving as an independent predictor of MACE in women but not men. These findings highlight the potential of diastolic analysis to refine risk stratification in women.
Capability of arterial spin labeling combined with morphological characteristics to distinguish early- and mid-stage chronic kidney disease: a prospective study on 5 T MRI
Background: Non‑invasive imaging biomarkers are increasingly recognized for their potential to complement estimated glomerular filtration rate (eGFR) in chronic kidney disease (CKD) staging, yet the combined diagnostic value of arterial spin labeling (ASL) perfusion and morphological parameters at 5 Tesla (5 T) magnetic resonance imaging (MRI) remains unexplored. In this study, we prospectively investigated the capability of ASL combined with morphological characteristics in identifying early‑stage (G1–G2) versus mid‑stage (G3A–G3B) CKD. Methods: A total of 103 clinically diagnosed CKD patients (66 in early-stage, 37 in mid-stage) underwent renal MRI examination on 5 T. Quantitative measurements included renal length, width, thickness, cortical thickness, cortical signal homogeneity, and cortical renal blood flow (RBF). Qualitative evaluation of corticomedullary differentiation (CMD) was also performed. Independent samples t-test, Mann-Whitney U test, and Pearson χ2 test were used to compare differences between groups. Significant imaging characteristics underwent multivariable logistic regression to identify independent diagnostic factors. The diagnostic efficacy of each characteristic and the combined RBF-morphology model was assessed using the receiver operating characteristic (ROC) curve. Results: Compared with the early-stage group, the mid-stage group showed significantly lower renal length (t=2.62), width (Z=−2.61), cortical thickness (Z=−3.69), and RBF (Z=−5.07), along with greater cortical signal heterogeneity (Z=−4.55) and worse CMD contrast (χ2=25.97) (all P<0.001). Multivariate analysis identified renal length (P=0.004), CMD contrast (P=0.010), cortical signal heterogeneity (P=0.022), and RBF values (P=0.002) as independent factors for differentiating early- from mid-stage CKD. ROC analysis showed that the area under the curve (AUC) values for renal length, CMD contrast, cortical signal heterogeneity, RBF values, and the combined RBF-morphology model were 0.629 (P=0.024), 0.707 (P<0.001), 0.771 (P<0.001), 0.802 (P<0.001), and 0.916 (P<0.001), respectively. Conclusions: The combination of ASL and morphological characteristics achieves significantly better diagnostic performance between early- and mid-stage CKD than any single imaging characteristic, suggesting that 5 T MRI-derived perfusion and morphological biomarkers may serve as complementary imaging indicators for assessing renal structural and functional alterations in CKD.
Altered corpus callosum development and corpus callosum-cerebellar spatial relationships in preterm infants at term-equivalent age
Background: Preterm (PT) birth disrupts critical third-trimester brain maturation, particularly affecting the corpus callosum (CC) and cerebellum. As interconnected components of cerebro-cerebellar networks, their developmental trajectories and spatial relationships remain incompletely understood. This study used serial cranial ultrasonography to characterize these alterations from birth to term-equivalent age (TEA). Methods: This prospective observational study included 117 neonates, comprising 60 term infants and 57 PT infants. A total of 719 cranial ultrasound examinations were analyzed. PT infants underwent serial weekly ultrasonography from birth to 40 weeks’ corrected gestational age (CGA), whereas term infants were examined within the same TEA window. CC morphometry, including CC length (CCL), CC curve length (CCCL), height, area, and thickness, CC-fastigium length (CCFL), CC-fastigium angle (CCFA), and vermis morphometry were measured. Developmental trajectories were assessed using nonlinear regression, segmented linear regression, and linear mixed-effects models. Multivariable regression was used to identify factors independently associated with morphometric outcomes. Results: At TEA, the PT infants had shorter CCL (39.8±2.3 vs. 42.5±3.8 mm, adjusted P=0.015), shorter CCCL (53.7±6.0 vs. 68.5±17.4 mm, adjusted P=0.004), smaller vermis area (400.3±94.6 vs. 455.9±81.1 mm2, adjusted P=0.041), shorter CCFL (42.1±10.3 vs. 48.6±3.0 mm, adjusted P<0.001), and larger CCFA (43.1±6.8° vs. 33.6±4.5°, adjusted P<0.001) than the term controls. In the PT infants, segmented regression showed rapid CCL growth before 35 weeks’ CGA (1.04 mm/week, P<0.001), followed by no significant linear growth thereafter (P=0.059). CCFL continued to increase after 35 weeks but at a slower rate (0.55 mm/week, P<0.001), whereas CCFA showed an exploratory nonlinear trajectory. In the multivariable analysis, PT birth was independently associated with shorter CCL [β=−2.30, 95% confidence interval (CI): −4.15 to −0.46, P=0.015] and larger CCFA (β=7.04, 95% CI: 3.60–10.48, P<0.001). Conclusions: PT birth is associated with quantitatively measurable alterations in CC development, cerebellar vermis size, and CC-cerebellar spatial configuration at TEA. Serial cranial ultrasonography captured divergent postnatal growth trajectories, including a late-PT inflection in CC growth and persistent enlargement of CCFA. These findings support ultrasound-based longitudinal morphometric monitoring of postnatal brain development in PT infants.
Imaging study of white matter microstructural damage and glymphatic system dysfunction in patients with primary insomnia: a combined analysis based on PSMD, DTI-ALPS, and EPVS
Background: Primary insomnia is associated with cognitive impairment and subtle brain structural alterations; however, the relationship between white matter microstructural damage and glymphatic-related imaging changes remains unclear. This study investigated these alterations by jointly assessing peak width of skeletonized mean diffusivity (PSMD), diffusion tensor imaging analysis along the perivascular space (DTI-ALPS), and enlarged perivascular spaces (EPVS). Methods: This retrospective cross-sectional study included 64 right-handed adults, comprising 32 patients with primary insomnia and 32 healthy controls. All participants underwent standardized assessments of sleep, mood, and cognition, as well as 3.0-T magnetic resonance imaging, including diffusion tensor imaging and T2-weighted imaging. PSMD and the DTI-ALPS index were calculated from diffusion data, and EPVS in the basal ganglia and centrum semiovale were visually rated. Between-group differences were evaluated using independent-samples t-tests or Mann-Whitney U tests with false discovery rate correction. Ordinal logistic regression was used to adjust the basal ganglia EPVS comparison for demographic and vascular risk factors. Partial correlations were adjusted for age, sex, and education, with additional analyses further adjusted for depressive and anxiety symptoms. Results: Patients with primary insomnia had higher PSMD values than healthy controls (P=0.047). Basal ganglia EPVS scores were higher in the unadjusted comparison (P=0.040), but this association was attenuated after adjustment for age, education, sex, diabetes, hypertension, smoking, and alcohol consumption (odds ratio =0.329, 95% confidence interval: 0.099–1.091; P=0.069). No significant between-group differences were found in the DTI-ALPS index (P=0.267) or centrum semiovale EPVS scores (false discovery rate-adjusted P=0.211). In the full sample, higher PSMD was associated with lower DTI-ALPS values (r=−0.331, adjusted P=0.019), higher basal ganglia EPVS scores (r=0.456, adjusted P=0.001), and lower Montreal Cognitive Assessment (r=−0.329, adjusted P=0.019) and Mini-Mental State Examination scores (r=−0.440, adjusted P=0.001). These associations remained significant after additional adjustment for depressive and anxiety symptoms. Conclusions: Primary insomnia was associated with greater white matter microstructural heterogeneity, whereas evidence of glymphatic dysfunction was partial and indirect. Higher PSMD was related to glymphatic-related imaging markers and poorer cognitive performance; however, the small sample and cross-sectional design warrant cautious interpretation.
Impact of acquisition geometry on ultrasound-derived fat fraction measurements: implications for standardized acquisition and measurement interchangeability
Background: Ultrasound-derived fat fraction (UDFF) is an emerging quantitative imaging biomarker for noninvasive assessment of hepatic steatosis. However, the influence of acquisition geometry on UDFF measurements remains incompletely characterized. This study aimed to determine the effects of anatomical measurement window, subcapsular sampling depth, and skin-to-liver capsule distance (SCD) on UDFF measurements and measurement interchangeability. Methods: In this prospective study, 238 volunteers underwent UDFF measurements at five predefined hepatic windows and four standardized subcapsular sampling depths (1.0, 1.5, 2.0, and 3.0 cm). The right anterior intercostal window with a subcapsular sampling depth of 1.5 cm served as the prespecified internal methodological reference configuration for within-case comparisons. Reference-based within-case differences and Bland-Altman analyses were used to assess window- and depth-dependent reference-relative differences and agreement. Linear mixed-effects models were applied to evaluate associations of acquisition geometry and SCD with systematic differences and absolute deviations. Intra- and inter-operator repeatability were assessed using intraclass correlation coefficients (ICCs). Results: UDFF values differed significantly across anatomical windows at a standardized depth (P<0.001). Left-lobe measurements demonstrated substantial positive bias relative to the reference window (+4.96% to +6.05%) and wide limits of agreement, whereas smaller but statistically significant window-dependent biases persisted within the right lobe. Subcapsular sampling depth exerted a systematic effect on UDFF, with shallow measurements at 1.0 cm showing positive bias (+2.03%), whereas measurements at 2.0 and 3.0 cm demonstrated small negative biases relative to the 1.5-cm reference. Greater SCD was independently associated with larger absolute deviations (β =+1.05% per cm, P<0.001). Under the standardized reference configuration, UDFF demonstrated good repeatability, with both intra- and inter-operator ICCs of 0.83. Conclusions: UDFF measurements are strongly influenced by acquisition geometry, particularly anatomical window selection and subcapsular sampling depth. Reference-based analysis demonstrates limited individual-level interchangeability across hepatic windows in the studied cohort, underscoring the importance of standardized acquisition protocols to reduce acquisition-dependent variability and to support reliable and comparable quantitative UDFF measurements in longitudinal and multicenter settings.
Parotid gland tumor computed tomography image segmentation and benign-malignant differentiation diagnosis
Background: Parotid gland tumors are the most common type of salivary gland neoplasms. The accurate determination of tumor nature is essential for clinical treatment decision-making. Existing deep learning (DL)-based methods for image segmentation and differential diagnosis often yield blurred boundaries when segmenting parotid tumors on computed tomography (CT) images and face difficulties in distinguishing benign from malignant lesions. This study aimed to develop a solution based on an improved TransUNet to achieve precise tumor segmentation and to enable automatic benign–malignant differentiation of parotid gland tumors by fusing CT images with clinical text information. Methods: First, we propose an enhanced PT-TransUNet segmentation model. Built upon the classic TransUNet architecture, it incorporates a learnable Difference of Gaussians (DoG) edge enhancement module at the front end to adaptively sharpen tumor boundary features. In addition, a PSA-CBAM module—which integrates multi-scale convolution with a convolutional block attention module—is embedded in the decoder to improve the model’s capability to capture multi-scale features. Second, we construct a multimodal diagnostic model that uses PT-TransUNet as the image branch to extract CT image features, employs the Chinese BERT Whole Word Masking (WWM) model as the text branch to extract clinical text features, and performs deep feature fusion via a bidirectional cross-modal attention mechanism to achieve binary classification of benign versus malignant tumors. Results: On a dataset of 158 CT images, PT-TransUNet achieved Dice coefficients of 0.8212±0.0269 for parotid gland segmentation and 0.8075±0.0245 for parotid tumor segmentation, compared with 0.8118±0.0324 and 0.7928±0.0350 by nnUNet, respectively (P<0.05). The 95% Hausdorff distance (HD95) was 16.14±0.58 mm for benign tumors and 6.28±0.58 mm for malignant tumors. In the multimodal diagnostic task, the fusion model using bidirectional cross-modal attention achieved an accuracy of 0.8909±0.0249 and a sensitivity of 0.9000±0.1369, whereas the CT-only model achieved 0.6091±0.0249 accuracy and 0.1500±0.1369 sensitivity, and the text-only model achieved 0.7273±0.0321 accuracy and 0.4000±0.1369 sensitivity (P<0.001 for both comparisons). Compared with feature-level concatenation, which achieved an accuracy of 0.8455±0.0518, and decision-level weighted average, which achieved an accuracy of 0.7545±0.0249, the cross-modal attention mechanism yielded higher accuracy. Conclusions: This study confirms the superiority of the proposed PT-TransUNet model for CT image segmentation of parotid tumors and highlights the great potential of the cross-modal fusion strategy that integrates CT images with clinical text information for benign-malignant differentiation. This approach provides a new technical pathway for automated and accurate assisted diagnosis of parotid gland tumors and holds important value for clinical application.
Spectral computed tomography multi-parameter images for differentiating between tumor tissue and inflammatory tissue in pneumonic-type lung carcinoma lesions
Background: Pneumonic-type lung carcinoma (PTLC) is a special type of lung cancer with imaging manifestations similar to pneumonia. Due to the intralesional heterogeneity of PTLC, it is difficult to accurately select the optimal target for needle biopsy. Differentiating between tumor tissue and inflammatory tissue within the lesion before puncture could significantly increase the biopsy rate and facilitate early diagnosis. This study aimed to investigate the utility of spectral computed tomography (CT) parameters in differentiating between tumor tissue and inflammatory tissue in PTLC lesions, providing a novel method for target selection before needle biopsy. Methods: A total of 102 patients with PTLC and 96 patients with pneumonia were retrospectively enrolled in the study between September 2020 and April 2024 to evaluate the differentiation of tumor and inflammatory tissue components in PTLC lesions. An additional 30 patients with high suspicion of PTLC were prospectively enrolled to validate the diagnostic accuracy of tumor tissue localization. All patients underwent contrast-enhanced spectral CT examinations, and final diagnoses were confirmed based on pathological findings and follow-up CT evaluations. The ability to visually distinguish tumor tissue from inflammatory tissue in PTLC lesions using conventional CT imaging and spectral CT imaging was evaluated. Conventional CT (CTconventional) values, iodine density (ID), 40-keV monochromatic energy CT (CT40keV) values, and Z-effective (Zeff) values were measured in the suspicious tumor area (STA), suspicious inflammatory area (SIA), tumor area (TA), and inflammatory area (IA). The Mann-Whitney U test was used to compare spectral CT parameters among STA, TA, SIA, and IA, and the diagnostic efficiency of each parameter for differentiating tumor from inflammatory tissues was evaluated. The cut-off value of arterial-phase (AP) ID was applied to guide targeted biopsy in 30 prospectively enrolled patients with PTLC, and the diagnostic accuracy was evaluated. Results: Among the 102 patients with PTLC, the numbers of cases in which tumor tissue and inflammatory tissue could be differentiated on AP/venous phase (VP) images were 34/27 for conventional CT images, 90/81 for ID images, 87/71 on 40-keV monoenergetic images, and 95/84 for Zeff images, respectively. Spectral CT images exhibited significantly higher discriminatory efficiency than conventional CT images (Q =157.48 for AP and Q =138.87 for VP; both P<0.001). In both the AP and VP, the SIAs presented with significantly higher CTconventional, CT40keV, ID, and Zeff values than the STAs (all P<0.001). The AP ID value demonstrated good diagnostic performance, with a cut-off value of 1.47 mg/mL [area under the curve (AUC) =0.913, specificity =94.10%]. In the prospective validation cohort of 30 patients, an accuracy of 96.7% was achieved. Conclusions: Spectral CT parametric imaging can effectively differentiate tumor tissue from inflammatory tissue in PTLC lesions and may facilitate accurate target selection for biopsy.
Resting-state functional MRI analysis of the brain’s adaptation to chronic hypoxia: disease and environment
Background: The brain is highly sensitive to oxygen supply; however, it remains unclear whether chronic hypoxia resulting from disease or environmental exposure leads to similar or distinct patterns of brain adaptation. This study aimed to characterize and compare the structural and functional brain changes associated with chronic hypoxia induced by chronic obstructive pulmonary disease (COPD) and high-altitude residence. Methods: A total of 110 participants were enrolled in the study, including 20 patients with COPD (aged 45–65 years) and 30 matched controls, as well as 30 high-altitude residents (aged 35–50 years, altitude >2,800 m for >30 years) and 30 matched low-altitude controls. Structural brain assessment was performed using three-dimensional (3D) T1-weighted imaging, with voxel-based morphometry (VBM) analysis conducted via statistical parametric mapping. Functional evaluation was performed using regional homogeneity (ReHo) analysis with the Data Processing and Analysis of Brain Imaging (DPABI) toolkit based on resting-state blood oxygen level-dependent (BOLD) data. Group comparisons were performed using two-sample t-tests, with statistical significance set at P<0.05. Results: Patients with COPD exhibited reduced gray matter volume in eight regions, predominantly located in emotion- and cognition-related regions, including the Temporal_inf_R (cluster size =194, t=3.50), Rectus_R (cluster size =145, t=3.72), ParaHippocampal_L (cluster size =158, t=3.68), and Insula_R (cluster size =201, t=4.38). The high-altitude residents showed gray matter changes primarily in visual regions, including the Cuneus_L (cluster size =162, t=2.79) and Occipital_Mid_L (cluster size =140, t=−2.82). ReHo analysis revealed increased ReHo in the bilateral caudate (R: cluster size =125, t=2.99; L: cluster size =89, t=2.34) and decreased ReHo in the Lingual_R (cluster size =94, t=−4.93) and Precuneus_R (cluster size =92, t=−4.78) in the COPD patients, while the high-altitude residents showed increased ReHo in the bilateral supplementary motor area (R: cluster size =164, t=3.80; L: cluster size =144, t=3.48) and decreased ReHo in the Precuneus_R (cluster size =82, t=−4.08). No significant differences in age or education were observed between the groups; however, the hemoglobin and oxygen saturation (SpO2) levels differed significantly between the groups (P<0.01). Conclusions: These findings indicate that COPD and high-altitude residence are associated with distinct structural brain changes, affecting emotion- and cognition-related regions in patients with COPD and visual regions in high-altitude residents, despite broadly similar functional (ReHo) alterations in motor and visual areas. These findings highlight the differential vulnerability of brain regions to distinct types of chronic hypoxia.
Exploring double low-dose CT technology for achieving coronary mixed reality radiation dose reduction: a preliminary study in coronary artery disease patients
Background: Coronary artery disease (CAD) is a leading global health concern; however, coronary computed tomography (CT) angiography (CCTA) is constrained by two-dimensional interpretation. Mixed reality enhances three-dimensional (3D) visualisation and interventional guidance, but conventional-dose CT acquisition raises concerns about radiation exposure. It is unknown whether double low-dose CT can generate adequate digital imaging and communications in medicine (DICOM) data for coronary mixed reality; we therefore evaluated its feasibility, dose reduction, diagnostic performance, and clinical utility. Methods: A total of 192 consecutive patients with suspected CAD were prospectively enrolled and randomised to the double low-dose mixed reality group (Group A), the conventional-dose mixed reality group (Group B), or the conventional-dose CT group (Group C). Radiation dose, contrast media dose, and coronary mixed reality quality were compared between Group A and Group B. With coronary angiography (CAG) as the reference standard, receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of coronary mixed reality in detecting coronary stenosis. Perioperative outcomes of coronary mixed reality-assisted CAG were recorded and compared. Results: Compared with Group B, Group A reduced the effective radiation dose by approximately 82% and the contrast media dose by approximately 47%; the quality of coronary mixed reality showed no significant difference between the two groups. The artificial intelligence (AI)-assisted coronary mixed reality workflow demonstrated an area under the curve (AUC) of 0.92, a specificity of 0.90, a sensitivity of 0.94, and an accuracy of 0.92 for detecting coronary stenosis. Coronary mixed reality-assisted CAG effectively reduced the time to localise CAD, total radiation time, total radiation exposure, and contrast media dose by approximately 36%, 45%, 28%, and 40%, respectively. Conclusions: In non-obese patients [body mass index (BMI) <30 kg/m2] imaged using a specific CT platform [dual-source, sinogram-affirmed iterative reconstruction (SAFIRE)] and commercial 3D software, double low-dose CT reduced the radiation dose by 82% without compromising coronary mixed reality quality. The AI-assisted mixed reality workflow showed promising diagnostic performance (AUC =0.92); however, these findings are preliminary and require validation in broader populations and settings.
Magnetic resonance imaging R2* mapping for evaluating Ki-67 proliferation index and cytokeratin 19 expression in solitary iron-sparing hepatocellular carcinoma: a retrospective single-center study
Background: Iron may be involved in the occurrence and development of hepatocellular carcinoma (HCC), and R2* relaxometry is the preferred noninvasive method for iron quantification. However, whether hepatic and intratumoral R2* values are associated with Ki-67 proliferation index (PI) and cytokeratin 19 (CK19) expression in iron-sparing HCC remains unclear. This study aimed to determine the associations of preoperative hepatic and intratumoral R2* values with Ki-67 PI and CK19 expression in patients with solitary iron-sparing HCC. Methods: Consecutive adult patients with solitary HCC who underwent surgery at a single center between May 2018 and May 2025 were retrospectively enrolled. All patients had intratumoral iron-sparing as indicated on preoperative 3.0-T magnetic resonance imaging (MRI) R2* imaging and had complete postoperative pathological data for Ki-67 and CK19 expression. Hepatic and intratumoral R2* values were measured via region of interest (ROI) analysis. According to the cohort median, a high Ki-67 PI was defined as Ki-67 positivity in >20% of tumor cells, and CK19 positivity was defined membranous/cytoplasmic staining in ≥5% of tumor cells. Meanwhile, hepatic R2* was categorized at a cutoff of 136 s−1 and alpha-fetoprotein (AFP) at a cutoff of 400 ng/mL. Exact logistic regression identified predictors of a high Ki-67 PI and positive CK19 expression, and model performance was evaluated via the area under the receiver operating characteristic curve (AUC). Results: Eighty patients were enrolled (mean age 55.11±9.69 years; 93.75% male). A high Ki-67 PI was present in 43 (53.75%) patients, and positive CK19 expression was present in 16 (20.00%) patients. The high Ki-67 PI group, as compared to the low Ki-67 PI group, had higher AFP levels (P=0.035) and lower hepatic R2* values (P=0.038). According to multivariate exact logistic analysis, the independent predictors of a high Ki-67 PI were AFP >400 ng/mL [odds ratio (OR) =19.04; 95% confidence interval (CI): 2.98–121.46, P=0.002] and a hepatic R2* value ≥136 s−1 (OR =0.27; 95% CI: 0.10–0.75; P=0.012); the model including both of these factors yielded an AUC of 0.771 (95% CI: 0.664–0.858). The only independent predictor of positive CK19 expression was AFP >400 ng/mL (OR =8.50; 95% CI: 2.38–30.34; P=0.001), while an R2* value ≥136 s−1 was not significantly associated with positive CK19 expression (P=0.124). Conclusions: In this retrospective single-center study of patients with solitary iron-sparing HCC, it was found that a combination of hepatic R2* (≥136 s−1) and AFP (>400 ng/mL) values may serve as a biomarker for predicting a high Ki-67 PI, whereas they had limited value for predicting positive CK19 expression.
Validation of voxel-based computed tomography liver fat quantification for assessing incident type 2 diabetes risk in a health check-up population
Background: Conventional computed tomography (CT)-based liver fat assessment relies mainly on mean attenuation or liver-to-spleen ratios, whereas a novel voxel-based approach enables whole-liver, voxel-level quantification of hepatic fat. However, its value for predicting incident type 2 diabetes (T2D) remains unclear. Therefore, this study aimed to validate whether voxel-based liver fat quantification derived from routine CT can be used to assess the risk of incident T2D in a health check-up population. Methods: In this retrospective cohort study, individuals who underwent routine noncontrast abdominal CT examinations between January 2013 and December 2023 and were free of diabetes at baseline were included. Liver fat was quantified via an automated voxel-based method incorporating spleen-referenced attenuation criteria on the basis of whole-liver segmentation. The average liver fat fraction was defined as the proportion of liver voxels classified as fat. Incident T2D was identified through longitudinal review of outpatient, inpatient, and health check-up records. Associations between liver fat and incident T2D were evaluated via Cox proportional hazards models adjusted for demographic, anthropometric, and metabolic covariates. Restricted cubic spline (RCS) analysis was used to explore dose-response relationships. Predictive performance was assessed via time-dependent receiver operating characteristic (ROC) analysis at 3 and 5 years, calibration plots, and decision curve analysis (DCA). Incremental predictive value was evaluated by comparing liver fat-based models with hemoglobin A1c (HbA1c)-based and clinical models. Results: Among 356 participants [median age, 57 years, interquartile range (IQR), 53–63 years; 56.5% male], 32 individuals (9.0%) developed incident T2D during a median follow-up of 4.5 years. A higher average liver fat fraction was significantly associated with an increased risk of incident T2D after multivariable adjustment [hazard ratio (HR) per standard deviation (SD), 1.65; 95% confidence interval (CI): 1.05–2.60, P=0.031]. RCS analysis demonstrated a monotonic increase in diabetes risk with increasing liver fat fraction. The liver fat fraction showed moderate discrimination for incident T2D, with time-dependent area under the curve (tAUC) values of 0.71 (95% CI: 0.59–0.84) at 3 years and 0.72 (95% CI: 0.60–0.82) at 5 years. Compared with clinical variables or HbA1c alone, the addition of liver fat improved predictive performance for incident T2D, increasing the C-index from 0.574 (0.453–0.680) to 0.740 (95% CI: 0.663–0.803). Conclusions: Voxel-based CT quantification of liver fat is independently associated with incident T2D and provides incremental value for diabetes risk assessment in a health check-up population.
Association of epicardial fat tissue combined with left ventricular strain with left ventricular fibrosis in obese patients: a cardiac magnetic resonance study
Background: Obesity is an established independent risk factor for cardiovascular disease, and myocardial fibrosis represents a key pathological mechanism underlying obesity-related cardiac dysfunction. The early identification of fibrotic remodeling is therefore clinically important. Epicardial adipose tissue (EAT) exerts local paracrine and mechanical effects on the myocardium, and left ventricular (LV) strain derived from cardiac magnetic resonance (CMR) feature tracking enables sensitive detection of subclinical myocardial dysfunction. However, the association between EAT, LV strain, and LV fibrosis in obese patients remains unclear. This study aimed to investigate the association of EAT combined with LV strain with LV fibrosis in obese patients using CMR. Methods: A total of 80 obese patients were prospectively enrolled and divided into late gadolinium enhancement (LGE)(+) and LGE(−) groups based on the presence of myocardial fibrosis detected by CMR. They were matched with 80 healthy controls with normal body mass index (BMI). All participants underwent CMR non-contrast and contrast-enhanced scans to measure EAT volume, EAT thickness, and LV/left atrial (LA) function indices, including conventional cardiac function parameters and myocardial strain parameters. Spearman correlation analysis was used to assess relationships between EAT and LV/LA strain parameters. Univariate and multivariate logistic regression were performed to explore associations between EAT, LV strain, and LV fibrosis. Receiver operating characteristic (ROC) curves were used to evaluate the discriminative ability of combined EAT and LV strain for fibrosis. Results: Overall circumferential, radial, and longitudinal strains progressively decreased across the control, LGE(−), and LGE(+) groups (all P<0.05). EAT thickness and volume in the left atrioventricular groove (LAVG) and inferior interventricular groove (IIVG) were significantly higher in the LGE(+) group than they were in the LGE(−) group and control group (all P<0.05). LAVG thickness and EAT volume showed negative correlations with LA reservoir strain, conduit strain, and radial strain (r=−0.34 to −0.26, all P<0.05), and positive correlations with longitudinal and circumferential strains (r=0.18 to 0.36, all P<0.05). Multivariate logistic regression identified EAT volume [odds ratio (OR) =1.065; 95% confidence interval (CI): 1.028–1.103, P<0.001], LAVG thickness (OR =1.386; 95% CI: 1.064–1.805; P=0.016), and longitudinal strain (OR =1.322; 95% CI: 1.062–1.646; P=0.012) as independent predictors of LV fibrosis. The combined model yielded an area under the curve (AUC) of 0.943 (95% CI: 0.874–0.985). Conclusions: CMR enables precise assessment of EAT and LV strain in obese patients. LAVG thickness, EAT volume, and longitudinal strain were independently associated with LV fibrosis and may provide useful imaging information, but external validation is required before clinical application.
Diagnostic significance of cancer antigen 125/carcinoembryonic antigen ratio combined with IOTA-ADNEX model in discriminating between primary ovarian cancer and ovarian metastases
Background: Ovarian metastases (OMs) are relatively common and exhibit unfavorable biological characteristics. The present study sought to evaluate the diagnostic value of the International Ovarian Tumor Analysis (IOTA) Assessment of Different NEoplasias in the adneXa (ADNEX) model, the cancer antigen 125 (CA125)/carcinoembryonic antigen (CEA) ratio, and their combination in distinguishing OM from primary ovarian cancer (OC). Methods: This retrospective study enrolled patients with histopathologically diagnosed OM and primary OC. OM was classified into three types based on ultrasound (US) findings. The risk of each mass was calculated using the ADNEX model. The two groups’ clinical data, US characteristics, and ADNEX model risk were recorded and compared. The area under the receiver operating characteristic (ROC) curve (AUC) was employed to evaluate the diagnostic performance. Results: OM originated most frequently from the intestinal tract (58.3%). In total, 52.8% (38/72) of OM cases were identified simultaneously with primary tumors, whereas 47.2% (34/72) were diagnosed after the primary tumor. About 22.2% (16/72) of OM cases were type A, 37.5% (27/72) were type B, and 40.3% (29/72) were type C. The ADNEX model, CA125/CEA ratio, and the combined CA125/CEA ratio of ADNEX were compared between the two groups. The diagnostic performance of the ADNEX combined CA125/CEA ratio was significantly superior to that of the model alone {AUC = 0.886 [95% confidence interval (CI): 0.836–0.936], sensitivity =0.736 (95% CI: 0.619–0.833), accuracy =0.843 (95% CI: 0.788–0.895)}. Conclusions: Both ADNEX and the CA125/CEA ratio alone can effectively distinguish OM from primary OC. Adding the CA125/CEA ratio to the IOTA-ADNEX model may improve its performance in differentiating between OM and primary OC.
Ensemble machine learning classifiers based on computed tomography radiomics for predicting spread through air spaces in lung adenocarcinoma: a multicenter retrospective cohort study with transcriptomic interpretation
Background: Spread through air spaces (STAS) in lung adenocarcinoma (LUAD) is associated with adverse outcomes and may have implications for surgical planning. We aimed to develop and externally validate an ensemble machine learning model integrating preoperative computed tomography (CT) radiomics and clinicoradiological (CR) features for the prediction of STAS. Methods: This multicenter retrospective study included 1,206 patients with stage I LUAD from three centers, of whom 384 had STAS-positive tumors. Patients from two centers were divided into a training set (n=675) and an internal test set (n=290), and patients from the third center formed an external validation cohort (n=241). A radiomics score (Rad-score) was constructed using least absolute shrinkage and selection operator (LASSO) regression. Six tree-based algorithms and three ensemble strategies were evaluated using the Rad-score and CR features. The fixed Rad-score formula and threshold derived from the training set were transferred to a public radiogenomic cohort with matched CT images, whole-slide images (WSIs), and RNA sequencing data. Results: The stacking model achieved the best performance, with area under the receiver operating characteristic curve (AUC) values of 0.932 [95% confidence interval (CI): 0.904–0.960] in the internal test set and 0.883 (95% CI: 0.843–0.923) in the external validation cohort. SHapley Additive exPlanations (SHAP) analysis identified the Rad-score, CT density, and nodule size as the most influential predictors of model-predicted STAS risk. In the radiogenomic cohort, the transferred Rad-score classification was concordant with pathologic STAS status in 19 of 24 cases (Fisher’s exact test, P=0.0056). Radiomics-predicted high-risk tumors showed transcriptomic alterations related to cell junction assembly, cell adhesion molecule (CAM) pathways, MYC targets, and glycolysis. Conclusions: A stacking ensemble model combining CT radiomics and CR features enabled robust, noninvasive preoperative prediction of STAS in stage I LUAD. Exploratory radiogenomic analysis suggested that the imaging-derived high-risk signature was associated with decreased cellular adhesion and metabolic reprogramming.
Ultrasound evaluation of the anatomy and hemodynamics of the Giacomini vein
Background: Ultrasound characteristics of the Giacomini vein (GV) in Chinese populations remain poorly defined. The aim of this study was to analyze the anatomical and hemodynamic characteristics of the GV using color Doppler ultrasound and to provide evidence to support clinical diagnosis and treatment. Methods: Prospectively, 121 patients with lower extremity discomfort and varicose veins were continuously collected from July 2025 to January 2026, involving a total of 238 limbs. Patients were categorized into a primary group (C0) and a varicose group (C1–C4) according to the CEAP (Clinical, Etiological, Anatomical, and Pathophysiological) classification. All patients underwent ultrasound examination, and 38 cases underwent digital subtraction angiography (DSA). The GV was classified into four types. Agreement between ultrasound and DSA findings was assessed using Kappa statistics. Differences in GV detection rate, inner diameter, and reflux-related parameters were compared between groups, among GV types, and according to the severity of saphenous vein reflux. Results: Ultrasound and DSA demonstrated high concordance in GV classification (kappa =0.854). The overall GV detection rate was 73.95%, with a significantly higher rate in the right lower limb than in the left lower limb (P<0.05) and no significant difference between sexes. The great saphenous vein (GSV) confluence type was the most prevalent (73.3%). Detection rates of GV presence and reflux were significantly higher in the varicose group than in the primary group (P<0.05), whereas no significant difference in inner diameter was observed. Reflux detection rates showed an increasing trend across varicose subgroups; however, this trend did not reach statistical significance. The incidence of GV reflux was significantly higher in the small saphenous vein (SSV) reflux group and in the group with both GSV and SSV reflux than in the GSV reflux group (P<0.05). The severity of saphenous vein reflux had no significant effect on GV-related parameters. Conclusions: Ultrasound demonstrated significant clinical utility in the assessment of the GV. GV reflux was closely associated with the severity of lower extremity varicose veins and with the type of saphenous vein reflux. Prioritization of preoperative GV assessment may reduce postoperative recurrence rates and minimize intraoperative injury.
Potential utilization of lymphoscintigraphy in patients with kaposiform hemangioendothelioma
Background: Kaposiform hemangioendothelioma (KHE) is characterized by the dysregulation of both angiogenic and lymphangiogenic pathways. The application of lymphoscintigraphy in the management of KHE remains poorly documented. This study aims to delineate the lymphoscintigraphic imaging features of KHE and assess the potential utility of this modality in clinical management. Methods: In this retrospective study, we evaluated lymphoscintigraphic features in patients with KHE. The assessed parameters included the extent of dermal backflow, flow patterns, lymphatic stasis, regional lymph node visualization, and collateral vessel formation. Findings were classified into three grades of obstruction severity. In patients with follow-up scans, temporal changes from baseline were systematically characterized. Results: Twenty-five patients (15 male, 10 female) with KHE underwent lymphoscintigraphy, comprising 24 with extremity and one with truncal involvement. Abnormal findings were detected in 22 of the 24 extremity cases. In all abnormal cases, lymphedema was unilateral and ipsilateral to the lesion. Lymphatic stasis within the involved lesions was a universal finding (100%, 22/22). Regarding flow patterns, 68% (15/22) of these patients demonstrated a restricted pattern, while the remaining 32% (7/22) exhibited a trunk flow pattern. Among the six patients who underwent follow-up lymphoscintigraphy, five showed no significant improvement in lymphatic drainage. However, one patient, who initially presented with partial obstruction, achieved complete resolution with normal drainage on subsequent imaging. Conclusions: Lymphoscintigraphy effectively characterizes lymphatic dysfunction in KHE, aiding in both initial diagnosis and the long-term management of secondary lymphedema.
AFFUNet: adaptive feature fusion Transformer U-Net with joint loss function for medical image segmentation
Background: High-precision medical image segmentation is crucial for enabling computer-aided accurate diagnosis and personalized treatment planning. Although Transformer-based methods have shown promising results, they still face significant challenges. These include a fixed feature fusion strategy that cannot adapt to dynamic changes in feature importance, the standard Dice loss that treats all pixels equally and struggles to focus on difficult samples, and inadequate boundary precision optimization, which limits the clinical applicability of segmentation results. To address these challenges, this study proposes Adaptive Feature Fusion U-Net (AFFUNet), an adaptive feature fusion (AFF) Transformer U-Net with a joint loss function, aiming to improve segmentation accuracy through dynamic feature fusion, hard sample reweighting, and explicit boundary optimization. Methods: Based on the Cross-Shaped Window Transformer UNet (CSWin-UNet) architecture, we propose three modifications to address the challenges discussed above: (I) an AFF module that replaces standard skip connection concatenation to dynamically learn fusion weights for encoder and decoder features; (II) focal Dice loss, which adjusts the Dice loss based on prediction confidence to better prioritize difficult samples; (III) boundary-aware loss, which explicitly enhances boundary precision through a gradient-based boundary extraction technique. Results: We evaluated our method on two benchmark datasets: the Synapse multi-organ computed tomography (CT) dataset and the Automated Cardiac Diagnosis Challenge (ACDC) cardiac magnetic resonance imaging (MRI) dataset. On the Synapse dataset, our method achieved an average Dice coefficient of 81.21% and an average 95th percentile Hausdorff distance (HD95) of 18.41 mm, representing a 0.09% improvement in Dice and a 0.45 mm reduction in HD95 compared to the state-of-the-art CSWin-UNet. On the ACDC dataset, our method achieved a mean Dice of 89.77%, consistently outperforming CSWin-UNet (88.44%). Ablation studies confirmed that each component contributes to the overall performance, particularly in small organs and boundary regions. Notably, the added AFF module introduces negligible computational overhead: compared to CSWin-UNet, the parameters and floating point operations per second (FLOPs) remained virtually unchanged (−0.02 M, −0.036 G). Thus, our method achieves improved segmentation accuracy while maintaining practical efficiency. Conclusions: This work integrates AFF, focal Dice loss, and boundary-aware loss into a collaborative framework that works across the feature, objective, and boundary levels. The resulting end-to-end mechanism achieves improved performance on standard benchmarks while maintaining practical efficiency, warranting further evaluation in clinical settings.
A radiomics-based machine learning model for the preoperative differentiation of lung adenocarcinoma subtypes
Background: The preoperative differentiation of lung adenocarcinoma subtypes is critical for implementing personalized treatment but is difficult to accomplish with conventional imaging. This study aimed to develop an interpretable multimodal model integrating clinical, peritumoral, radiomic, and deep learning features to improve diagnostic accuracy. Methods: A total of 3,038 patients from four hospitals were divided into training (n=1,822), test (n=608), and validation (n=608) sets. Two radiologists manually segmented two-dimensional tumor regions on computed tomography using ITK-SNAP software. After Pearson correlation analysis and least absolute shrinkage and selection operator regression, the radiomic score and deep learning score were generated. Clinical features were selected via univariate analysis, the Boruta algorithm, and recursive feature elimination (RFE). Individual logistic models were built and fused with the optimal combination selected via support vector machine-synthetic minority oversampling technique and extreme gradient boosting. Performance was evaluated in terms of the Obuchowski index, accuracy, F1-score, calibration, and decision curves, while interpretability was assessed via Shapley additive explanations (SHAP) and individual conditional expectation (ICE). Results: The fused model achieved Obuchowski indices of 0.85 [95% confidence interval (CI): 0.84–0.87], 0.81 (95% CI: 0.78–0.83), and 0.79 (95% CI: 0.76–0.81) in the training, test, and validation sets, respectively outperforming the single-modality models. The F1-scores for the lepidic, acinar/papillary, and solid/micropapillary subtypes, respectively, were 0.77, 0.61, and 0.63 in the training set; 0.72, 0.57, and 0.59 in the test set; and 0.74, 0.54, and 0.52 in the validation set. Calibration and decision curve analysis confirmed the robustness and clinical utility of the model. SHAP analysis identified ResNet-101 feature as the best predictor, followed by peritumoral radiomic score, and lobulation. ICE plots revealed the linear and monotonic relationships between key features and predicted probabilities across subtypes. Conclusions: The radiomics model developed in this study facilitates the accurate and interpretable preoperative classification of lung adenocarcinoma subtypes. Fusion of clinical, peritumoral, and deep learning features enhances diagnostic performance and supports clinical decision-making.
Multichannel deep learning-based MRI model for predicting breast cancer axillary lymph node invasion: a comparative study to Node-RADS
Background: The accurate evaluation of axillary lymph node (ALN) status before surgery is critical to formulate appropriate surgical strategies and determine whether axillary lymph node dissection (ALND) should be performed in breast cancer (BC) patients. This study aimed to develop a multichannel multiscale deep learning (DL) model for the preoperative prediction of ALN metastasis for BC patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to compare its performance with that of the Node Reporting and Data System (Node-RADS). Methods: A total of 735 patients were included in this two-center retrospective study. A DL framework integrating features from axial, sagittal, coronal, and multiplanar reformat DCE-MRI images was constructed. ResNet101 served as the feature extractor, followed by a transformer-based fusion module. Model interpretability was enhanced with gradient-weighted class activation mapping (Grad-CAM) and SHapley additive exPlanations (SHAP). Diagnostic performance was evaluated using the area under the curve (AUC), sensitivity, and specificity, and compared with the Node-RADS in an external validation cohort (n=148). Results: The proposed model achieved promising results with AUC values of 0.959 [95% confidence interval (CI): 0.942–0.976] in the training cohort, 0.885 (95% CI: 0.839–0.931) in the internal validation cohort, and 0.908 (95% CI: 0.862–0.954) in the external validation cohort. The predictive ability of the model remained stable across diverse patient subgroups stratified by age, tumor size, and Breast Imaging Reporting and Data System (BI-RADS) category. In the external validation cohort, the model demonstrated diagnostic accuracy comparable to that of the Node-RADS (AUC: 0.908 vs. 0.909, P=0.971), with no significant differences in sensitivity, specificity, or accuracy (all P>0.05). Agreement between the two approaches was moderate (kappa =0.523). Conclusions: The developed DL model provides accurate and robust preoperative prediction of ALN metastasis and its performance was comparable to that of the Node-RADS. It could serve as a clinical decision-support tool to reduce unnecessary ALN dissections.
Amide proton transfer-weighted magnetic resonance imaging for the diagnosis of triple-negative breast cancer: a comparative study with diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging
Background: Amide proton transfer-weighted (APTw) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that enables the noninvasive quantification of endogenous mobile proteins and peptides. This study aimed to preliminarily evaluate the diagnostic performance of multipool chemical exchange saturation transfer (CEST)-derived parameters for differentiating benign from malignant breast lesions and identifying triple-negative breast cancer (TNBC) and to compare this modality with diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI). Methods: This prospective single-center study included 148 patients with breast lesions who underwent 3 T APTw, DWI, and DCE-MRI examinations. Pathological results were used to classify lesions as benign or malignant and to determine molecular subtypes based on immunohistochemistry. Twelve CEST-derived metabolic parameters, the apparent diffusion coefficient (ADC), and DCE-MRI parameters [volume transfer constant (Ktrans), rate constant (Kep), and extracellular extravascular volume fraction (Ve)] were compared between benign and malignant lesions, as well as between TNBC and non-TNBC subtypes. Diagnostic performance was assessed via the receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations. Results: A total of 148 breast lesions were included in the analysis, among which 43 were benign and 105 malignant [luminal A: n=26; luminal B: n=31; human epidermal growth factor receptor 2 (HER2)-enriched: n=16; TNBC: n=32]. Among the 12 metabolic parameters from APTw imaging, only amide values of the malignant group were significantly higher than those of the benign group (P=0.002). Malignant lesions also exhibited lower ADC values and higher Ktrans and Kep values (P<0.05). The combination of amide, ADC, Ktrans, and Kep yielded an AUC of 0.932 [95% confidence interval (CI): 0.891–0.968]. For differentiating TNBC from non-TNBC, the CEST-derived parameter downfield nuclear Overhauser effect-suppressed apparent exchange-dependent relaxation (DNS-AREX) showed significant differences, achieving an AUC of 0.762 (95% CI: 0.665–0.842), with a sensitivity of 91.7% and a specificity of 68.5%. No significant differences were observed for DWI or DCE-MRI parameters between TNBC and non-TNBC. Conclusions: Multipool CEST provides complementary value to conventional MRI for breast lesion characterization. The CEST-derived parameter DNS-AREX demonstrated a modest but statistically significant ability to identify TNBC, while conventional DWI and DCE-MRI parameters did not exhibit significant predictive ability. APTw imaging may serve as a noninvasive imaging biomarker for molecular subtyping in breast cancer.
Non-invasive risk stratification of immunoglobulin A nephropathy and crescent formation using contrast-enhanced ultrasound combined with serological markers
Background: Immunoglobulin A nephropathy (IgAN) has heterogeneous clinical and pathological manifestations. Renal biopsy is the invasive diagnostic standard, and crescents mark active glomerular injury. Quantitative contrast-enhanced ultrasound (CEUS) can characterize renal microvascular perfusion. We aimed to develop a non-invasive nomogram integrating quantitative CEUS parameters and routine serological biomarkers to diagnose IgAN and predict crescent formation. Methods: In this retrospective single-center study, 184 patients with chronic kidney disease (CKD) underwent CEUS within 7 days before renal biopsy. Patients were classified into IgAN (n=94) and non-IgAN (n=90) groups, and the IgAN cohort was further stratified by crescent status (n=47 in each subgroup). Quantitative time-intensity curve (TIC) parameters were extracted from regions of interest (ROIs) using VueBox 7.0 software. Elliptical ROIs of approximately 4 mm × 3 mm were placed in the mid renal cortex and medulla closest to the probe to generate cortical and medullary TICs. Cortex-to-medulla normalization was applied. The analyzed parameters included peak enhancement (PE), wash-in rate (WiR), and wash-in perfusion index (WiP). Logistic regression models were constructed for IgAN diagnosis, crescent prediction in IgAN, and crescent prediction in the entire cohort. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate discrimination, calibration, and clinical net benefit. DeLong tests compared the combined model (CEUS parameters plus serological indicators), clinical-only model, and CEUS-only model to assess the incremental diagnostic value of CEUS. Subgroup analysis based on estimated glomerular filtration rate (eGFR) was performed for crescent prediction in IgAN to examine the influence of renal function on model performance. Results: IgAN patients showed significantly altered renal perfusion characteristics compared with non-IgAN patients. The combined model incorporating age, eGFR, urine albumin-to-creatinine ratio (UACR), and WiR achieved moderate discrimination for IgAN diagnosis, with an area under the curve (AUC) of 0.78 [95% confidence interval (CI): 0.72–0.85]. For crescent prediction in IgAN, the combined model based on age and WiP achieved an AUC of 0.74 (95% CI: 0.64–0.85). For crescent prediction in the entire cohort, the combined model based on age and rise time (RT) showed an AUC of 0.72 (95% CI: 0.64–0.81). DeLong tests showed that the combined models outperformed the corresponding clinical-only and CEUS-only models (all P<0.05), supporting the incremental but moderate diagnostic value of CEUS. Subgroup analysis showed stronger predictive performance in patients with preserved renal function. Conclusions: CEUS-derived microvascular information, when combined with clinical biomarkers, provides incremental diagnostic information and may assist pre-biopsy risk stratification and crescent formation prediction in CKD. These findings should be interpreted as exploratory and require external validation before routine clinical implementation.
Local pressure as a dominant hemodynamic driver of wall enhancement in anterior communicating artery aneurysms: a facet-level computational fluid dynamics and vessel wall imaging analysis
Background: Size-based risk stratification often overlooks small but unstable intracranial aneurysms (IAs). Aneurysm wall enhancement (AWE) on vessel wall imaging (VWI) is a validated marker of wall instability, yet the local hemodynamic drivers of this pathology, particularly in complex anterior communicating artery (ACoA) aneurysms, remain incompletely characterized. This study leverages a combined computational fluid dynamics (CFD)-VWI approach to characterize the mechanobiological coupling between local hemodynamics and quantitative wall remodeling in ACoA aneurysms. Methods: We retrospectively analyzed 24 patients harboring 25 ACoA aneurysms. A Vector-Integrated Surface Parametrization (VISP) pipeline achieved sub-voxel sampling density [through adaptive interpolation rather than imaging resolution beyond the native 0.6 mm magnetic resonance imaging (MRI) voxel] for co-registration of CFD and 3T-VWI, with wall enhancement defined at a contrast ratio (CR) ≥0.6. To identify hemodynamic drivers of enhanced wall thickness (EWT) while explicitly accounting for within-patient hierarchical clustering, four complementary analytical frameworks were applied in parallel: (I) intra-patient paired bootstrap tests (2,000 resamples) comparing enhanced and non-enhanced wall segments within each of the 13 AWE-positive patients; (II) a multivariate linear mixed model (LMM) with patient-level random intercepts for EWT severity (n=12,473 enhanced segments); (III) generalized estimating equations (GEEs) with cluster-robust variance for AWE presence (n=157,284 segments); and (IV) ensemble machine-learning models (Random Forest and XGBoost) interpreted via Shapley Additive exPlanations (SHAP) values across segment-level, patient-centered, and patient-level GroupKFold cross-validation (CV). Cross-patient generalization of EWT prediction was disclosed separately as an out-of-sample analysis. Results: Focal AWE was identified in 14 of 25 aneurysms (13 patients), spatially coinciding with hemodynamic stagnation zones. Enhanced segments exhibited significantly lower local Pressurepeak (∆ =−35.25 Pa, PFDR =0.02) and wall shear stress (WSS)peak (∆ =−3.97 Pa, PFDR <0.001) compared to non-enhanced segments under intra-patient paired bootstrap testing. Three further frameworks converged on Pressurepeak as the dominant independent driver of wall thickness among enhanced segments: multivariate LMM β=−0.181 (P=2.31×10−11); GEE β=−0.5452 (robust P=0.0499); and a Random Forest model, in which Pressurepeak ranked first by SHAP feature importance at the segment level and remained among the top three across every CV regime. Conclusions: We present a facet-level CFD-VWI pipeline that achieves sub-voxel sampling density for spatially mapping local hemodynamics onto quantitative wall remodeling in ACoA aneurysms on a clinical 3T platform. Across four independent hierarchical analyses, local Pressurepeak consistently emerged as the dominant independent hemodynamic driver of wall thickening among enhanced segments, complementing the established low-WSS association. This framework is intended as a mechanistic explanatory tool for local hemodynamic-AWE coupling; broader clinical translation will require larger, externally validated cohorts.
Quantification of real-time maximal renal perfusion using dynamic renal imaging
Background: Although the peak value of the renal blood perfusion time–activity curve obtained by renal dynamic imaging (RDI) represents the real-time maximum renal blood perfusion (MAX-RP), it is easily affected by the radioactive activity of the injection and is difficult to quantitatively measure. This study proposes a renal blood perfusion peak ratio (RBPPR) model to eliminate injection dose differences and explore its clinical value in quantitatively evaluating MAX-RP. Methods: A retrospective analysis was conducted on 151 cases (a total of 205 kidneys) who underwent RDI examination from August 2020 to June 2025. According to the 30% limit of the peak difference rate of renal graph and the normal lower limit value of total glomerular filtration rate (GFR), the participants were divided into a control group and an experimental group (which included: the completely compensated group and the incompletely compensated group). Analysis was conducted of distribution of RBPPR and the differences among groups, its correlation with GFR, and its diagnostic efficacy. Results: RBPPR values were concentrated within each group [standard deviation (SD) ≤1.35%]. RBPPR strongly correlated with each kidney GFR (ekGFR; r=0.778, P<0.001). Across the three groups, both ekGFR (F=94.123, P<0.001) and RBPPR (F=43.336, P<0.001) differed significantly. Compared with the control group (RBPPR 3.67%±0.90%, ekGFR 49.12±9.11 mL/min/1.73 m2), the complete compensation group showed significantly higher RBPPR (5.20%±1.35%, P<0.001) and ekGFR (76.00±17.75, P<0.001), whereas the incomplete compensation group exhibited elevated ekGFR (58.12±8.50, P<0.001) but no significant RBPPR increase (3.92%±0.66%, P=0.145 vs. control). For diagnosing complete renal compensation, the optimal RBPPR threshold was 3.620%, yielding an area under the curve (AUC) of 0.842 [95% confidence interval (CI): 0.782–0.902], sensitivity 91.6%, and specificity 62.0%. Conclusions: The RBPPR model can stably and quantitatively evaluate MAX-RP with good accuracy. It provides a simple and reliable new quantitative method for evaluating whether renal function can be completely compensated and the assessment of renal reserve function.
Improving the preoperative diagnosis of invasive lung adenocarcinoma by combining multiplanar volume rendering (MPVR)-based solid component measurement with pathological analysis
Background: The early diagnosis of lung adenocarcinoma (LUAD) remains challenging. Multiplanar volume rendering (MPVR) has demonstrated good diagnostic performance; however, misclassification can still occur. This study investigated the pathological basis of MPVR misclassification and developed a model to improve the preoperative diagnosis of invasive adenocarcinoma (IAC). Methods: A total of 799 patients with LUAD were retrospectively included in the study. Based on computed tomography (CT) appearance (part-solid nodule vs. pure ground-glass nodule) and pathological diagnosis, the patients were classified into four groups: the false-negative group (n=96), the true-negative group (n=122), the false-positive group (n=132), and true-positive group (n=449). Clinicoradiological and pathological characteristics were compared among groups. Multivariate logistic regression was used to identify independent predictors of IAC and develop a combined prediction model, which was evaluated using receiver operating characteristic curve analysis. Results: Compared with the patients in the false-positive group, those in the true-positive group were older (58.0 vs. 49.0 years, P<0.001) and had larger MPVR-measured solid component diameters (8.2 vs. 4.9 mm, P<0.001). The patients in the false-negative group were also older than those in the true-negative group (P=0.008), and both the CT- and MPVR-measured solid component diameters differed significantly between these two groups (both P<0.05). Histopathological analysis showed that false-positive findings were primarily associated with dense tumor structures and fibrous septal thickening, while false-negative findings were predominantly observed in minimally invasive adenocarcinoma (MIA) or well-differentiated IAC with invasive components <2 mm. The combined model achieved area under the curve values of 0.816 [95% confidence interval (CI): 0.781–0.851] and 0.778 (95% CI: 0.712–0.844) in the training and test sets, respectively. Conclusions: MPVR-measured solid components provide valuable information for distinguishing IAC from adenocarcinoma in situ and MIA. Integrating clinical characteristics and CT imaging features further improves the preoperative diagnosis of IAC and may facilitate individualized clinical decision-making.
DCE-MRI study on regional and Fazekas-stratified heterogeneity of blood-brain barrier leakage in white matter hyperintensities and related risk factors
Background: Blood-brain barrier (BBB) disruption is a key pathological mechanism of cerebral small vessel disease (CSVD). White matter hyperintensities (WMH), a major imaging feature of this disease, show unclear regional and severity-related differences in BBB leakage, along with uncertain independent risk factors. Conventional understanding that leakage increases steadily with lesion severity has not been validated with refined stratification. This study aimed to characterize the heterogeneous patterns of BBB leakage in periventricular (PWMH) and deep WMH (DWMH) stratified by Fazekas grade and identify their independent risk factors using quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters. Methods: This single-center retrospective study included 75 patients with sporadic CSVD who underwent 3.0-tesla (3.0T) DCE-MRI. WMH were classified into PWMH and DWMH locations and graded by the Fazekas scale (0/1, 2, 3). The volume transfer constant (Ktrans) and plasma volume fraction (Vp) were derived from the Extended Tofts two-compartment model to assess BBB permeability and local perfusion, respectively. Group comparisons were performed using Mann-Whitney U tests, correlations were evaluated with Spearman rank analysis, and independent predictors of Ktrans were identified via stepwise multiple linear regression, with a significance threshold of α=0.05 (two-sided). Results: Ktrans was significantly higher in Fazekas grade 2 PWMH than in grade 0/1 (P=0.0014) and significantly lower in grade 3 (P<0.0001). For DWMH, Ktrans was significantly higher in grade 2 (P=0.037) and significantly lower in grade 3 (P=0.0002) relative to grade 0/1. PWMH showed significantly higher Ktrans than DWMH at identical grade 0/1 and grade 2 (both P<0.05), but not at grade 3 (P>0.05). Vp declined progressively with increasing Fazekas grade: in PWMH, Vp was significantly lower in both grade 2 (P=0.027) and grade 3 (P<0.0001) versus grade 0/1; in DWMH, only grade 3 showed significantly lower Vp than grade 0/1 (P=0.021). Spearman correlation analysis revealed that Ktrans was positively correlated with Vp in most subgroups (PWMH grade 0/1: r=0.444, P=0.014; DWMH grade 0/1: r=0.288, P=0.043), except for PWMH grade 3 (r=0.274, P=0.367). Multiple linear regression (Fazekas grades 0–2) showed that age [β=−0.526, 95% confidence interval (CI): −0.944 to −0.108, P=0.015], Vp (β=0.870, 95% CI: 0.031–1.709, P=0.043), and Fazekas grade 2 classification (β=20.970, 95% CI: 12.01–29.94, P<0.0001) were independent predictors of Ktrans in PWMH. For DWMH, type 2 diabetes mellitus (β=9.770, 95% CI: 3.870–15.670, P=0.002) and Fazekas grade 2 classification (β=6.845, 95% CI: 0.977–12.710, P=0.023) were independent positive predictors of Ktrans. Conclusions: BBB leakage in WMH presents clear regional and severity-dependent heterogeneity. Moderate lesions represent the active stage of barrier disruption. Reduced leakage values in severe lesions may mainly stem from diminished local perfusion restricting contrast agent delivery instead of restored barrier function. Periventricular and deep lesions are regulated by distinct risk factors. These results support refined risk evaluation and targeted intervention for CSVD.
High-frequency ultrasound semi-quantitative assessment of cartilage damage in rheumatoid arthritis using the outcome measures in rheumatology scoring system
Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by progressive joint destruction, including cartilage damage. High-frequency ultrasound (US) enables direct visualization of cartilage in small hand joints. This study aimed to characterize the distribution and burden of US-detected cartilage damage in patients with RA using the Outcome Measures in Rheumatology (OMERACT) scoring system and to compare these findings with those from patients with hand osteoarthritis (OA) and healthy controls (HCs). Methods: In this single-center cross-sectional study, 129 patients with RA, 36 patients with hand OA, and 59 HCs were enrolled. The second and third metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints of both hands were examined using a 24-MHz linear probe. Cartilage damage was semi-quantitatively scored using the OMERACT 0–2 grading system, and cartilage thickness was measured quantitatively. Patient-level count outcomes were analyzed using negative binomial regression, maximum cartilage grade using ordinal logistic regression, and joint-level cartilage damage using logistic generalized estimating equations to account for clustering within patients. Spearman correlation was used to assess associations between cartilage scores and clinical and sonographic parameters. Results: A total of 1,792 joints were analyzed. Cartilage damage (grade ≥1) was observed in 46.5% of RA patients, 38.9% of OA patients, and 11.9% of HCs. Severe cartilage damage (grade 2) was observed in 10.1% of RA patients and 11.1% of OA patients, but in none of the HCs. Compared with OA, RA patients showed significantly higher MCP total cartilage scores [incidence rate ratio (IRR) =2.17; 95% confidence interval (CI): 1.12–4.20; P=0.021], a greater number of affected joints (IRR =1.92; 95% CI: 1.08–3.43; P=0.027), and higher overall cartilage grade scores (IRR =1.76; 95% CI: 1.01–3.06; P=0.046). At the joint level, RA was associated with higher odds of cartilage damage than OA after adjustment and clustering correction [odds ratio (OR) =1.91; 95% CI: 1.03–3.57; P=0.041], mainly driven by MCP involvement (OR =2.85; 95% CI: 1.27–6.39; P=0.011). Cartilage damage in RA predominantly involved the right MCP2 and MCP3 joints, whereas OA showed a more uniform distribution with relatively greater PIP3 involvement. MCP and PIP thickness did not differ between RA and OA (P=0.327 and P=0.861, respectively), but both were significantly lower in RA than in HCs (both P<0.001). Established RA showed a higher prevalence of cartilage damage than early RA (62.5% vs. 39.3%, P=0.024). Conclusions: High-frequency US using the OMERACT system demonstrated that RA is characterized by a higher overall cartilage damage burden and a distinct MCP-predominant distribution pattern, rather than greater maximum single-joint severity. US semi-quantitative cartilage grading may support structural characterization of RA in comparison with hand OA and HCs, but longitudinal studies are needed to determine its prognostic and monitoring value.
Beyond plaque morphology: a multimodal imaging model combining structural vulnerability and functional reserve predicts stroke recurrence in symptomatic intracranial atherosclerosis
Background: Symptomatic intracranial atherosclerotic disease (ICAD) is associated with a high risk of recurrent ischemic stroke, and improved risk stratification approaches are needed. The aim of this study was to develop a multimodal imaging model integrating high-resolution vessel wall imaging (HR-VWI)-derived plaque vulnerability with the cerebral collateral cascade (CCC) framework to predict long-term stroke recurrence in ICAD. Methods: In this retrospective cohort study, patients with anterior-circulation ICAD underwent computed tomography angiography (CTA), CT perfusion (CTP), and HR-VWI. CCC status was assessed across arterial collaterals, tissue perfusion, and venous outflow (CCC+, CCCmixed, CCC−). HR-VWI metrics including plaque burden and enhancement ratio were quantified. Independent predictors were identified using Firth-corrected Cox models. The performance of single-modality (plaque-only or CCC-only) and integrated multimodal prediction models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and DeLong’s test. Results: Among 89 patients (median follow-up 30.0 months), 20 experienced recurrent stroke. The recurrence group demonstrated significantly higher plaque burden, enhancement ratio, and hypoperfusion volume (all P<0.05). Recurrence rate was higher in the CCC− group (64.3%) than in the CCC+ group (5.3%, P<0.001). Multivariable analysis identified plaque enhancement ratio [adjusted hazard ratio (aHR) =2.52, P=0.005], plaque burden (aHR =1.07, P=0.013), and CCC− profile (aHR =3.39, P=0.005) as independent predictors. The multimodal model integrating these predictors demonstrated improved discriminative performance [area under the ROC curve (AUC) =0.829], outperforming the best plaque-only model (AUC =0.735, P<0.05). Conclusions: This multimodal model synergistically combines structural plaque vulnerability and functional hemodynamic reserve, providing a potentially useful tool for stratifying long-term recurrence risk in symptomatic ICAD.
A curriculum-guided unified framework for robust unsupervised domain adaptation on multi-cohort Parkinson’s disease diagnosis
Background: Current artificial intelligence (AI) models for Parkinson’s disease (PD) diagnosis via magnetic resonance imaging (MRI) are significantly impeded by domain shift, and performance often decreases due to heterogeneity in imaging protocols and scanners across hospitals. While existing unsupervised domain adaptation (UDA) methods combining self-training and adversarial training enhance model generalizability, their dependence on pseudo-labels often introduces confirmation bias from noisy predictions. This study aimed to mitigate pseudo-label noise, enhance domain-invariant feature learning and develop a novel UDA framework for robust cross-center PD diagnosis. Methods: We propose a curriculum-guided unified UDA (CGU-UDA) framework that integrates self-training and adversarial training. Its core innovation is a feedback loop between adaptive pseudo-label refinement and contextual feature regularization. First, a curriculum learning scheduler dynamically adjusts confidence thresholds per class based on real-time learning progress and progressively filters high-quality pseudo-labels. Second, a consistency constraint module enforces prediction agreement between original target images and their randomly masked image and leverages these refined labels to promote robust, context-aware feature learning. Finally, an adversarial domain discriminator conditioned on a randomized multilinear map is applied to align feature distributions across domains. Results: Evaluations on two independent multi-cohort PD MRI datasets show that CGU-UDA consistently surpasses leading UDA benchmarks. It achieves an average increase of over 2% in classification accuracy and over 3% in area under the curve (AUC) across varied cross-domain tasks. On Hospital→Parkinson’s Progression Markers Initiative (PPMI), accuracy improved from 65.81% of baseline to 67.95%, AUC from 0.6474 to 0.7148; on PPMI→Hospital, accuracy improved from 63.83% to 65.96%, AUC from 0.6324 to 0.6687. Ablation studies confirm that both the dynamic thresholding mechanism and the masked consistency constraint are crucial to this performance gain. Conclusions: This work advances robust medical AI model by directly tackling the pseudo-label noise problem in domain adaptation. The CGU-UDA framework demonstrates strong potential for deploying reliable diagnostic models across diverse clinical settings and leading to effective clinical application.
3D reconstruction of the target tooth occlusal surface from a single-view image for root canal surgical navigation
Background: In robotic endodontic navigation, preoperative cone-beam computed tomography (CBCT) provides root canal information, whereas intraoperative vision usually provides only single-view tooth surface images. This study aimed to reconstruct the target tooth occlusal surface from a single visible-light image as a geometric interface for CBCT registration and root canal information mapping. Methods: A statistical shape model (SSM) was constructed from 50 homologous tooth intraoral scan (IOS) models using curvature-adaptive non-rigid iterative closest point (NICP) registration. For each single-view image, the target tooth region was segmented, illumination interference was suppressed, and fissure features were enhanced. The extracted texture cues were converted into a bounded pseudo-height field and injected into the statistical template under top-surface gating and vertex stability weighting. Results: On the occlusal region of interest (ROI), the proposed method achieved a root mean square distance (RMSD) of 0.432 mm, average symmetric surface distance (ASSD) of 0.308 mm, Hausdorff distance (HD) of 2.540 mm, Chamfer distance (CD) of 0.466 mm2, and Dice similarity coefficient (DSC) of 0.340. It outperformed the selected single-view comparison method in surface error metrics. Ablation experiments further showed that illumination normalization, stability weighting, and accurate segmentation contributed to improved reconstruction stability and detail recovery. Conclusions: The proposed method reconstructs an anatomically constrained occlusal surface from a single-view image and provides a practical geometric interface for subsequent CBCT registration and root canal information mapping. It does not directly reconstruct the root canal anatomy or replace CBCT-based canal identification. Current validation remains limited to dental models, limited sample diversity, and intact or nearly intact occlusal morphology; altered clinical crowns require further evaluation.
Endometrial elasticity in infertile women assessed by transvaginal shear wave elastography
Background: Endometrial receptivity (ER) is critical for embryo implantation, and its impairment is a major contributor to infertility. Current ER evaluation methods are invasive and poorly standardized. Shear wave elastography (SWE) offers a noninvasive means of quantifying tissue stiffness, but its utility in ER assessment is underexplored. This study aimed to investigate the differences in endometrial elasticity between infertile patients and healthy controls across the menstrual cycle using transvaginal SWE. Methods: This prospective study was conducted at The Third Affiliated Hospital of Guangzhou Medical University between March and September 2023. Infertile patients and healthy women of reproductive age were enrolled. Transvaginal ultrasound SWE was used to assess endometrial thickness and stiffness parameters, including mean (Emean), maximum (Emax), and minimum (Emin) elastic modulus values during the proliferative and secretory phases of the menstrual cycle. Results: A total of 169 participants were included, comprising 114 infertile patients and 55 healthy controls aged 28–35 years. No significant differences were observed between the groups in endometrial thickness or pattern distribution during either the proliferative or secretory phase (all P>0.05). During the proliferative phase, infertile patients showed significantly higher Emean and Emax values than healthy controls in both the anterior and posterior walls (anterior Emean: 32.26 vs. 23.06 kPa, P<0.001; anterior Emax: 63.47 vs. 41.47 kPa, P<0.001; posterior Emean: 34.91 vs. 26.77 kPa, P = 0.002; posterior Emax: 62.01 vs. 46.65 kPa, P=0.021). During the secretory phase, infertile patients exhibited significantly higher Emean values in both walls (anterior: 20.18 vs. 16.10 kPa, P=0.019; posterior: 21.23 vs. 16.00 kPa, P=0.006). Receiver operating characteristic (ROC) analysis indicated good discriminative performance for anterior wall Emean [area under the curve (AUC) =0.775] and Emax (AUC =0.784). Conclusions: Infertile patients exhibit increased endometrial stiffness, particularly during the proliferative phase, and transvaginal SWE—especially anterior wall Emean and Emax—may serve as a noninvasive tool to assess ER.
Construction of a radiomics-based diagnostic nomogram for patellofemoral osteoarthritis—using lateral knee X-ray images from a South China population
Background: Patellofemoral osteoarthritis (PFOA) is a common cause of anterior knee pain but is frequently overlooked on routine radiographic assessment. This study aimed to develop and internally validate a radiomics-based nomogram for diagnosing PFOA using lateral knee radiographs combined with clinical features. Methods: This retrospective multicenter study included 1,742 patients with 2,197 knees who underwent knee radiography between July 2017 and July 2020. During screening, 13 patients (15 knees) were excluded because of poor positioning or unqualified image quality, leaving 1,729 patients (2,182 knees) for analysis. PFOA was identified on lateral radiographs according to the Framingham criteria. Radiomic features were extracted from manually delineated rectangular regions of interest (ROIs) on lateral knee radiographs. The dataset was randomly divided into training and internal test sets at a ratio of 7:3. After feature selection, logistic regression (LR), k-nearest neighbors (KNN), and random forest (RF) models were developed and compared. A radiomics-clinical nomogram was subsequently constructed and internally validated. Results: Twenty-five radiomic features were ultimately selected for model construction. In the internal test set, the LR model achieved the best performance, with an area under the curve (AUC) of 0.773. Age was identified as an independent risk factor for PFOA. The nomogram integrating radiomic features, age, and sex showed improved diagnostic performance, with an AUC of 0.842, and demonstrated good calibration and clinical utility. External test set further demonstrated acceptable diagnostic performance of the LR model, with an AUC of 0.751. Conclusions: A radiomics-based nomogram integrating lateral knee radiographs with clinical factors showed good diagnostic performance for PFOA and may serve as a practical tool for radiographic assessment.
An nnU-Net-based framework with adaptive feature representation for 3D brain tumor segmentation
Background: Accurate segmentation of glioma subregions from multimodal magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and response assessment, but remains challenging because of boundary ambiguity, heterogeneous appearance, and small enhancing tumor (ET) components. This study aimed to develop and evaluate a controlled nnU-Net v2-based framework for three-dimensional (3D) brain tumor segmentation (BraTS) by improving adaptive feature representation and boundary-aware learning while preserving the reproducibility of the self-configuring nnU-Net pipeline. Methods: We propose a conditional convolution and squeeze-and-excitation with boundary-aware learning network (CondSEB-Net), a controlled enhancement of the nnU-Net v2 framework for 3D medical image segmentation. Conditional convolution (CondConv) is introduced to improve sample-specific feature adaptation, multi-level 3D squeeze-and-excitation (SE) attention is used to recalibrate channel responses, and a boundary-aware loss based on signed distance maps (SDMs) is incorporated to strengthen contour-level supervision. The proposed method was evaluated on BraTS2020 and kidney tumor segmentation (KiTS)2019 using fixed five-fold cross-validation and compared with representative 3D segmentation baselines. Results: On BraTS2020, CondSEB-Net improved the average Dice score from 0.8525±0.0217 to 0.8659±0.0184 and reduced the average 95% Hausdorff distance (HD95) from 10.57±4.01 to 9.08±3.64 mm compared with nnU-Net v2. The average symmetric surface distance (ASSD) also decreased from 3.08±0.37 to 2.65±0.30 mm. Fold-wise paired statistical analysis showed statistically supported improvements in both region overlap and boundary-related metrics. On KiTS2019, CondSEB-Net also achieved superior or competitive performance with limited additional computational overhead. Conclusions: CondSEB-Net improves adaptive feature representation and boundary-related segmentation performance while preserving the self-configuring pipeline of nnU-Net v2. The current results provide benchmark-level evidence for the proposed framework, while external multi-center validation remains necessary before clinical deployment.
Diagnostic performance and dose reduction of virtual noncontrast imaging in pediatric computed tomography urography
Background: Computed tomography urography (CTU) is critical to diagnosis in pediatric urology, but conventional four-phase protocols entail substantial cumulative radiation exposure. Minimizing radiation while preserving diagnostic efficiency is paramount in pediatric imaging. This study aimed to evaluate the diagnostic performance of dual-source dual-energy computed tomography (DECT)-derived virtual noncontrast (VNC) images as a substitute for true noncontrast (TNC) images and to assess the feasibility of omitting the unenhanced scans in pediatric CTU. Methods: This retrospective, single-center, intraindividual paired comparative study reviewed 217 consecutive pediatric patients who underwent four-phase dual-source DECT urography between June 2022 and December 2024. The protocol included a TNC scan, dual-energy corticomedullary/arterial and nephrographic/venous phases (reconstructed from the arterial phase and venous phase, respectively), and an excretory phase. The computed tomography (CT) attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality scores were compared between VNC and TNC images across normal anatomical structures and various urological lesions (tumors, hydronephrosis, cysts, calculi, trauma, and malformations). Statistical analyses included paired the Student t-test, Wilcoxon signed-rank test with Bonferroni correction, the intraclass correlation coefficient (ICC), Bland-Altman analysis, and the Cohen kappa statistic. Radiation dose reduction was evaluated via the paired t-test. Results: VNC significantly underestimated the CT attenuation of renal calculi compared to TNC [mean difference ~330 Hounsfield units (HU)]. Subcutaneous fat attenuation was slightly higher on VNC (mean bias <2.82 HU; P<0.001). For other soft-tissue regions, VNC attenuation was slightly lower than TNC (difference <8.62 HU), showing moderate-to-excellent agreement. VNC yielded lower image noise (P<0.001) and a similar or higher CNR when compared with TNC scans. Subjective image quality scores for VNC images were ≥3 in all but one patient, satisfying diagnostic requirements. Omitting the baseline TNC phase reduced the cumulative effective radiation dose by 24.6% (from 7.98±1.71 to 6.02±1.28 mSv). Conclusions: DECT-derived VNC and conventional TNC images demonstrate comparable density resolution and overall image quality, with no significant differences in SNR, CNR, or noise between the two VNC phases (P>0.017). VNC can safely replace TNC scans for routine anatomical evaluation and lesion detection in pediatric CTU, significantly reducing the radiation dose. However, a TNC scan remains necessary for the accurate quantitative assessment of renal calculi.
Midaxillary-to-midclavicular diaphragmatic mobility ratio predicts weaning success in invasive mechanical ventilation: an observational cohort study
Background: A high proportion of patients undergoing invasive mechanical ventilation (IMV) experience weaning failure. As the primary respiratory muscle, the diaphragm rapidly atrophies under the influence of IMV, analgesics/sedatives/muscle relaxants, and nutritional depletion. The accurate assessment of diaphragmatic function is critical for predicting weaning success. Previous studies have primarily focused on diaphragmatic mobility (DM) and diaphragmatic thickening fraction (DTF), both limited by substantial inter-individual variability and regional contractile heterogeneity. This study introduces a novel parameter—the ratio of DM measured at the midaxillary line (MAL; reflecting domed portion mobility) to that at the midclavicular line (MCL; reflecting posterior wall mobility)—as a predictor of weaning outcomes, and evaluates its statistical discriminative ability. Methods: This observational cohort study enrolled patients receiving IMV for >72 hours in the intensive care unit (ICU) of our hospital between 18 September 2025 and 10 April 2026. Patients were divided into success and failure groups based on the outcome of the first weaning attempt. Clinical data including sex, age, Acute Physiology and Chronic Health Evaluation version II (APACHE II) score, and primary etiology for IMV were collected. Bedside ultrasound measurements of MAL, MCL, and the MAL/MCL ratio were performed at 24 and 72 hours after ICU admission, and during spontaneous breathing trial (SBT) (awake state, minimal sedatives/analgesics). Serum potassium (K+), prealbumin (Pa), partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2), and left ventricular ejection fraction (LVEF) were also recorded. The study involved only diagnostic data and routine samples, with no interventional procedures. Results: (I) No significant differences were observed between groups in age or APACHE II score. (II) At 24 hours and during SBT, Pa, MAL, MCL, LVEF, and K+ showed no intergroup differences, whereas the MAL/MCL ratio differed significantly. (III) At 72 hours, none of the parameters, including MAL/MCL, showed significant intergroup differences. (IV) During SBT, the area under the curve (AUC) of MAL/MCL was 0.945, with a cutoff value >1.11 yielding a specificity of 94.44%. Conclusions: As a novel diaphragmatic ultrasound parameter, the MAL/MCL ratio, which mitigates inter-individual baseline variability, demonstrated excellent discriminative performance in this single-center cohort. Clinicians may consider incorporating MAL and MCL measurements with ratio calculation during weaning assessment. However, calibration analysis indicated that its clinical utility lies primarily in identifying patients at high risk of weaning failure rather than providing precise individual risk probabilities. These findings warrant validation in larger, multicenter studies before routine clinical adoption.
Quantitative and semi-quantitative comparison of myocardial blood flow between 99mTc-tetrofosmin and 99mTc-sestamibi: based on continuous rapid dynamic imaging technology using cadmium-zinc-telluride cardiac single-photon emission computed tomography
Background: Dynamic imaging with cardiac-dedicated cadmium-zinc-telluride single-photon emission computed tomography (CZT-SPECT) enables both the absolute quantification of myocardial blood flow (MBF) and the semi-quantitative assessment of myocardial perfusion imaging (MPI). This study aimed to investigate and compare differences in quantitative and semi-quantitative outcomes between technetium-99m-tetrofosmin (99mTc-TF; TF group) and technetium-99m-sestamibi (99mTc-MIBI; MIBI group) using a continuous rapid dynamic acquisition protocol. Methods: Patients with no or mild coronary artery disease (CAD) who underwent myocardial flow reserve (MFR) assessment using a CZT-SPECT continuous rapid dynamic acquisition protocol were retrospectively enrolled in the study (n=100 per group). Quantitative perfusion parameters [rest MBF (rMBF), stress MBF (sMBF), and MFR] were compared between groups for each coronary territory and the whole left ventricle (LV). Within-group coefficients of variation (CVs) and multivariate linear regression analyses were performed. Semi-quantitative perfusion indices and left ventricular functional parameters were also compared. MPI image quality was scored for both groups. Comparisons of scores and assessments of interobserver reproducibility were performed. Results: No significant differences were observed in the baseline demographic and clinical characteristics between the TF and MIBI groups (all P>0.05). Quantitative flow analysis revealed no significant between-group differences in rMBF for the left circumflex (LCX) artery, right coronary artery (RCA), or the global LV (all P>0.05). In contrast, significant between-group differences were observed in rMBF in the left anterior descending (LAD) artery, as well as in sMBF and MFR across all coronary territories and global LV values (all values were higher in the MIBI group than in the TF group; all P<0.05). The within-group CVs were consistently lower in the TF group than in the corresponding MIBI group. Additionally, the CVs for rMBF were lower than those for sMBF and MFR in both groups across all coronary territories and global LV values. Multivariate linear regression analysis showed negative beta (β) coefficients for TF relative to MIBI. No significant differences were found in the semi-quantitative MPI perfusion indices or the left ventricular functional parameters between groups (all P>0.05). Image quality scores differed significantly in both between-group comparisons and within-group comparisons between stress and rest imaging (all P<0.05). Scores were lower in the TF group than in the MIBI group, suggesting better MPI image quality with TF. Additionally, rest image scores were lower than stress image scores in both groups. The intraclass correlation coefficients (ICCs) were excellent (0.869–0.957, all P<0.05). Conclusions: Under a continuous rapid dynamic acquisition protocol, 99mTc-TF provides conventional semi-quantitative perfusion parameters and left ventricular functional indices comparable to those obtained with 99mTc-MIBI, while offering superior MPI image quality. Differences in quantitative flow measurements were observed between the two tracers. These findings suggest that further head-to-head studies using positron emission tomography (PET) as the reference standard are warranted to establish TF-specific quantitative parameters.
Effects of convolutional neural network models on the segmentation of the interscalene brachial plexus in ultrasound imaging for radiomics
Background: The application of artificial intelligence (AI) in ultrasound (US)-guided regional anesthesia has expanded, particularly in enhancing the accuracy, safety, and training effectiveness of procedures through deep learning-based anatomical segmentation. This study aimed to develop and validate an automatic segmentation model for supraclavicular-to-interscalene brachial plexus block (ISB) using the You Only Look At CoefficienTs (YOLACT) algorithm and to compare its performance with that of a U-Net model. Methods: A total of 1,100 patients scheduled to undergo ISB were enrolled. US images encompassing the anatomical range from the supraclavicular fossa to the C7 vertebral level were acquired by experienced anesthesiologists. Images were annotated to identify the brachial plexus nerve, anterior scalene muscle (ASM), middle scalene muscle (MSM), and subclavian artery (SA). YOLACT and U-Net models were trained for automatic segmentation. Model performance was assessed using Intersection over Union (IoU), Dice similarity coefficient (DSC), Hausdorff distance (HD), the proportion of images with a brachial plexus nerve IoU > 0.5, and segmentation accuracy metrics. Results: A total of 6,600 US images were analyzed. The YOLACT model demonstrated significantly higher IoU and DSC values and lower HD values compared with the U-Net model for segmentation of the brachial plexus nerve, ASM, MSM, and SA (P<0.001). The proportion of images with a brachial plexus nerve IoU greater than 0.5 was also significantly higher with YOLACT (P<0.001). Conclusions: An automatic segmentation model for ISB, spanning the supraclavicular region to the C7 level, was developed using the YOLACT algorithm. Although quantitative performance metrics favored YOLACT over U-Net, subjective accuracy assessments were comparable between models. Further studies using larger datasets are required to clarify the potential clinical applicability of this approach.
Prognostic value of ultrasound Breast Imaging Reporting and Data System for disease-free survival in early-stage breast cancer: a retrospective study (2011–2018)
Background: The Breast Imaging Reporting and Data System (BI-RADS) offers a standardized diagnostic risk stratification framework for assessing breast lesions. However, whether the imaging phenotype captured by the preoperative ultrasound (US) BI-RADS category is associated with long-term survival in early-stage breast cancer (BC) remains unclear. The aim of this study was to evaluate the prognostic value of the preoperative US BI-RADS category and to develop a nomogram integrating this imaging marker with clinicopathological factors to predict disease-free survival (DFS). Methods: We retrospectively analyzed 681 patients with early-stage BC (2011–2018). Survival outcomes across different US BI-RADS categories were compared using Kaplan-Meier curves and log-rank tests. Associations between candidate variables and survival outcomes were evaluated using univariate and multivariate Cox regression analyses. We developed a US-based nomogram with US BI-RADS category, lymph node metastasis (LNM), and Ki-67 to predict 5- and 10-year DFS. We also built a clinical model based on standard clinicopathological factors for comparison. We assessed both models using bootstrap-corrected concordance index (C-index) and area under the receiver operating characteristic (ROC) curve (AUC), and constructed calibration curves to compare predicted with observed DFS. Results: Patients with US BI-RADS categories 3–4B showed significantly superior DFS compared to those with categories 4C–5 (P=0.004). Multivariate Cox regression analysis identified US BI-RADS categories 4C–5 and Ki-67 index ≥20% as independent predictors of less favorable DFS. The US-based nomogram demonstrated good predictive performance, with a C-index of 0.701 [95% confidence interval (CI): 0.611–0.770] and well-fitted calibration curves. The 5- and 10-year time-dependent AUCs were 0.720 (95% CI: 0.624–0.801) and 0.647 (95% CI: 0.549–0.738), respectively. Compared with the clinical model, the US-based nomogram showed improved discrimination. Conclusions: We identified the preoperative US BI-RADS category as a promising prognostic imaging phenotype marker for early-stage BC. Based on this marker, we developed a nomogram that outperformed the clinical model based on traditional prognostic factors. However, these preliminary findings require validation in broader populations prior to clinical application.
AneuGuidederived metal coverage ratio and pore density in Pipeline embolization devices: validation against VasoCT and association with aneurysm occlusion
Background: Metal coverage ratio (MCR) and pore density (PD) are key factors affecting the outcomes of intracranial aneurysms (IAs) treated with flow diverters (FDs), yet their in vivo quantitative assessment remains challenging. This study evaluated the accuracy of AneuGuide-derived MCR and PD against VasoCT-based measurements and explored their association with short-term aneurysm occlusion. Methods: This retrospective study enrolled patients treated with a single pipeline embolization device (PED) between June 2022 and April 2024. PED wires were reconstructed from postoperative VasoCT images to calculate MCRCT and PDCT at the aneurysm neck. Corresponding values (MCRAG and PDAG) were obtained using AneuGuide simulations. Agreement and correlation between the two methods were assessed using Bland-Altman analysis and Pearson correlation. The association between AneuGuide-derived parameters and aneurysm occlusion was evaluated using receiver operating characteristic (ROC) curves. Results: A total of 48 aneurysms were included. AneuGuide showed good agreement with VasoCT for both MCR (r=0.65, P<0.001) and PD (r=0.79, P<0.001), with no significant differences (P>0.05). A strong correlation was observed between MCR and PD (r=0.868, P<0.001). Non-occluded aneurysms exhibited lower MCR (22.05%±3.02% vs. 27.14%±7.65%, P=0.034) and PD (13.53±3.85 vs. 17.50±6.28 pores/mm2, P=0.058). ROC analysis yielded AUCs of 0.770 for MCR and 0.742 for PD. Conclusions: AneuGuide provided MCR and PD estimates that agreed well with VasoCT-derived measurements. Higher MCR and PD were associated with aneurysm occlusion at short-term follow-up. These findings support the utility of AneuGuide for geometric assessment of FD deployment and suggest a potential role in treatment-related evaluation.
Clinicoradiologic features and evolutionary characteristics of pulmonary focal mucinous adenocarcinomas across different density patterns: a retrospective multi-center study
Background: Pulmonary focal mucinous adenocarcinomas (MAs) (≤3 cm) frequently present with non-specific computed tomography (CT) features, posing diagnostic difficulties. This study aimed to identify the clinicoradiologic features of pulmonary focal MAs for early diagnosis. Methods: A total of 211 patients with 211 confirmed MAs were retrospectively enrolled from two centers between November 2012 and November 2025. Thirty-one (26.5%) of 117 subsolid mucinous adenocarcinomas (SS-MAs) and 20 (21.3%) of 94 solid mucinous adenocarcinomas (S-MAs) underwent follow-up CT. The clinical data and initial and follow-up CT findings were evaluated and compared between these two subtypes to identify significant differences. Results: Both SS-MAs and S-MAs mainly distributed in the lower lobes (62.4%, 62.9%) and subpleural zones (75.2%, 76.3%) (each P>0.05). Compared to patients with S-MAs, females (74.4% vs. 52.1%) and non-smokers (84.6% vs. 71.3%) were more common in those with SS-MAs. Compared to S-MAs, more SS-MAs had an ill-defined boundary (62.4% vs. 20.2%) and patchy appearance (43.6% vs. 24.5%), but fewer had lobulation (16.2% vs. 47.9%) and spiculation (8.5% vs. 38.3%) (each P<0.05). During follow-up, the S-MAs showed a higher cumulative incidence of volume doubling and a shorter volume doubling time (VDT) [336.5 (254.1–401.2) vs. 470.8 (400.8–566.3) days] than SS-MAs (each P<0.05). In addition to a significant increase in size and density of the SS-MA and S-MA in progression, the solid components in SS-MAs significantly increased in size and their boundary became increasingly well-defined (each P<0.05). However, the boundary of most SS-MAs remained ill-defined, and the density of S-MAs was consistently negative. Conclusions: Focal SS-MAs and S-MAs are two distinct radiologic phenotypes, and understanding their respective evolutionary characteristics is helpful for early diagnosis.
Nomogram for predicting left atrial appendage dense spontaneous echo contrast and/or thrombosis (dense spontaneous echo contrast/left atrial appendage thrombus): a comparison with CHADS2 and CHA2DS2-VASc scores based on three-dimensional transesophageal echocardiography
Background: Non-valvular atrial fibrillation (NVAF) carries a high risk of left atrial appendage thrombus (LAAT) and dense spontaneous echo contrast (dense SEC), the primary triggers of cardioembolic stroke. Conventional CHADS2 [congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, prior stroke/transient ischemic attack (TIA) score] and CHA2DS2‑VASc (congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, prior stroke/TIA, vascular disease, age 65–74 years, Sex category score) scores lack left atrial appendage (LAA) morphological features, yielding limited predictive accuracy for dense SEC/LAAT. This study constructed a nomogram based on quantitative LAA parameters derived from three-dimensional transesophageal echocardiography (3D-TEE) to predict dense SEC/LAAT in NVAF patients and compared its performance with the two conventional clinical risk scores. Methods: We retrospectively enrolled 159 NVAF patients who underwent 3D-TEE from July 2024 to December 2025, stratified into a dense SEC/LAAT positive group (n=50) and a negative group (n=109). Variables with severe multicollinearity [variance inflation factor (VIF) ≥10] were excluded. Univariate logistic regression (P<0.10) screened candidate predictors, followed by forward stepwise multivariate logistic regression to identify independent risk factors and construct a nomogram. Model discrimination, calibration and clinical utility were assessed via receiver operating characteristic (ROC) curves, calibration curves, 10-fold cross-validation, decision curve analysis (DCA) and clinical impact curves; inter-model area under the curve (AUC) comparisons used P<0.05 as the statistical significance threshold. Results: Four independent predictors of dense SEC/LAAT were identified: D-dimer >0.550 mg/L [odds ratio (OR) =7.805, 95% confidence interval (CI): 2.044–29.795, P=0.002]; European Heart Rhythm Association (EHRA) score ≥ IIb (OR =6.255, 95% CI: 1.458–26.833, P=0.013); LAA poor echogenicity (OR =23.037, 95% CI: 5.651–93.909, P<0.001); LAA orifice morphology (OR =0.537, 95% CI: 0.296–0.975, P=0.041). The nomogram achieved an original AUC of 0.892 (95% CI: 0.839–0.945) at an optimal cutoff value of 0.29, with sensitivity 0.82, specificity 0.83, accuracy 0.82, and negative predictive value 0.91. It significantly outperformed CHADS2 (AUC =0.629) and CHA2DS2-VASc (AUC =0.606) (all P<0.05). Ten-fold cross-validation yielded an optimism-corrected AUC of 0.868 and a mean Brier score of 0.127; however, marked overfitting was observed (calibration slope =4.175, mean maximum calibration error =0.385). DCA confirmed sustained positive net clinical benefit within the 10–50% threshold probability range. Conclusions: The 3D-TEE-based nomogram shows acceptable discrimination for dense SEC/LAAT in NVAF patients and addresses the limitations of traditional risk scoring systems. Nevertheless, prominent overfitting prevents its direct clinical use without external validation and recalibration; it can serve as an auxiliary research tool for LAAT risk stratification.
FEANet: Frequency-Enhanced Attention Network with detail-preserving fusion for pulmonary disease classification
Background: Pulmonary disease classification from medical images remains challenging due to heterogeneous imaging modalities and complex texture patterns. Most deep learning methods rely on spatial representations and underutilize frequency-domain information. This study aimed to develop and validate a frequency-enhanced deep learning framework for robust pulmonary disease classification across heterogeneous imaging datasets. Methods: We propose a Frequency-Enhanced Attention Network (FEANet) for pulmonary disease classification across multiple imaging modalities. FEANet employs a dual-branch architecture based on discrete wavelet transform to separate low-frequency (LF) structural information and high-frequency (HF) texture details. A Wavelet-Aware Semantic Context Attention (WASCA) module models global semantic information, a Frequency-Guided Detail Attention (FGDA) module adaptively enhances discriminative HF features, and a Frequency-Conditioned Fusion Attention (FCFA) module integrates complementary frequency representations. The model was evaluated on lung cancer computed tomography (CT), coronavirus disease 2019 (COVID-19) chest X-ray, and a clinical COPD CT dataset. Results: FEANet achieved accuracies of 98.70%, 96.04%, and 99.84%, with corresponding area under the curves (AUCs) of 0.9995, 0.9951, and 0.9998 on the lung cancer CT, COVID-19 X-ray, and COPD CT datasets, respectively. On the COVID-19 dataset, the full FEANet model achieved 96.04% accuracy and 96.04% F1-score, outperforming the strongest compared baseline by 0.52 percentage points in accuracy. Ablation analysis showed that the combined use of LF semantic attention, HF detail attention, and frequency-conditioned fusion produced the best performance. Robustness analysis further showed stable performance under mild-to-moderate image perturbations. Conclusions: FEANet effectively integrates frequency- and spatial-domain features, improving robustness and generalization across heterogeneous imaging modalities. It shows potential for clinical computer-aided diagnosis.
Efficacy and safety of the RC120 navigation system for the computed tomography-guided localization of pulmonary ground-glass nodules: a prospective study with historical controls
Background: Conventional computed tomography (CT)–-guided pulmonary nodule localization often has unsatisfactory precision and efficiency. The new RC120 navigation system (Shanghai Simple Touch Technology Co., Ltd.) is designed to streamline this process. This study aimed to evaluate the efficacy and safety of the RC120 system for the preoperative localization of ground-glass nodules (GGNs) by comparing a prospectively enrolled cohort operated by a novice to a historical control cohort operated by experts. Methods: This single-center study included a prospective cohort of 39 patients scheduled to undergo RC120-assisted localization by a single surgeon with no prior experience in CT-guided percutaneous interventions between July 2024 and January 2025. This group was compared to a retrospective control cohort of 138 patients who underwent conventional free-hand localization by two experienced surgeons between November 2021 and October 2023. The primary endpoint was the overall procedural success rate, defined as successful anchor deployment within 10 mm of the target nodule’s border. Key secondary endpoints included localization time, radiation dose, number of needle adjustments, and safety. Results: The baseline characteristics were well-balanced between the groups. Despite the RC120 group having a numerically lower success rate compared to the control group, the difference did not reach statistical significance (82.1% vs. 92.8%; P=0.062), with all RC120 anchor deployments remaining clinically adequate for surgical guidance. Although the procedures in the RC120 group were performed by a novice, there were significant improvements in key efficiency metrics as compared to the control group: the median localization time was shorter (15.0 min vs. 20.5 min; P<0.001), the median number of needle adjustments was lower (0 vs. 2; P<0.001), and the median cumulative radiation dose was lower (913.6 mGy·cm vs. 1091.4 mGy·cm; P=0.003). In terms of safety, the RC120 group had a significantly lower rate of hemothorax (2.6% vs. 21.0%; P=0.006). Learning curve analysis of the novice operator showed no significant correlation between case sequence and procedure time (rs=0.072; P=0.66), indicating a rapid acquisition of proficiency. Conclusions: In this preliminary study, use of the RC120 navigation system by a novice operator appeared feasible for GGN localization and was associated with enhanced procedural efficiency and reduced hemorrhagic complications as compared to historical free-hand techniques. The lack of a significant correlation between procedure time and case sequence suggests a minimal learning phase. These findings should be validated in future large-scale, randomized controlled trials.
SynQ-Breast: a flexible deep learning framework for quantitative MRI of breast tumors via synthetic data with IVIM-MRI validation
Background: Breast tissue exhibits inherently low signal-to-noise ratio (SNR) on diffusion-weighted imaging, which compromises the accuracy and stability of conventional model fitting. This study aimed to develop a flexible synthetic-data-driven deep learning framework (SynQ-Breast) for robust quantitative magnetic resonance imaging (qMRI) parameter estimation of breast tumors, addressing the challenges of low SNR and the scarcity of realistic training data. Methods: The supervised SynQ-Breast framework incorporates a training data synthesis module, where synthetic data are generated based on complex parameter distributions and spatial tissue texture. The synthetic data were modulated with realistic Rician noise to match clinical acquisitions, enabling the model to learn an anti-noise mapping to qMRI parameters. A U-Net incorporating spatial smoothness constraint was trained to produce qMRI parametric maps. Validation was performed on intravoxel incoherent motion MRI (IVIM-MRI) data from 49 breast tumor patients. Diagnostic performance was assessed using first-order histogram metrics from tumor regions, supported by statistical analyses (Mann-Whitney U test, independent t-test, Pearson correlation analysis, receiver operating characteristic curve analysis, and binary logistic regression analysis). Results: SynQ-Breast (30 ms per slice) achieved a three-order-of-magnitude acceleration in computational time, which was statistically significant (P<0.0001) compared to conventional nonlinear least squares (NLLS; 44 s per slice). The framework also yielded better lesion detectability in breast tissue in term of contrast-to-noise (CNR) [SynQ-Breast (mean ± standard deviation): 1.67±1.83, 1.76±1.58, 1.66±2.17, NLLS (mean ± standard deviation): 1.11±0.85, 0.47±0.37, 0.63±0.40 for diffusion coefficient (D), perfusion fraction (f), pseudo-diffusion coefficient (D*) respectively]. For tumor differentiation, SynQ-Breast achieved higher area under the curve (AUC) values than NLLS for single metric (best AUC: 0.843 vs. 0.808) and combined metric (AUC: 0.924 vs. 0.879). Conclusions: The SynQ-Breast framework provides a robust and efficient solution for breast qMRI, overcoming data scarcity and noise sensitivity. The performance of SynQ-Breast in IVIM-based breast tumor differentiation suggests its potential clinical value.
Contrast-enhanced ultrasound-based energy response phenotyping in thyroid nodule ablation: identifying heterogeneous subgroups via latent class analysis
Background: Thermal ablation of benign thyroid nodules shows substantial inter-patient heterogeneity, yet no pre-procedural tool exists to stratify treatment response. This study aims to identify energy response phenotypes in thyroid nodule thermal ablation using latent class analysis (LCA) and develop a pre-ablation predictive model. Methods: This retrospective study analyzed 157 patients undergoing thermal ablation for benign thyroid nodules. LCA integrated pre-ablation contrast-enhanced ultrasound (CEUS) parameters [peak intensity (PI), time to peak (TTP), area under the curve (AUC)], ablation energy, and volume reduction rates (VRRs) at 3, 6, and 12 months to identify distinct phenotypes. A multinomial logistic regression model using baseline CEUS and clinical variables predicted phenotype membership, validated in a held-out testing set (n=47, 30%). Results: Three energy response phenotypes were identified with adequate classification certainty (entropy =0.833): class 1 (delayed-response, n=56, 35.7%) exhibited the highest PI and prolonged TTP (both P<0.001) with gradual improvement (VRR 89.86% at 12 months); class 2 (energy-resistant, n=33, 21.0%) demonstrated reduced PI, rapid washout, required 7.6-fold higher energy (P<0.001), yet achieved poorest outcomes (12-month VRR 86.56%, P<0.001); class 3 (optimal-response, n=68, 43.3%) showed balanced perfusion achieving the highest VRR (97.65% at 12 months, P<0.001). Pre-ablation TTP emerged as the dominant predictor (100% relative importance). The discriminant model achieved good discrimination for class 1 (AUC =0.878) and class 2 (AUC =0.916), but limited discrimination for class 3 (AUC =0.726). Ten-fold cross-validation showed moderate stability (accuracy 59.73%±15.93%). Sensitivity analyses excluding energy from LCA confirmed phenotype stability (κ=0.82). Conclusions: CEUS-based phenotyping successfully stratifies thyroid nodule ablation candidates, enabling personalized treatment planning. Pre-ablation prediction identifies energy-resistant patients requiring alternative therapeutic strategies.
The value of CMR-derived left atrial strain parameters in differential diagnosis of cardiac amyloidosis and hypertrophic cardiomyopathy
Background: Differentiating cardiac amyloidosis (CA) from hypertrophic cardiomyopathy (HCM) is clinically challenging due to overlapping phenotypes of left ventricular hypertrophy. This study aimed to evaluate the diagnostic value of cardiac magnetic resonance (CMR)-derived left atrial strain parameters in differentiating CA from HCM. Methods: A retrospective analysis was performed on CMR data obtained from 37 patients with HCM, 41 patients with CA, and 29 individuals in the normal control group. Left ventricular and left atrial functions were quantified using CVI42 software (version 5.14.2). Left atrial strain parameters were assessed using the tissue tracking module, including left atrial reservoir strain (εs) and strain rate (SRs), conduit strain (εe) and strain rate (SRe), and booster strain (εa) and strain rate (SRa). Differences in left ventricular and left atrial functional parameters, as well as left atrial strain parameters among the three groups, were analyzed using the Kruskal-Wallis test. Pairwise comparisons were conducted using the Mann-Whitney U test. Receiver operating characteristic (ROC) curve analysis was performed using MedCalc (version 15.2.2) to determine the diagnostic efficacy of left atrial strain parameters in differentiating CA from HCM. Results: The absolute values of εs, εe, εa, SRs, SRe, and SRa were significantly lower in both the HCM and CA groups compared with the normal control group (P<0.05). Furthermore, the absolute values of these parameters were markedly lower in CA compared with HCM (P<0.05). Among these parameters, εs demonstrated the highest diagnostic performance for distinguishing CA from HCM, with an area under the ROC curve of 0.921. Conclusions: CMR-derived left atrial strain parameters, particularly εs, might serve as a reliable means for differentiating between HCM and CA, which need further prospective study including patients with all HCM types and conducting internal and external validation to confirm credibility and expand generalizability.
Large language models for analyzing contrast-enhanced ultrasound reports of pancreatic cystic lesions
Background: Pancreatic cystic lesions (PCLs) require precise imaging characterization to guide clinical management. Contrast-enhanced ultrasound (CEUS) reports contain operator-dependent narratives that challenge clinicians. Large language models (LLMs) show potential in medical text analysis but lack validation for pancreatic CEUS interpretation. This study primarily aimed to evaluate the diagnostic accuracy of LLMs in interpreting Chinese CEUS reports of PCLs. Methods: We retrospectively analyzed 80 pathologically confirmed PCLs (21 benign, 25 borderline malignant, 34 malignant). Four LLMs (GPT-4o, Claude 3.7 Sonnet, Gemini 2.0, DeepSeek-R1) and eight radiologists (senior/junior =4:4) independently interpreted reports under three input modalities: grayscale-only (IM1), grayscale + CEUS (IM2), and demographics + grayscale + CEUS (IM3). A weighted scoring system (0–100 points per case, yielding a maximum total of 8,000 points) quantified alignment with pathology-defined categories (benign/borderline/malignant). LLM errors were categorized into four reasons. Junior radiologists reinterpreted cases with LLM assistance. Results: Under CEUS input conditions (IM2/IM3), LLMs showed no statistically significant difference from senior radiologists and significantly outperformed junior radiologists. The median score per case (out of 100), after averaging across the four LLMs, increased with input complexity (IM1: 47.50; IM2: 53.75; IM3: 73.75). Diagnostic accuracy varied by pathology: malignant lesions scored highest, while benign serous cystic neoplasms scored lowest due to suboptimal CEUS visualization and LLMs’ knowledge gaps. LLM guidance elevated junior radiologists’ accuracy to senior levels. Conclusions: LLMs show promising capability in interpreting CEUS reports of PCLs, with no statistically significant difference from senior radiologists in this dataset. Their integration significantly improves junior clinicians’ interpretation of CEUS reports. These findings support further investigation of LLMs as potential auxiliary tools for ultrasound text analysis, though external validation is needed.
Spatially and temporally resolved assessment of quantitative skeletal muscle perfusion with non-contrast MRI in patients with PAD and healthy controls
Background: Peripheral arterial disease (PAD) is mainly defined by macrovascular obstruction, while microvascular dysfunction remains insufficiently characterized by conventional diagnostics. Arterial spin labeling (ASL) magnetic resonance imaging (MRI) enables non-invasive, contrast-free quantification of skeletal muscle blood flow (SMBF) in the calf, with potential applications in PAD diagnosis, risk stratification, and perioperative assessment of microcirculation. Deep learning enabled reconstruction of dynamic ASL data offers potential to generate temporally and spatially resolved high-quality perfusion maps. The purpose of this study was to determine whether this dynamic ASL could distinguish PAD from healthy controls (HCs), both on spatial and temporal domains. Methods: Dynamic SMBF maps of the calf were acquired with 15-sec temporal resolution before, during, and after a 3-minute isometric plantar flexion exercise (hyperemia) using a MR-compatible ergometer in 10 patients with mild to severe PAD and 10 age- and sex-matched healthy volunteers. PAD was confirmed by significantly lower ankle-brachial index values compared with HCs (0.60±0.21 vs. 1.25±0.1, P<0.0001). A convolutional neural network was trained on synthetic ASL data and applied to the dynamic series to reconstruct spatiotemporal SMBF maps with full-sampling and under-sampling schemes. Regions of interest were defined in five calf muscles (medial gastrocnemius, soleus, lateral gastrocnemius, tibialis anterior, and lateral compartment) and perfusion was quantified on the SMBF maps. Linear mixed-effects modeling evaluated SMBF across time, muscle, and cohort. Results: Peak exercise perfusion did not differ significantly between two cohorts across muscle groups (all P>0.05), except in the medial gastrocnemius when normalized for baseline perfusion [PAD: 54.0%±41.4 % vs. HC: 83.4% (66.2%, 128.4%), P=0.05]. In contrast, the hyperemic recovery time constant (τhyperemia), a time-dependent kinetic parameter describing post-exercise perfusion return towards baseline, was significantly prolonged in patients with PAD across all muscle groups (P<0.05), except the tibial compartment (P=0.094). Linear mixed-effects analysis demonstrated higher resting SMBF in patients with PAD compared with controls (difference +8.3 mL/min/100 g, P=0.011), but revealed a significantly attenuated exercise-induced perfusion response, reflected by a robust time × group × muscle interaction (P<0.0001). Deep learning reconstruction of under-sampled ASL data produced SMBF maps with relatively low bias compared to fully sampled references (Bland-Altman: −1.2 to −2.0 mL/min/100 g across conditions), potentially improving temporal resolution by 100%. Conclusions: Deep learning-enhanced, temporally resolved ASL MRI identifies attenuated exercise-induced perfusion and delayed recovery after exercise in patients with PAD that are not evident using static peak perfusion metrics alone. Active hyperemia recovery kinetics provide a sensitive marker of microvascular dysfunction, while deep learning reconstruction enables high temporal resolution imaging within clinically practical scan times.
13N-ammonia positron emission tomography/magnetic resonance imaging in patients with ischemia with non-obstructive coronary arteries: focus on feature tracking and T1 mapping
Background: Ischemia with non-obstructive coronary arteries (INOCA) is associated with elevated cardiovascular risk, yet early subclinical myocardial changes often go undetected by conventional imaging. This study aimed to determine whether patients with INOCA exhibit subclinical tissue alterations identified by T1 mapping and left ventricular wall motion abnormalities assessed by cardiac magnetic resonance feature tracking (CMR-FT), in comparison with matched healthy controls (HC). Methods: A total of 44 patients with INOCA and 22 matched HC were prospectively enrolled from August 2024 to February 2026, and all underwent hybrid cardiac positron emission tomography/magnetic resonance imaging (PET/MRI) examination. CMR-derived parameters were obtained and compared across the study groups. Results: Native T1 values increased progressively across three groups: HC, INOCA with myocardial flow reserve (MFR) >2, and INOCA with MFR <2 (1,196±19 vs. 1,213±8 vs. 1,244±20 ms; P<0.001). In contrast, the absolute values of global longitudinal strain (GLS) showed a gradual reduction (−19.21%±1.84% vs. −17.83%±0.92% vs. −16.25%±1.59%; P<0.001), whereas no significant intergroup differences were observed in left ventricular ejection fraction (LVEF) or extracellular volume (ECV) (both P>0.05). Both native T1 and GLS were negatively correlated with MFR (r=−0.739, P<0.001; r=−0.638, P<0.001, respectively). Similarly, native T1 and ECV were positively correlated with GLS (P<0.001 and P=0.004). Conclusions: Native T1 and GLS correlate with impaired MFR in patients with INOCA, consistent with subclinical left ventricular (LV) systolic dysfunction related to myocardial ischemia. Incorporating CMR into clinical practice may aid in the monitoring and treatment of patients with INOCA, and further studies exploring the underlying mechanisms are warranted.
Has computer-aided diagnosis now outperformed radiologists in the accurate localization of lung nodules?—a multicenter retrospective study
Background: Globally, lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer-related death. Manual radiologist (RAD) reading brings inconsistent localization results for subtle nodules, while existing computer-aided diagnosis (CAD) tools lack verified three-dimensional (3D) positioning accuracy compared with human readers. Thus, the aim of this study was to compare 3D localization accuracy of CAD and RAD evaluations for pulmonary nodules on computed tomography (CT) images. Methods: This multicenter retrospective study included 1,275 patients’ chest thin-section CT images from 3 centers. CT images were evaluated via a radiologist (RAD) assessment and a CAD tool. The CAD system was developed using a high-precision convolutional neural network (U-Net) for 3D localization of pulmonary nodules. The gold standard for nodule localization was established based on surgical findings, postoperative histopathological confirmation of the resected specimens, and correlation with preoperative CT images. Results: In Center 1 (921 pulmonary nodules), CAD and RAD correctly located 856 and 758 pulmonary nodules, with accuracy rates of 92.9% [95% confidence interval (CI), 91.1–94.4%] and 82.3% (95% CI, 79.7–84.6%), respectively. In Center 2 (145 pulmonary nodules), CAD and RAD correctly located 141 and 112 pulmonary nodules, with accuracy rates of 97.2% (95% CI, 94.54–99.90%) and 77.2% (95% CI, 70.39–84.05%), respectively. In Center 3 (209 pulmonary nodules), CAD and RAD correctly located 196 and 163 pulmonary nodules, with accuracy rates of 93.8% (95% CI, 90.53–97.07%) and 78% (95% CI, 72.38–83.62%), respectively. In all three centers, the accuracy rate using CAD was significantly greater than that using RAD (all P<0.001). Conclusions: Compared with manual detection by radiologists, the use of CAD increased the accuracy rate for 3D localization of pulmonary nodules.
Development and validation of an ultrasound-based deep learning radiomics nomogram for risk assessment of lymph node metastasis in papillary thyroid microcarcinoma
Background: Accurate preoperative prediction of cervical lymph node metastasis (LNM) in papillary thyroid microcarcinoma (PTMC) remains challenging, particularly because occult nodal disease is common and conventional ultrasound (US) assessment is operator-dependent. This study aimed to develop and validate a multicenter US-based deep learning radiomics nomogram integrating intratumoral, peritumoral, and clinical features for individualized LNM risk assessment in patients with PTMC. Methods: We retrospectively and prospectively collected multi-center data from three hospitals. Patients who underwent total thyroidectomy or lobectomy with lymph node dissection were allocated to a training set (763 cases), an external test set (118 cases), and a prospective validation set (94 cases). Radiomics features from within the tumor and deep learning (DL) features from the peritumoral region were extracted from the largest cross-sectional US image. Twelve machine learning (ML) models were built using the integrated features and evaluated on the test set to identify the optimal one. The machine learning prediction score (ML-score) was incorporated with clinical factors into a nomogram, and its performance was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and clinical impact curves (CICs) analysis. Results: The support vector machine (SVM) model showed the best overall performance among the twelve ML algorithms and was used to generate the SVM-score. Multivariable logistic regression identified age, sex, maximum tumor diameter, genetic mutation status, and SVM-score as independent predictors of LNM. The integrated nomogram achieved AUCs of 0.894 in the training cohort, 0.842 in the external test cohort, and 0.856 in the prospective validation cohort. Calibration curves showed good agreement between predicted and observed LNM risk, with Hosmer-Lemeshow test P values of 0.189, 0.383, and 0.254 in the three cohorts, respectively. DCA and CIC demonstrated that the nomogram provided greater clinical net benefit than the clinical model or SVM-score model alone. Conclusions: The US-based DL radiomics nomogram integrating imaging features and clinical factors showed good performance for preoperative prediction of LNM in patients with PTMC. This non-invasive tool may assist individualized cervical lymph node risk stratification and support clinical decision-making.
Quantitative generation of diffusion-weighted imaging from non-contrast CT in acute ischemic stroke: a multi-scanner deep-learning model with external validation
Background: Acute ischemic stroke (AIS) is a leading cause of death and disability worldwide, where early diagnosis and treatment are crucial for effective management. Computed tomography (CT), particularly non-contrast CT, is widely used due to its speed and low cost; however, it has limited sensitivity and specificity. Although diffusion-weighted imaging (DWI) provides higher diagnostic accuracy, it is time-consuming and may delay treatment. This study aims to investigate the feasibility of using deep learning techniques to convert non-contrast CT images of patients with cerebral ischemic stroke into DWI images. Methods: The dataset used in this study consisted of non-contrast CT images and DWI from 224 patients between May 2013 and April 2022, and it was divided into training (n=180), validation (n=20), and testing (n=24) cohorts. The proposed methodology used a modified ControlNet, a deep learning architecture known for its ability to learn complex mappings, to learn the intricate relationship between the two modalities and generate synthetic DWI based on non-contrast CT scans of patients with acute ischemic stroke. Model performance was evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE), together with Bland-Altman analysis, voxel-wise area under the receiver operating characteristic curve (AUC) for stroke core discrimination, and radiologist-based qualitative scoring. Results: On the independent testing cohort, the proposed model achieved a PSNR of 29.32±1.84 dB, an SSIM of 0.867±0.032, and an NMSE of 0.293±0.041, outperforming U-Net-, Pix2Pix-, CycleGAN-, and DualGAN-based comparison methods in quantitative and visual evaluations. For stroke core discrimination based on synthesized DWI, the proposed method achieved the highest voxel-wise AUC (0.764±0.026) among all compared methods. In case-level error analysis, the model showed 1 false-positive (FP) case (4.2%) and 4 false-negative (FN) cases (16.7%) in the 24 test cases. Qualitative evaluation by four radiologists showed high image quality, with a mean overall score of 4.78/5. The average inference time was 2.46 s per slice, corresponding to approximately 5–7 min for a full brain volume. Conclusions: The conversion of stroke CT scans into DWI using our methodology is not just feasible, but it has the potential to revolutionize bedside decision-making. This suggests a broader potential application to transform diagnostic imaging, particularly in the context of stroke diagnosis. Our method provides clinicians with valuable information, enabling the production of advanced DWI-like images on demand in emergency settings.
Clinical prognostic value of imaging and immuno-hormonal markers in uterine leiomyoma
Background: Uterine leiomyoma is a prevalent benign tumor arising from the smooth muscle cells of the uterine wall, profoundly affecting women’s reproductive health and overall quality of life. Despite extensive studies on hormonal and molecular genetic mechanisms, its pathogenesis remains incompletely understood. Increasing evidence suggests that cytokines, growth factors, and immune-hormonal interactions may play a key role in tumor progression and recurrence. This study aimed to evaluate the prognostic significance of imaging, immunological, and hormonal markers in the assessment of antitumor immune response and clinical outcomes in women of reproductive age with uterine leiomyoma. Methods: A total of 61 women diagnosed with uterine leiomyoma were examined at the Gynecology Department of Bukhara Outpatient Polyclinic (OPC). Clinical, biochemical, hormonal, and immunological parameters were analyzed using standard laboratory assays and magnetic resonance imaging (MRI). Statistical analysis included comparative, correlational, and regression methods to identify prognostic indicators. Results: Hereditary predisposition, history of medical abortion, and excess body weight were identified as major risk factors. Elevated serum biomarker levels of vascular endothelial growth factor (VEGF) (>95.1 pg/mL), estradiol (>40.5 pg/mL), and fibroblast growth factor-1 (FGF-1) (>81.4 pg/mL) significantly increased the likelihood of leiomyoma development (P<0.05). Enhancing antiproliferative activity of transforming growth factor beta-2 (TGF-β2) and reducing progesterone synthesis reflected impaired growth-factor-mediated regulation and potential neoplastic transformation. Serum levels of cancer antigen 125 (CA-125) and cancer antigen 19-9 (CA-19-9) were significantly elevated in women with uterine leiomyoma compared with controls (P<0.0001). MRI findings demonstrated heterogeneous leiomyoma morphology with variable localization, vascularization, and nodular architecture. Macroscopic examination revealed well-circumscribed firm nodules with a characteristic whorled appearance. Histological analysis confirmed benign smooth muscle proliferation with interlacing fascicles, minimal atypia, connective tissue stroma, and variable fibrotic or vascular changes. Conclusions: Uterine leiomyoma is characterized by elevated tumor markers, hormonal dysregulation, inflammatory and angiogenic activation, and distinct radiological and morphological features. Integrated evaluation of biochemical, imaging, and histopathological parameters may improve diagnostic assessment, characterization of tumor heterogeneity, and individualized management strategies in women with uterine leiomyoma. Integration of imaging findings with immuno-hormonal biomarkers may improve early detection, risk stratification, and individualized management of uterine leiomyoma.
Segment-resolved assessment of hydration effects on liver water-specific T1 in comparison with T1-MOLLI and transient elastography
Background: Quantitative liver T1 mapping is a promising noninvasive biomarker for diffuse liver disease, but it is influenced by physiological variability. Hydration alters tissue water content and perfusion, yet its effect on liver water-specific T1 (wT1) remains poorly characterized, particularly across liver segments. Prior studies relied on single-slice acquisitions, limiting assessment of spatial heterogeneity. The objective is to assess hydration-related changes in liver wT1 across all Couinaud segments using an accelerated multi-slice technique and to compare results with single-slice T1-modified Look-Locker inversion recovery (T1-MOLLI) and vibration-controlled transient elastography (VCTE). Methods: Twenty-nine healthy adults underwent blood sampling, VCTE, and liver magnetic resonance imaging (MRI) after an ≥8-hour fast without fluid intake and again after ingestion of 1 L of water followed by a 1-hour equilibration period. Multi-slice wT1 mapping was performed using a Dixon-based continuous inversion-recovery Look-Locker (CIR-LL) sequence covering the entire liver in a single breath-hold. Single-slice T1-MOLLI and whole-liver proton density fat fraction (PDFF) and T2* mapping were acquired for comparison. Regions of interest (ROIs) were placed on PDFF, T2*, and wT1 maps in all liver segments. Statistical analysis included intraclass correlation coefficient (ICC; [2, 1]), linear regression, Pearson correlation coefficients Bland-Altman plots and paired two-sided t-tests. Results: Hydration resulted in a higher global increase in mean liver wT1 (before: 773.4±63.2 ms, after: 800.3±66 ms) than T1-MOLLI (before: 853±68.8 ms, after: 861.6±55.9 ms). PDFF, T2*, and liver stiffness varied modestly within the physiological range. For liver stiffness, T1-MOLLI, and wT1, measurements obtained before and after hydration showed good agreement, with wT1 (y=0.9x+80.5, r=0.89, P<0.001) exhibiting a regression coefficient closer to that of liver stiffness (y=0.9x+0.7, r=0.63, P<0.001) than T1-MOLLI (y=0.6x+372.4, r=0.71, P<0.001). T1-MOLLI and wT1 showed good agreement under both hydration conditions (before: y=0.5x+355.7, r=0.64, after: y=0.8x+98.9, r=0.78), with T1-MOLLI yielding systematically higher values than wT1. Paired analysis demonstrated significant hydration-related changes in wT1 (P<0.001), with segment-wise wT1 increases remaining significant after correction for multiple testing. Furthermore, wT1 revealed spatial heterogeneity across liver segments. Conclusions: Liver wT1 is physiologically modulated by hydration and exhibits spatial heterogeneity across liver segments, even in the healthy liver. These findings underscore the importance of whole-liver, segment-resolved wT1 mapping and physiological standardization when interpreting quantitative liver MRI.
Implementation of an artificial intelligence-based system for mammography in the compulsory medical insurance program: results of a 3-year study
Background: The integration of artificial intelligence (AI) into mammography holds significant potential for addressing the increasing workload and radiologist burnout, yet its widespread clinical adoption is hindered by critical limitations in current validation practices. Existing frameworks frequently fail to account for AI’s dynamic evolution through retraining, data heterogeneity, and real-world deployment within healthcare systems such as compulsory medical insurance (CMI). This study aims to ensure continuous quality control of a mammography AI solution during its implementation in the CMI system by applying a novel lifecycle-based testing and monitoring methodology that addresses these specific gaps. Methods: The observational study incorporated retrospective functional and calibration testing, alongside prospective technical and clinical monitoring, interspersed with AI system updates. Anonymized digital mammograms from women aged ≥18 years underwent analysis. Prospective monitoring included all mammograms from participating sites (no exclusion criteria), capturing continuous real-world clinical data. The mammography AI system utilized U-Net++ and Mask2Former architectures, trained on ~4,000 mammograms. Key metrics encompassed area under the curve (AUC), accuracy, sensitivity, specificity, technical defect rates, and clinical assessment scores. Results: The test dataset comprised 404,502 mammograms from 206 medical organizations and three mammography equipment manufacturers. A total of 336 radiologists participated. The testing and monitoring period lasted 2 years and 5 months. Over this time, AUC increased by 10.8% (from 0.83 to 0.92), accuracy by 16.9% (from 0.77 to 0.90), sensitivity by 4.8% (from 0.84 to 0.88), and specificity by 30.0% (from 0.70 to 0.91). The average technical defect rate decreased by 26.7% (from 3.0% to 0.8%), and the clinical assessment score rose by 47.8% (from 54.38% to 80.36%). The study culminated in the integration of the AI system into the regional CMI program. A key limitation of this study is the relatively small retrospective calibration testing dataset (100 mammograms) and the lack of external validation on independent datasets from other regions or countries. Conclusions: Iterative testing with prospective real-world monitoring, interleaved developer updates, and radiologist feedback substantially enhanced mammography AI performance. This lifecycle testing methodology demonstrates feasibility for clinical integration and CMI program deployment, balancing rigorous validation with continuous improvement. Future work will focus on expanding the retrospective calibration testing dataset and scaling the approach to a national level within the CMI framework.
Diagnostic performance of the main pulmonary artery-to-ascending aorta diameter ratio for pulmonary hypertension defined by mean pulmonary arterial pressure in children with left-to-right shunt congenital heart disease
Background: The computed tomography (CT)-derived main pulmonary artery-to-ascending aorta diameter ratio (MPA/AA) may identify pulmonary hypertension (PH). In left-to-right (L-R) shunt congenital heart disease (CHD), pulmonary overcirculation may enlarge the pulmonary artery without elevated mean pulmonary arterial pressure (mPAP), complicating threshold interpretation. We evaluated MPA/AA for mPAP-defined PH using right heart catheterization (RHC) and derived upper reference limits in RHC-negative children. Methods: Among 231 eligible children who underwent CT and RHC within 3 months, the primary diagnostic analysis was performed in 164 children with L-R shunt CHD. A heterogeneous non-L-R cardiovascular cohort (n=67) was used only to assess model transportability. Disease-context upper reference limits were estimated in 109 children without mPAP-defined PH on RHC. Results: In the primary cohort, median MPA/AA was higher in children with mPAP-defined PH than in those without it (1.56 vs. 1.22; P<0.001) and correlated with mPAP (Spearman r=0.69; P<0.001). The MPA/AA model had an area under the curve of 0.94 [95% confidence interval (CI): 0.90–0.97]. At a cutoff of 1.40, the model presented with 84.5% sensitivity and 92.6% specificity, whereas at a cutoff of 1.44, it presented with 96.3% specificity and 73.6% sensitivity. Bootstrap validation showed little overfitting. The maximum spline-derived 95th-percentile upper reference limit in RHC-negative children was 1.39. Discrimination was retained in the non-L-R cohort, although calibration was weaker. Conclusions: In children with L-R shunt CHD, CT-derived MPA/AA adds useful information for identifying mPAP-defined PH on clinically indicated CT, but it does not replace RHC or justify CT screening.
Effect of imaging parameters and clinical characteristics on pericoronary adipose tissue attenuation in patients without coronary plaque
Background: Pericoronary adipose tissue (PCAT) attenuation serves as a marker of coronary inflammation. However, numerous confounders may affect PCAT attenuation. This study aimed to investigate the influence of coronary computed tomography angiography (CCTA) imaging parameters and clinical characteristics on the fat attenuation index (FAI) of PCAT. Methods: One hundred and ninety-six subjects [mean age ± standard deviation (SD), 55.4±9.5 years; 81 males] who underwent CCTA and had no coronary plaque were included in this retrospective study. Images were scanned at 100 or 120 kVp and reconstructed using two methods. PCAT attenuation was assessed using two measurement approaches: a proximal 40-mm segment of each coronary artery and a 10-mm segment in each coronary artery segment. The intraclass correlation coefficient (ICC) was used to assess interobserver agreement for FAI measurements. Linear regression analyses were performed to identify independent factors associated with FAI. Results: Interobserver agreement for FAI measurements was excellent, with an ICC of 0.985–0.998; 120 kVp was independently associated with higher FAI values across the majority of segments (β=4.9–8.1, all P<0.001), except for FAI_RCA_mid_10mm and FAI_LCX_dist_10mm. The reconstruction method Iterative Model Reconstruction (IMR; level 1) with Cardiac Routine yielded significantly lower FAI than iDose4 (level 5) with Xres Standard in FAI_RCA_40mm, FAI_LAD_40mm, FAI_LAD_mid_10mm, and FAI_LCX_prox_10mm (β=−3.0 to −2.2, all P<0.05). Sex (male vs. female) was independently associated with FAI_LCX_dist_10mm (β=−3.7, P=0.032). Body mass index (BMI) ≥24 kg/m2 was associated with lower FAI_LAD_40mm, FAI_LAD_prox_10mm, and FAI_LAD_dist_10mm (β=−3.4 to −2.4, all P<0.05). Diabetes was negatively associated with FAI_LAD_prox_10mm (β=−3.9, P=0.030). Smoking was a positive predictor for FAI_RCA_40mm, FAI_LAD_40mm, FAI_RCA_mid_10mm, FAI_LAD_prox_10mm, and FAI_LCX_dist_10mm (β=4.1–6.5, all P<0.05). Dyslipidemia showed a negative association with FAI_LAD_40mm, FAI_RCA_dist_10mm, and FAI_LCX_prox_10mm (β=−3.9 to −2.5, all P<0.05). Other factors, including vessel attenuation, age, hypertension, and family history of coronary artery disease (CAD), showed no significant associations. Conclusions: In patients without coronary plaque, PCAT attenuation depends on imaging parameters and clinical characteristics, underscoring the importance of considering both imaging and clinical factors when interpreting FAI measurements.
Dry eye detection based on multidimensional temporal features of fluorescein tear film videos
Background: Dry eye disease (DED) is a common chronic eye disease with a high prevalence, and the imbalance of the tear film is a core characteristic of its pathogenesis. The time that is required for the tear film to break and form a dry spot after eye opening is known as the fluorescein breakup time (FBUT). FBUT is a standard clinical test for DED diagnosis. Currently, most DED diagnoses rely on FBUT test image acquisition devices and manual timing judgments, which are subjective, complex, and difficult. Additionally, computer-aided detection techniques face challenges in feature recognition, accuracy (ACC), and real-time, fully automated detection. Therefore, the automated recognition of dry eye based on deep learning analysis of fluorescein tear film breakup videos has become an important research direction. The objective of this study was to develop a system using a deep learning model to automatically detect DED from fluorescein tear film breakup videos. Methods: We constructed a fluorescein tear film video dataset with 148 DED cases and 100 non-DED cases based on clinical evaluations, and a segment dataset included 2,543 video frames with annotated tear film regions. Based on this, we proposed a fully automated method for DED detection with multidimensional temporal features of tear film as input. First, we used the multi-attention segmentation network (MASN) to segment the tear film region. Then, the tear film percentage curve in the video was used to calculate the fully open eye threshold, which determines the consecutive frames for further analysis. Subsequently, we constructed an efficient network to compress the morphological features in the tear film region and used PyRadiomics to extract the texture features from the tear film region. Finally, the cross-attention transformer classification network (CATCN) was used to detect the DED, which fused the temporal and spatial features from two different perspectives. Results: The segmentation results of the tear film region achieved an ACC of 0.97, a sensitivity (SE) of 0.82, and an area under the curve (AUC) of 0.98. The DED detection method was then tested on our datasets, achieving an ACC of 0.92, an SE of 0.86, and an AUC of 0.96. Conclusions: The fully automated dry eye detection system achieves excellent detection performance through precise tear film segmentation and multi-view tear film feature fusion recognition. It offers a convenient new method for large-scale screening of DED.
Application of computer-aided design/computer-aided manufacturing and mixed reality technologies in a novel mandibular reconstruction plate system
Mandibular reconstruction following oral and maxillofacial tumor resection is essential for restoring occlusion and facial appearance. Conventional methods require intraoperative bending of plates, increasing surgical time and risking metal fatigue and plate fracture. Patient-specific implants (PSIs), created via preoperative computed tomography (CT) data and three-dimensional (3D) metal printing, have improved surgical accuracy and reduced operation times. In Japan, Cosmofix®︎ is the only PSI system available, but it lacks osteotomy guides, complicating precise bone cuts and screw insertion angles. To address these gaps, we developed custom-designed osteotomy guides, using computer-aided design (CAD)/computer-aided manufacturing (CAM) and in-house 3D printing, and incorporated mixed reality (MR) technology to enhance preoperative and intraoperative planning and accuracy. Preoperative CT and 3D reconstruction were performed, and the resection range and Cosmofix® plate were designed in consultation with the engineer. The plate was equipped with claw-like projections to prevent displacement. Based on the plate design data, an osteotomy guide was created and manufactured using a 3D printer. Screw holes were designed for fixation. Using STL data, we created an application enabling multiple surgeons to visualize anatomical structures and resection margins in 3D during preoperative conferences and intraoperatively. A 22-year-old male patient with mandibular ameloblastoma underwent segmental resection and reconstruction using Cosmofix®, a custom osteotomy guide, and MR technology. Preoperative virtual planning allowed precise design of the reconstruction plate and osteotomy guides, and MR applications enabled shared surgical understanding and verification of resection range, jaw positioning, and implant angles during surgery. Postoperative accuracy assessment showed that bone surface error within 1 mm was achieved in 94.1% (right) and 92.9% (left) of the surgical area. At 1 year postop, functional and aesthetic outcomes were favorable, with no plate or bone abnormalities or signs of recurrence; furthermore, the patient showed recovering nerve function. The combination of PSIs, custom osteotomy guides, and MR technology enabled accurate, efficient mandibular reconstruction. This approach improved surgical precision and resulted in excellent functional and cosmetic outcomes
Congenital pulmonary airway malformation mimicking lung cancer in adults: a pitfall in oncologic diagnosis
Congenital pulmonary airway malformation (CPAM) presenting as a solid or solid‑cystic mass on computed tomography (CT) is rare in adults. Its imaging features can closely resemble those of primary lung cancer, often leading to misdiagnosis and adversely influencing clinical decision-making. This dual‑center retrospective case series included adult patients who were radiologically diagnosed with lung cancer but then surgically confirmed to have CPAM between January 2010 and December 2025. Two senior cardiothoracic radiologists jointly reviewed the CT images to evaluate basic lesion characteristics (size, location, and morphology), malignancy‑mimicking signs that prompted the misdiagnosis (e.g., lobulation, spiculation, and pleural retraction), and benign indicators that had been overlooked (e.g., internal microcysts, bronchial communication, and long‑term stability). Descriptive statistics were applied. Ultimately, six patients (two males and four females) with a median age of 59.5 years (range, 36–62 years) were included. The mean lesion diameter was 22.3±8.5 mm. Among the lesions, three were solid‑cystic (all communicating with bronchi), and three were solid (one showing marginal calcification). The misdiagnosis‑related “malignant” features included lobulation (n=3), spiculation (n=3), and pleural retraction (n=3). All three contrast‑enhanced lesions demonstrated mild homogeneous enhancement, with a mean venous‑phase enhancement value of 18.7±3.2 HU. Postoperative pathology confirmed CPAM with no evidence of malignancy. In conclusion, adult CPAM appearing as a solid or solid‑cystic mass on CT poses a considerable diagnostic challenge due to its striking resemblance to lung cancer.
Impact of CT slice thickness reduction algorithm on AI-based lung nodule detection in chest CTs of colorectal cancer patients
This retrospective multicenter study evaluated whether deep learning-based super-resolution (SR) reconstruction can enhance structural conspicuity in thick-section chest computed tomography (CT) and improve the detection performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system for lung nodules in 96 patients with colorectal cancer (CRC) undergoing chest CT for metastatic surveillance. Pulmonary nodules <10 mm and ≤3 per patient were analyzed. Three image sets were evaluated using a commercial AI-CAD system: original thin-section images (reference), original 5-mm thick-section images, and SR-converted thin-section images. Nodule- and patient-level detection performances were compared using the Cochran-Mantel-Haenszel test and aligned rank transform analysis of variance (ART-ANOVA). Quantitative nodule metrics, image noise, and morphologic consistency were assessed between original and SR-converted thin-section images. Among 105 reference nodules, AI sensitivity increased from 31.4% with thick-section images to 61.0% with SR-converted images, and positive predictive value (PPV) increased from 42.9% to 80.0%. Patient-level sensitivity improved from 41.5% to 67.1%. SR reconstruction reduced image noise (P<0.001) and preserved nodule morphology, with 95.3% anatomic concordance and 80% solid-feature consistency. Nodule size remained comparable; however, density was lower in converted images. SR reconstruction generated 20% hallucinated nodules (16/80), predominantly benign or artifactual structures. In conclusion, SR reconstruction enhances AI-based pulmonary nodule detection by compensating for structural detail lost in thick-section CT. Despite hallucinated nodules, SR reconstruction may provide a feasible harmonization strategy for retrospective multicenter AI research using heterogeneous CT datasets, although further validation in larger populations is required.
Three-dimensional ultrasound for diagnosing Robert’s uterus: a case series
Background: Robert’s uterus (also known as Robert’s uterine malformation) is a rare congenital anomaly characterized by a complete, asymmetric muscular septum that divides the uterine cavity into two separate hemi-cavities, one of which is often blind or severely hypoplastic, while the cervix is single and exhibits a normal morphological configuration with a single normal cervix. It is associated with poor reproductive outcomes, including infertility and recurrent pregnancy loss. Accurate diagnosis with conventional two-dimensional (2D) ultrasound is challenging. Three-dimensional (3D) ultrasound enables coronal-plane reconstruction of the uterus, which is crucial for a definitive diagnosis. This study aimed to summarize the 3D ultrasound imaging features of Robert’s uterus and evaluate its diagnostic value and impact on reproductive outcomes. Case Description: This retrospective case series included 10 patients with Robert’s uterus confirmed by hysteroscopy and laparoscopy. All patients underwent preoperative transvaginal 2D and 3D ultrasound. 3D ultrasound clearly demonstrated the characteristic features in all cases: a broad fundus, a complete uterine septum extending to just above the internal cervical os, resulting in two completely separate cavities with visible asymmetry in size and shape, and a single normal cervical canal. The diagnostic accuracy of 3D ultrasound compared with surgical findings was 100%. The main clinical presentations were primary infertility (four cases) and recurrent miscarriage (four cases). All patients underwent hysteroscopic septoplasty under laparoscopic guidance based on 3D ultrasound findings. Postoperative 3D ultrasound at three months showed a largely restored uterine cavity with a residual septum <1 cm in all cases. Conclusions: 3D ultrasound provides a non-invasive, intuitive, and accurate method for diagnosing Robert’s uterus by clearly displaying its characteristic anatomy in the coronal plane. It is an ideal tool for preoperative diagnosis, surgical planning, and postoperative assessment. 3D ultrasound should be considered a first-line imaging modality for the evaluation of suspected uterine anomalies, especially in women with unexplained infertility or recurrent pregnancy loss.
Liver transplantation for pediatric liver tumors: a case report of hepatic angiosarcoma with literature review
Background: Pediatric hepatic angiosarcoma (HAS) is poorly understood. The lack of effective therapies for unresectable pediatric liver tumors creates a clinical imperative, positioning liver transplantation (LT) as a pivotal modality for exploring the roadmap to prolonged survival. Case Description: We report the first Chinese pediatric case of HAS treated with LT. A 2-year-old boy was admitted to hospital due to abdominal pain and distension. Imaging findings were suggestive of possible malignancy, but initial biopsy yielded a diagnosed infantile hepatic hemangioma (IHH). Following living-donor LT for unresectable disease, explant pathology revealed an angiosarcoma arising within a hemangioma. The patient ultimately died from metastasis 21 months after LT. Our review of pediatric LT for liver tumors over 15 years identified nine pediatric HAS cases, only one of whom was correctly diagnosed pre-LT, with survival ranging from 3 to 66 months. Regarding other tumors, six cases of IHH were reported prior to 2010, without survival data. Recent studies on hepatoblastoma (HB) report a 5-year overall survival (OS) of 80–90%, and the prognosis for hepatocellular carcinoma (HCC) has markedly improved, with a combined 5-year OS of 74%. In the largest reported study, the 5-year OS rates for hepatic epithelioid hemangioendothelioma (HEH) and hepatic undifferentiated embryonal sarcoma (HUES) were 60.6% and 90.0%, respectively. Evidence for biliary tract rhabdomyosarcoma (BT-RMS) and malignant rhabdoid tumor (MRT) of the liver are anecdotal, and no pediatric cases of primary hepatic kaposiform hemangioendothelioma (KHE) treated with LT have been reported. Conclusions: Pediatric HAS is rare and difficult to distinguish from IHH; contrast-enhanced computed tomography/magnetic resonance imaging (CT/MRI) aids differentiation. For suspected malignant transformation, contrast-enhanced ultrasound (CEUS) and ultrasound-guided multi-site biopsy are recommended. No standardized treatment exists for pediatric HAS. Given the poor long-term survival, LT or combined chemotherapy remains exploratory but is the only option for unresectable tumors. The high survival rates of LT for HB and HCC offer a promising blueprint. With advances in surgical techniques and increased living donor availability, pediatric LT deserves more attention.
Contrast-enhanced ultrasound-guided precision fibrinolysis for refractory loculated chylothorax after lung transplantation: a case report
Background: Pleural complications remain a major source of morbidity after lung transplantation, with chylothorax posing particular therapeutic challenges. When complicated by fibrinous septation, effective drainage becomes difficult, often necessitating intrapleural fibrinolytic therapy (IPFT). However, in the early post-transplant period, blind fibrinolysis carries substantial risks, including hemorrhage and disruption of fragile bronchial anastomoses or lymphatic vessels. Case Description: We report a 31-year-old woman with pulmonary lymphangioleiomyomatosis (PLAM) who developed refractory loculated chylothorax 39 days after bilateral lung transplantation. Initial chemical pleurodesis was ineffective and subsequently induced a honeycomb-like, non-communicating pleural effusion that was not amenable to conventional drainage. To balance the need for septation lysis against the risk of bleeding, contrast-enhanced ultrasound (CEUS) was integrated as a real-time guidance tool. CEUS facilitated the precise differentiation between avascular fibrin septa and vascularized pleural tissue, allowing targeted low-dose urokinase injection into isolated locules. The restoration of inter-locule communication was directly visualized, permitting early termination of fibrinolytic exposure. Subsequent drainage and repeat pleurodesis resulted in full lung re-expansion without recurrence. Conclusions: To our knowledge, this is the first case utilizing CEUS to guide precision fibrinolysis in a lung transplant recipient. This case provides a proof-of-concept that CEUS-guided precision fibrinolysis can transform a traditionally blind and high-risk intervention into a controlled, visualization-driven procedure. By enabling targeted intervention and real-time efficacy assessment, this approach offers a safer salvage strategy for high-risk patients.
Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography
Background and Objective: Pulmonary embolism (PE) is a common life-threatening cardiovascular disorder. Computed tomography pulmonary angiography (CTPA) is the diagnostic reference standard, but iodinated contrast use may be limited by renal insufficiency, allergy, or other patient factors. Non-contrast computed tomography (NCCT) avoids contrast administration but has lower intrinsic diagnostic accuracy. This narrative review summarizes artificial intelligence (AI) applications for PE assessment across CTPA and NCCT, compares their clinical roles, and identifies evidence gaps for translation. Methods: A structured literature search was conducted across PubMed, Google Scholar, Web of Science, and IEEE (Institute of Electrical and Electronics Engineers) Xplore from database inception to June 2026. Search terms covered PE, CTPA, NCCT, AI, deep learning, machine learning, and related concepts. Eligible evidence included peer-reviewed original studies, validation studies, and peer-reviewed conference proceedings reporting AI methods, quantitative findings, or clinically relevant methodological insights. Key Content and Findings: AI for CTPA has progressed from computer-aided detection toward segmentation, clot burden quantification, workflow triage, and risk stratification, with encouraging performance in selected retrospective and real-world studies. AI for NCCT remains exploratory and includes indirect sign analysis, direct detection, synthetic contrast generation, and cross-modality learning. Major limitations include small heterogeneous datasets, limited multicenter validation, suboptimal subsegmental PE detection, and uncertain lesion-level safety of synthetic imaging. Conclusions: AI may improve diagnostic efficiency and support radiologist interpretation, but it should remain an augmentative tool rather than an autonomous replacement for clinical expertise. CTPA-based AI has more mature evidence for central PE detection and workflow triage, whereas NCCT-based approaches require rigorous validation before broad clinical implementation. Priorities include multicenter datasets with vessel-level annotations, direct comparison of NCCT strategies under common reference standards, and lesion-level validation of synthetic imaging safety.
Artificial intelligence in assisted localization of fetal ultrasound standard planes: a narrative review
Background and Objective: Ultrasound imaging has become a vital clinical diagnostic tool due to its cost-effectiveness and absence of ionizing radiation. In fetal ultrasound, the precise localization of standard ultrasound planes is a prerequisite for subsequent quantitative analysis and diagnosis. However, manual identification is highly operator-dependent and subjective, and the multi-planar data acquired by three-dimensional (3D) fetal ultrasound present challenges for efficient localization within large datasets. The objective of this narrative review is to systematically summarize the research progress and methodologies of artificial intelligence (AI) in the automated and precise localization of fetal ultrasound standard planes, while discussing the current challenges and future directions for its clinical application. Methods: We conducted structured literature search in the PubMed and Web of Science databases to identify studies applying AI to ultrasound imaging, with a primary focus on fetal standard plane recognition. The search focused on all types of publications involving AI-based standard plane recognition in ultrasound between 1998 and 2025. Only peer-reviewed articles and review papers published in English were included. Titles and abstracts were screened to determine eligibility. Key Content and Findings: This review details the transformative impact of AI, particularly deep learning, on automating standard plane localization in ultrasound, with a primary emphasis on fetal applications. Key findings reveal a progression from handcrafted features to advanced architectures like convolutional neural network and reinforcement learning, achieving expert-level accuracy across fetal standard planes, including abdominal, facial, brain, and cardiac views, while representative adult applications (e.g., hepatobiliary and thyroid imaging) are included as supplementary examples. The review identifies technical challenges, including data variability and computational costs, which are being addressed via transfer learning, attention mechanisms, and lightweight network design. The emergence of multi-organ models highlights a trend towards comprehensive AI systems for holistic screening. Conclusions: AI significantly advances ultrasound by automating standard plane localization, particularly in fetal ultrasound, enhancing diagnostic consistency and workflow efficiency. Integration into clinical practice promises to reduce operator dependency and improve screening accessibility. Persistent challenges include model generalizability, data scarcity, and real-world clinical integration. Future work should prioritize multi-center validation, explainable AI, and end-to-end system development to fully realize this technology’s potential.
“Left-dominant” azygos system: transvertebral confluence, absent right arch, and drainage into the left brachiocephalic vein—a case description
Spatiotemporal image correlation combined with high-definition live rendering mode and tomographic ultrasound imaging for evaluating fetal cardiac hemangioma in the right atrium: a case description
Prenatal diagnosis and integrated perinatal management of isolated type I membranous jejunal atresia: a case description
Minimally invasive surgical extraction of an impacted supernumerary tooth in right maxilla with use of a 3D-printed surgical guide and mixed reality: a case description
Methodological considerations in bibliometric retrieval for ultrasound-derived skeletal muscle research
Seven-year sonographic evolution from a simple vaginal wall cyst to hepatoid adenocarcinoma of the urethrovaginal septum
Prenatal magnetic resonance imaging differentiation between fetal subcutaneous fat thickening in macrosomia and subcutaneous edema: a description of two cases
Post-traumatic neuroma mimicking lymph node metastasis on 18F-FDG PET/CT in lung cancer: the value of inter-lesional metabolic discordance
Computed tomography peritoneography: one-stop diagnosis and treatment reference for multiple combined complications in peritoneal dialysis patients
Air embolism occurred during preoperative localization for pulmonary resection: a case description
A three-dimensional-printed model from ultrasound volumes: a new tool for the prenatal diagnosis of frontonasal dysplasia