Quantitative Imaging in Medicine and Surgery
Aims and Scope Quantitative Imaging in Medicine and Surgery (QIMS, Quant Imaging Med Surg, Print ISSN 2223-4292; Online ISSN 2223-4306) publishes peer-reviewed original reports and reviews in medical imaging, including X-ray, ultrasound, computed tomography, magnetic resonance imaging and spectroscopy, nuclear medicine and related modalities, and their application in medicine and surgery. While focus is on clinical investigations, papers on medical physics, image processing, or biological studies which have apparent clinical relevance are also published. This journal encourages authors to look at the medical images from a quantitative angle. This journal also publishes important topics on imaging-based epidemiology, and debates on research methodology, medical ethics, and medical training. Descriptive radiological studies of high clinical importance are published as well. Published quarterly from Dec. 2011 to Dec. 2012 and bimonthly from Feb. 2013 to Feb 2018, QIMS now follows a monthly publication model. QIMS is indexed in PubMed/PubMed Central, Scopus and Web of Science [Science Citation Index Expanded (SCIE)]. Its latest impact factor is: 2.8. QIMS is published by AME Publishing Company, with support from Wolters Kluwer (the journal content will be made available on Wolters Kluwer Health’s Lippincott and Ovid platforms). Indexing Quantitative Imaging in Medicine and Surgery is indexed and covered by Web of Science [Science Citation Index Expanded (SCIE)] PubMed PubMed Central (PMC) Google Scholar Scopus Ovid Information for Authors QIMS is a member of Committee on Publication Ethics (COPE) and it follows the Committee on Publication Ethics (COPE)'s guidelines and the ICMJE recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journal. Manuscripts submitted must be the original work of the author(s) and must not be published previously or under consideration for publication elsewhere. Manuscripts Turnaround Time First Editorial Decision: 3-5 days. Peer review: 1-2 months. Revision time: 2-4 weeks. Publication Ahead of Print: within 1 month after being accepted (with APC payment received). Formal publication: within 1-3 months after being accepted (with APC payment received). Original Articles are listed as priority. QIMS’s position on case reports and review articles. QIMS welcomes case reports where quantitative imaging played a role for diagnosis and/or treatment; also welcome first time realization (in animals or in human subjects) of a new imaging technique. These case reports are usually written a concise and short letter format (see <https://qims.amegroups.com/>) for example. Case reports of particular clinical importance are also published in a longer format; for these cases we expect important pathophysiological, diagnostic, therapeutic implications. We do not publish case report only because of the rarity of the cases. Note, although we believer reporting case materials is important for the advancement of medicine. The space reserved for case report remains limited for each issue. The decision to publish or not publish a case material submission can sometimes depend on the available space of the journal. QIMS welcomes reviews and comments on published papers. Review papers should contain authors’ analytical appraisal of published papers and personal viewpoints, instead of a mere aggregation of published abstracts. We expect review papers are in three forms, 1) expert reviews, usually published in editorial format, provide authors’ own insights and perspective; 2) systematic review; 3) educational reviews, including pictorial reviews. Systematic reviews (maybe narrative in writing but critical in nature) are particularly welcomed. Publication Schedule Published quarterly from Dec. 2011 to Dec. 2012 and bimonthly from Feb. 2013 to Feb 2018, QIMS now follows a monthly publication model. Open Access Statement This journal is a peer reviewed, open access journal. All content of the journal is published under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). All articles published open access will be immediately and permanently free for all to read, download, copy and distribute as defined by the applied license. Free access and usage Permitted third party reuse is defined by the CC BY-NC-ND 4.0 license. This license allows users to copy and distribute the article, provided: this is not done for commercial purposes and further does not permit distribution of the Article if it is changed or edited in any way. the user gives appropriate credit (with a link to the formal publication through the relevant DOI) and provides a link to the license but not in an any way implying that the licensor is endorsing the user or the use of the work. no derivatives including remix, transform, or build upon the material was allowed for distribution. The full details of the license are available at https://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright For open access publishing, this journal uses an exclusive licensing agreement. Authors will transfer copyright to AME Publishing Company, but will have the right to share their article in the same way permitted to third parties under the relevant user license, as well as certain scholarly usage rights. For any inquiry/special circumstance on the copyright, commercial usage or adaptation of QIMS articles, please contact: permissions@amegroups.com For reprint order, please contact: sales@amegroups.com Support for Authors to Comply with Funding Body Mandates We work with authors of research articles supported by funding bodies with open access mandates to ensure that authors can meet their funders’ requirements for public access to research results. In addition, we offer further support for authors who are required to comply with funding body mandates, including but not limited to: All articles published open access will be immediately and permanently free for everyone to read, download, copy and distribute. If a specific open access license is needed, please contact the editorial office for confirmation before submission. Example of statements in a published article: https://jgo.amegroups.org/article/view/76355/html. No copyright is claimed for any work of the U.S. government. Example of statements in published articles: https://actr.amegroups.org/article/view/8875; https://med.amegroups.org/article/view/8358/html. Contact with Editorial Office Suki Tang, Corresponding Editor, Quantitative Imaging in Medicine and Surgery Email: qims@amepc.org Publisher Information QIMS is published by AME Publishing Company. 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
Free-breathing, blood-flow-independent, three-dimensional relaxation-enhanced angiography without contrast and triggering (REACT) for aorta measurement and diagnosis in patients with aortic disease: a comparison with computed tomography angiography
Background: Patients with aortic diseases require precise quantitative assessment and long-term regular follow-up monitoring. Computed tomography angiography (CTA) is widely used for evaluation; however, its application is limited by ionizing radiation exposure and iodinated contrast administration. This study aimed to evaluate the feasibility and accuracy of a novel technique—free-breathing, blood-flow-independent, three-dimensional (3D), relaxation-enhanced angiography without contrast and triggering (REACT)—for the assessment of aortic disease as an alternative to CTA. Methods: A total of 101 patients were included in this single-center prospective study, comprising 54 patients with aortic aneurysm (AA), 30 patients with aortic dissection (AD), and 17 patients who had previously undergone endovascular aortic repair (EVAR). All the patients underwent both REACT and CTA examinations. An intraindividual assessment was performed by measuring aortic diameters at 11 levels. Overall image quality was evaluated independently by three readers using a 5-point scale. Additionally, for AA, AD, and EVAR, diagnostic confidence using different criteria was assessed. Differences and consistency between REACT and CTA were analyzed. Results: There was no significant difference in aortic diameter assessment between REACT and CTA (all P>0.05). REACT had lower overall image quality than CTA (3.77±0.52 vs. 4.29±0.57, P<0.001). REACT showed comparable diagnostic confidence to CTA for the assessment of aneurysm morphology and intraluminal thrombus (ILT). However, REACT showed lower diagnostic confidence than CTA for AD and EVAR evaluation. Interobserver consistency was high for the two imaging methods in subjective scoring (range, 0.67–0.88). Conclusions: REACT can accurately display aortic morphology and is valuable for diagnosing aortic disease, especially AA. It could serve as an alternative to CTA for repeated aortic imaging.
MDIFN: a deep learning–based multi-dimensional interaction fusion network for placental MRI segmentation
Background: The placenta serves as a vital channel for maternal–fetal substance exchange and is a crucial organ for maintaining pregnancy. Placental diseases can severely affect maternal health and fetal growth. Given the limitations of single-dimension segmentation models and traditional fusion strategies in meeting the demands of automatic placental magnetic resonance imaging (MRI) segmentation, this study developed an automatic placental MRI segmentation method based on a multi-dimensional interaction fusion network (MDIFN). Methods: This study included 267 placental MRI cases that met the inclusion criteria. After preprocessing steps, such as bias field correction, resampling, intensity normalization, and dimension matching, a dual-branch foundational architecture combining two-dimensional UNet (2D-UNet) and three-dimensional UNet (3D-UNet) was constructed. An interaction fusion bidirectional gated cross-dimension module was designed to achieve layer-wise synergistic fusion of two-dimensional (2D) detail features and three-dimensional (3D) contextual features. A global context recalibration module and a residual refinement module were introduced to optimize feature quality. A homoscedastic uncertainty-based multi-task weighted loss function was employed to balance the training process. Model performance was compared against six mainstream models using 10-fold cross-validation, and ablation studies were conducted to validate the contribution of core modules. Results: The model achieved a Dice coefficient of 0.8749±0.0132 (P<0.05) on the test set, representing an improvement of 16.2% compared to pure 2D-UNet and 8.4% compared to pure 3D-UNet. The sensitivity was 0.8999±0.0172 (P<0.05), and the F1 score was 0.8749±0.0132, which were the optimal values among all models. The relative volume difference was 0.0301±0.0173, and the 95% Hausdorff distance was 7.67±1.04. Ablation experiments showed that removing the gating mechanism, global context module, or bidirectional interaction reduced the Dice coefficient to 0.8554, 0.8551, and 0.8566, respectively, confirming that each component contributes to performance improvement. Conclusions: This study addresses the limitations of single-dimension segmentation and traditional fusion models, providing an efficient and objective technical approach for automatic placental segmentation.
Deep meta-learning framework with U-Net segmentation and optimization-based feature selection for breast cancer detection in MRI images
Background: Breast cancer is one of the most prevalent cancers worldwide and continues to pose significant challenges to global healthcare systems. The survival rate of patients largely depends on early detection, accurate diagnosis, and timely treatment. Medical imaging techniques such as mammography, ultrasound, and magnetic resonance imaging (MRI) are widely used for detecting abnormalities in breast tissue. However, many existing breast cancer detection approaches suffer from limitations such as high computational complexity, long tumor detection time, inaccurate predictions, and high false-positive rates. These challenges highlight the need for more reliable and efficient automated diagnostic systems. The aim of this study is to develop an efficient deep meta-learning framework for accurate breast cancer detection and classification using MRI images. The proposed system integrates preprocessing, tumor segmentation, feature extraction, optimization, and deep learning models to improve diagnostic accuracy, reduce false-positive rates, and support early clinical decision-making in computer-aided diagnosis systems. Methods: This work proposes a deep meta-learning framework for precise breast cancer detection and classification using MRI images. The framework integrates multiple stages to improve diagnostic accuracy. Initially, Adaptive Median Filtering (AMF) is applied to enhance image quality and remove noise during preprocessing. Subsequently, U-Net-based segmentation is used to accurately identify tumor regions. Growth Distribution Depth (GDD) is employed to extract discriminative features from the segmented regions, and Salp Swarm Optimization (SSO) is used to select the most relevant features. Finally, deep meta-learning-based classification is performed using pre-trained convolutional neural network (CNN) models, including AlexNet, VGG16, and LeNet, which are fine-tuned on breast cancer datasets to improve classification performance. Results: Experimental results demonstrate that the proposed framework achieves superior performance compared to existing methods. Among the evaluated models, the VGG16-based classifier shows the best performance, achieving an accuracy of 98.82%, precision of 97.1%, recall of 99.11%, and an F1-score of 98.25%. The integration of tumor-focused segmentation and optimization-based feature selection significantly improves classification accuracy and reduces false detections. Conclusions: The proposed deep meta-learning framework provides an effective and reliable approach for automated breast cancer detection from MRI images. By combining advanced preprocessing, segmentation, feature extraction, optimization, and deep learning classification techniques, the framework enhances diagnostic accuracy and efficiency. The results suggest that the proposed method has strong potential to support computer-aided diagnosis systems and assist clinicians in early breast cancer detection.
Comparative accuracy of mean pulmonary artery pressure estimation using transthoracic echocardiography versus phase-contrast MRI in healthy adults
Background: Non-invasive assessment of mean pulmonary artery pressure (MPAP) is crucial for clinical screening and longitudinal follow-up of pulmonary arterial hypertension (PAH). Transthoracic echocardiography (TTE) acts as the initial non-invasive modality, though it presents limited reliability in MPAP estimation. Phase-contrast magnetic resonance imaging (PC-MRI) allows direct and quantitative hemodynamic measurement, yet its comparative accuracy in healthy adults remains insufficiently explored. This study aimed to evaluate the accuracy of MPAP estimation in healthy adults using TTE compared to PC-MRI, with relevance to non-invasive assessment in settings such as pulmonary hypertension. Methods: Twenty healthy volunteers underwent imaging with 3.0 Tesla PC-MRI to assess the main pulmonary artery (MPA) cross-sectional area (CSA) and forward peak velocity. MPAP was calculated using a previously validated formula. Within 48 hours, the same participants underwent TTE to obtain heart rate-adjusted pulmonary artery acceleration time (PAAT), from which MPAP was derived. The reference standard for MPAP was based on values obtained via right heart catheterization (RHC) in healthy adults. Agreement was defined as estimated MPAP falling within the 95% confidence interval (CI) of the RHC reference values. Between-modality differences were assessed using a paired t-test, with a two-tailed P value <0.05 considered statistically significant. Results: All imaging studies were of diagnostic quality. MPAP estimated using PC-MRI (14.08±2.95 mmHg) fell within the 95% CI of the reference standard for all participant (100%). In contrast, TTE-derived MPAP (6.70±12.89 mmHg) demonstrated agreement in only 9 participants (45%), with 6 participants (30%) exhibiting non-physiological negative values. The difference in estimation agreement with established reference ranges between the two modalities was statistically significant (P<0.01). Mean deviation from the reference mean (14.0 mmHg) was +0.08 mmHg for PC-MRI and −7.30 mmHg for TTE. Bland-Altman analysis showed a mean bias of 7.38 mmHg (95% CI: 1.28 to 13.48 mmHg) with wide limits of agreement (−13.76 to 28.52 mmHg), indicating poor interchangeability. Conclusions: PC-MRI demonstrated superior agreement with established reference ranges and reproducibility compared to TTE for non-invasive MPAP measurement in healthy adults. The TTE-based PAAT method exhibited limited reliability in this population, frequently producing implausible results. PC-MRI may serve as a reliable non-invasive modality for quantitative evaluation of pulmonary artery pressure.
Exploring key factors affecting stone retention rates in percutaneous nephrolithotomy from a three-dimensional structural perspective: a retrospective study
Background: Percutaneous nephrolithotomy (PCNL) is a mainstream minimally invasive strategy for renal calculi. Despite widespread use, residual calculi after PCNL remain a key clinical challenge. Precise preoperative evaluation of renal collecting system anatomy and rational planning of the percutaneous access route are critical for improving stone-free rates. This study aimed to explore whether morphological features of the target calyx can predict stone clearance from a non-target puncture calyx and establish a model for assessing residual stone risk. Methods: A retrospective study was conducted including 105 PCNL cases (67 stone-free, 38 with residual stones) with distinct puncture and target calyces between January 2023 and June 2025. Three-dimensional (3D) models were reconstructed from preoperative computed tomography (CT) urography to measure spatial anatomical parameters. Patient characteristics, stone features, and 3D metrics were entered into binary logistic regression to screen independent predictors and construct a nomogram. Model performance was evaluated using receiver operating characteristic (ROC), calibration curves, decision curve analysis (DCA), and internal validation (n=29). Results: Three independent predictors were identified: punctured calyx length-to-width ratio [odds ratio (OR) =6.89, P=0.020], inter-calyceal axis angle (OR =0.929, P<0.001), and target calyx length-to-width ratio (OR =8.925, P=0.003). The model showed excellent discrimination with an area under the curve (AUC) of 0.905, sensitivity of 86.8%, and specificity of 80.6% at the optimal cutoff of 0.291. The Hosmer-Lemeshow test indicated good calibration (χ2=12.467, P=0.132). Calibration and DCA confirmed favorable accuracy and clinical utility. Internal validation yielded an AUC of 0.829, verifying model stability and reliability. Conclusions: A predictive model combining calyceal length-to-width ratios and inter-calyceal axis angle can effectively predict stone clearance in the target calyx. This model provides a valuable reference for optimizing calyx selection and improving stone-free outcomes in PCNL.
Analysis of CT and MRI imaging features of ectopic pancreas
Background: Ectopic pancreas (EP) is a rare congenital anomaly that is often difficult to diagnose preoperatively due to its nonspecific clinical manifestations and imaging overlap with other gastrointestinal subepithelial lesions. Several characteristic imaging findings have been reported; however, the quantitative imaging features of EP remain insufficiently investigated. Therefore, this study aimed to characterize the clinical and imaging manifestations of EP, summarize its computed tomography (CT) features, quantitatively compare CT attenuation values between EP and native pancreas across different imaging phases, and explore phase-specific quantitative differences that may provide additional imaging information for EP characterization. Available magnetic resonance imaging (MRI) findings were also analyzed descriptively for supplementary illustration. Methods: The clinical and imaging data of 17 patients with surgically confirmed EP were retrospectively reviewed. All patients underwent non-contrast CT, 13 underwent triphasic contrast-enhanced CT, and two underwent non-contrast MRI. The imaging characteristics of benign-appearing lesions representing EP tissue were evaluated, and exploratory qualitative and quantitative comparisons were performed between the EP lesions and the native pancreatic tissue. Results: EP was most frequently located in the duodenum (52.9%, 9/17) and stomach (35.3%, 6/17), typically presenting as solitary ovoid lesions (52.9%, 9/17) with ill-defined margins (64.7%, 11/17) and a long diameter-to-short diameter ratio >1.4 in 64.7% (11/17) of cases. The following three imaging signs were observed in the 13 patients who underwent contrast-enhanced CT: the duct sign (5/13 cases, 38.5%), the umbilical sign (1/13 cases, 7.7%), and the perilesional fat line sign (1/13 cases, 7.7%). On the non-contrast CT and arterial-phase images, EP showed lower attenuation than the native pancreas [non-contrast: 45.18±2.91 vs. 48.59±2.90 Hounsfield units (HU), corrected P=0.002; arterial phase: 76.38±15.12 vs. 95.46±7.34 HU, corrected P=0.002]; however, no statistically significant differences were observed during the venous or delayed phases. In the exploratory analysis, arterial-phase CT attenuation showed the largest attenuation difference and the highest area under the curve (AUC) among the evaluated phases in this cohort. Conclusions: EP demonstrates characteristic morphological features and phase-specific quantitative CT attenuation patterns. In this small cohort, arterial-phase CT attenuation showed the greatest quantitative difference relative to the native pancreas among the evaluated phases and may provide supportive information for EP identification when interpreted alongside specific CT signs. These results should be interpreted with caution.
The role of glymphatic-related structural and functional imaging biomarkers in the pathological progression of Alzheimer’s disease: a multimodal imaging-based study
Background: The comparative utility of choroid plexus volume (CPV) and the diffusion tensor imaging-based perivascular space (DTI-ALPS) index as glymphatic biomarkers across the Alzheimer’s disease (AD) continuum is unclear. This study aimed to perform a head-to-head comparison of their relationships with AD pathology, cognition, and diagnostic performance. Methods: This study analyzed data from 848 AD Neuroimaging Initiative (ADNI) participants [426 cognitively normal (CN), 309 with mild cognitive impairment (MCI), and 113 with AD dementia]. Group differences in CPV and the ALPS index were assessed using generalized linear models (GLM), and their associations with AD biomarkers and cognition were examined via partial correlation. Diagnostic performance using logistic regression, and longitudinal predictive value was assessed with linear mixed models. Results: Compared with the CN and MCI groups, patients with AD exhibited significantly increased CPV and reduced ALPS indices (all P<0.001). Amyloid-β-positive (Aβ+) participants also showed significantly higher CPV and lower ALPS indices than Aβ-negative (Aβ−) individuals (all P<0.001). Elevated CPV was associated with lower cerebrospinal fluid (CSF) Aβ42 (r=−0.15, P<0.001), poorer Mini-Mental State Examination (MMSE; r=−0.12, P<0.001), Montreal Cognitive Assessment (MoCA; r=−0.17, P<0.001), and reduced hippocampal volume (r=−0.21, P<0.001). Conversely, higher ALPS indices were associated with increased CSF Aβ42 (r=0.12, P<0.001), better MMSE (r=0.13, P<0.001), and preserved hippocampal volume (r=0.15, P<0.001). CPV achieved superior diagnostic performance for differentiating AD from MCI [area under the curve (AUC) =0.774] and CN (AUC =0.912). Moreover, integrating CPV, ALPS, and hippocampal volume further improved classification performance across diagnostic and Aβ stratification tasks. Longitudinal analyses demonstrated that higher baseline CPV was associated with better baseline cognitive performance and slower decline in executive, language, and memory functions, whereas lower baseline ALPS indices predicted accelerated memory decline over time. Conclusions: Our findings identify CPV and the ALPS index as a pair of promising, complementary neuroimaging biomarkers for AD. Their synergistic integration into diagnostic models improves the precision of early detection and intervention strategies.
Clinical evaluation of the effectiveness of laser localization system in CT-guided percutaneous lung puncture positioning: a prospective randomized controlled pilot study
Background: Computed tomography (CT)-guided percutaneous lung biopsy localization plays a pivotal role in the diagnosis and management of thoracic diseases. The present study aimed to investigate and compare a laser localization system with traditional puncture technology, focusing on their accuracy and safety in clinical practice. Methods: A total of 120 patients who required preoperative CT-guided percutaneous lung nodule localization between October 2023 and October 2024 were prospectively enrolled. Among them, 60 patients who underwent puncture under CT guidance with the assistance of a laser locator were assigned to the observation group, and 60 patients who received conventional CT-guided puncture were allocated to the control group. Adverse reactions related to percutaneous lung puncture were recorded according to the Common Terminology Criteria for Adverse Events (CTCAE 5.0). The puncture time, average number of CT scans, one-time puncture success rate, and incidence of complications were compared between the two puncture methods. Results: The average diameter of lung nodules in the observation group was 0.85±0.38 cm, with a one-time puncture success rate of 93.3% (56/60); in the control group, the average diameter of lung nodules was 0.87±0.31 cm, and the one-time puncture success rate was 83.3% (50/60). Compared with the control group, the observation group had fewer average CT scans (3.53±1.40 vs. 5.80±1.56 times), shorter puncture time (7.63±3.51 vs. 11.00±5.31 min), and lower radiation dose (45.81±12.41 vs. 65.28±15.16 mSv), with statistically significant differences (all P<0.05). When comparing the puncture errors of nodules in different lung lobe locations between the two groups, the results showed that in the observation group, the puncture error for lower lobe nodules was significantly greater than that for non-lower lobe nodules (2.48±0.65 vs. 1.90±0.31 mm, P<0.05); similarly, in the control group, the puncture error for lower lobe nodules was also greater than that for non-lower lobe nodules (3.61±1.72 vs. 2.58±0.52 mm, P<0.05). Conclusions: The laser locator-assisted CT-guided percutaneous lung biopsy yields favorable procedural outcomes, with shorter operation time, fewer repeated scans and lower radiation exposure. Although no significant difference was found in the one-time puncture success rate versus the conventional method, this auxiliary localization system shows promising clinical value. Further large-scale multicenter trials are required to validate its efficacy and routine clinical applicability.
Artificial intelligence model based on the detection of key anatomical structures for identifying standard planes on ultrasound of the first-trimester fetal central nervous system: a multicenter study
Background: Prenatal ultrasound examination at 11+0 to 13+6 weeks’ gestation can detect approximately 33–50% of major fetal structural abnormalities. Despite recent advances in artificial intelligence (AI) for fetal ultrasound, its application in examining the first-trimester fetal central nervous system (FCNS) remains limited. This study aimed to develop, validate, and determine the clinical value of an AI FCNS model (FCNS-Model) based on the detection of key anatomical structures with the purpose of identifying FCNS ultrasound standard planes (FCNS-USPs). Methods: This study included 2,901 FCNS-USPs images from three hospitals, which were split into a training set (2,124 images), a test set (264 images), an internal validation set (254 images), and a clinical validation set (259 images). Additionally, an independent external validation set (300 images) was formed from images acquired at a fourth hospital. The performance of junior, intermediate, and senior physicians, as well as that of the FCNS-Model, was compared against the standard of an expert ultrasound team. Results: In the test set, the model achieved precision, recall, and F1 scores above 92%. For the internal validation set, classification accuracy for each plane was 99.3–100%, with kappa values >0.90, indicating strong agreement with the expert team. In the clinical validation set, FCNS-Model achieved significantly higher areas under the curve (AUCs) than did the senior physician for the anterior-posterior midsagittal plane (0.984 vs. 0.906; P<0.001), the transverse plane through the thalamus (0.982 vs. 0.938; P=0.003), and the coronal plane through the frontal lobe (0.915 vs. 0.852; P=0.03). For the transverse plane of the lateral ventricle, the FCNS-Model’s AUC of 0.979 was slightly higher than that of the junior physician (AUC =0.971; P=0.042). Furthermore, McNemar testing demonstrated that the FCNS-Model achieved significantly higher sensitivity than did the junior physician in identifying FCNS-USPs (P=0.02). In the external validation set, the model maintained stable performance despite a moderate decrease, supporting its generalizability. Conclusions: FCNS-Model accurately identifies FCNS-USPs and is an effective tool for assisting ultrasound physicians in obtaining standard planes and identifying key anatomical structures, particularly in settings with varying levels of operator experience.
Feasibility of predicting breast cancer Ki-67 expression using histogram features derived from time-dependent diffusion magnetic resonance imaging
Background: Through the quantification of multiple microstructural parameters, time-dependent diffusion magnetic resonance imaging (Td-dMRI) offers a novel approach for establishing urgently needed imaging biomarkers of tumor heterogeneity. This study employed Td-dMRI-derived histogram parameters to predict Ki-67 expression in breast cancer. Methods: A total of 86 female patients with breast cancer were enrolled in this prospective study. Microstructural parameters, including mean cell diameter (d), intracellular volume fraction (fin), intracellular diffusion coefficient (Din), extracellular apparent diffusion coefficient (Dex), and intracellular water pre-exchange lifetime (Tauin), were estimated using the JOINT model based on Td-dMRI data acquired with pulsed (PGSE) and oscillating (OGSE) gradient spin-echo sequences. Two additional parameters, intracellular water exchange rate (Kin) and cellularity, were subsequently derived through calculation. The apparent diffusion coefficient (ADC) was calculated from the conventional diffusion-weighted imaging (DWI), PGSE (32 and 52 ms), and OGSE (17 and 33 Hz) sequences. Histogram features were extracted based on parameters derived from Td-dMRI. The independent t-test or Mann-Whitney U test was used to compare differences between Ki-67 groups. The dataset was divided into a training set and test set at a ratio of 7:3 for internal validation. Clinical and conventional imaging characteristics identified by univariate regression analysis (P<0.1), together with histogram features selected by least absolute shrinkage and selection operator (LASSO) regression, were used to further establish a logistic regression model in the training set. Model performance was assessed by receiver operating characteristic (ROC) curves in the training and test sets. The area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Results: Nine histogram parameters differed significantly between the Ki-67 expression groups; however, no significant difference in ADCDWI was observed (P=0.859). Axillary lymph node metastasis (ALNM) was identified as an independent predictor of high Ki-67 expression (odds ratio (OR) =2.991; 95% confidence interval (CI): 0.964–9.278, P=0.058). Among the eight histogram features ultimately selected by LASSO regression, Cellularity_P10 and ADC33Hz_Skewness contributed the most to the model. The combined model achieved an AUC of 0.890 (95% CI: 0.809–0.971), with a sensitivity, specificity, and accuracy of 89%, 65%, and 80%, respectively, in the training set, and an AUC of 0.870 (95% CI: 0.712–1), with a sensitivity, specificity, and accuracy of 80%, 50%, and 73%, respectively in the test set. Conclusions: Td-dMRI-derived histogram features show promise for predicting Ki-67 expression in breast cancer and may complement conventional imaging.
Respiratory motion-corrected quiescent period gating magnetic resonance attenuation correction (Q.MRAC) for enhancing quantitative accuracy in hepatic [18F]FDG PET/MRI: a dual-domain motion compensation study
Background: Respiratory motion-induced misalignment between positron emission tomography (PET) and magnetic resonance attenuation maps remains a critical challenge in abdominal PET/magnetic resonance imaging (PET/MRI) quantification. This study evaluated the performance of a novel dual-domain motion correction strategy combining emission-domain [quiescent phase gating PET acquisition (Q.Static)] and attenuation-domain [quiescent period gating magnetic resonance attenuation correction (Q.MRAC)] approaches in enhancing standardized uptake value (SUV) accuracy in hepatic fluorine-18 fluorodeoxyglucose ([18F]FDG) imaging. Methods: Fifty patients underwent [18F]FDG PET/MRI with two respiratory motion correction protocols: dual-domain correction (Q.Static + Q.MRAC) and conventional MRAC [conventional static PET acquisition (Static) + conventional magnetic resonance attenuation correction (C.MRAC)]. Three 3-cm-diameter spherical volumes of interest (VOIs) were positioned in the right hepatic lobe, including the upper lobe (VOI1), middle lobe (VOI2), and lower lobe (VOI3) to evaluate SUV quantification, spatial alignment of attenuation maps against end-expiratory T2-weighted imaging (T2WI), and motion correction efficacy. Analysis of variance was applied for comparisons of the mean SUV (SUVmean) across VOIs and protocols, the Kruskal-Wallis test to compare the differences in SUVmean, paired t-tests to analyze alignment metrics, and the intraclass correlation coefficient (ICC) to measure interobserver consistency. Results: VOI2 demonstrated minimal SUV variability [coefficient of variation (CV) =2.29–2.55%], validating its use as a reference region as per the relevant guidelines. Respiratory motion caused consistent SUV underestimation: VOI1 underestimation relative to VOI2 was 16.4% for Q.Static + Q.MRAC and 26.3% for Static + C.MRAC, while the maximum underestimation rate for VOI3 was 11.4% for Static + C.MRAC. Q.Static + Q.MRAC reduced VOI1 underestimation by 38.9% as compared to Static + C.MRAC (P=0.006). Q.MRAC significantly improved anatomical alignment as compared to C.MRAC (P<0.001), reducing mismatch ratio by 48.7% and the mismatch area by 52.4% and 33.6% in the coronal and axial views, respectively. Eight percent of Q.MRAC acquisitions failed due to irregular breathing. Conclusions: Dual-domain motion correction (Q.Static + Q.MRAC) significantly improves SUV quantification accuracy in the upper lobe of right liver. These findings establish a foundation for achieving greater accuracy for tumor assessment and metabolic imaging in clinical practice through the use of [18F]FDG PET/MRI.
Predictive value of a nomogram based on multiparameter quantitative ultrasound for pregnancy outcomes in infertile women attempting natural conception
Background: Infertility affects many couples worldwide, and the accurate assessment of endometrial receptivity (ER) is critical. However, conventional ultrasound has limited predictive value. Multiparameter quantitative ultrasound is a promising tool for comprehensive evaluation, yet most studies focus on assisted reproduction populations, leaving its combined predictive value for natural conception unclear. This study aimed to explore its value for predicting pregnancy outcomes in infertile women attempting natural conception. Methods: Infertile women who presented to Shenzhen Second People’s Hospital from September 2023 to December 2024 were prospectively enrolled. Patients underwent transvaginal ultrasound examination during the mid-luteal phase. Evaluated parameters included endometrial thickness and pattern, direction and frequency of endometrial peristalsis, proportion of endometrial peristalsis duration, uterine artery blood flow characteristics, endometrial and subendometrial blood flow, endometrial volume (EV) and stiffness, and junctional zone (JZ) characteristics (thickness, blood flow, and stiffness). Clinical pregnancy was followed up, dividing patients into pregnancy and non-pregnancy groups. Group comparisons used independent t-test, Mann-Whitney U test, Chi-squared test, or Fisher’s exact test as appropriate. Least absolute shrinkage and selection operator (LASSO) regression selected predictors for nomogram construction. Model performance was assessed by receiver operating characteristic (ROC) curve with 500 bootstrap internal validations, calibration curve, and decision curve analysis (DCA). Results: A total of 102 infertile patients were enrolled, of which 22 comprised the pregnancy group and 80 the non-pregnancy group. The results of the study showed a statistically significant difference between both groups in terms of endometrial thickness, direction of endometrial peristalsis, proportion of endometrial peristalsis duration, number of endometrial and subendometrial vessels, endometrial and subendometrial vascular pattern, endometrial and subendometrial blood flow distribution, EV, endometrial vascularization index (VI), endometrial vascularization flow index (VFI), endometrial mean Young’s modulus (Emean), JZ-VI, JZ-VFI, EV/JZ volume (EV/JZ-V), and JZ-Emean (P<0.05). LASSO regression selected six important parameters related to pregnancy outcome: proportion of endometrial peristalsis duration, direction of endometrial peristalsis, EV, endometrial Emean, JZ-Emean, and JZ-VFI. The nomogram constructed on the basis of these parameters showed good predictive efficacy, with an area under the curve (AUC) of 0.862 [95% confidence interval (CI): 0.760–0.964], a sensitivity of 0.882, a specificity of 0.758, and an accuracy of 0.783. The calibration curve showed a good agreement (Brier score 0.066, Omnibus P=0.051), and the DCA showed good clinical usefulness. Conclusions: The nomogram, based on the proportion of endometrial peristalsis duration, direction of peristalsis, EV, endometrial Emean, JZ-Emean, and JZ-VFI, demonstrates good predictive performance. By integrating a multidimensional ‘structure-function-blood flow’ assessment, it provides an objective tool for individualized counseling and optimizing conception timing.
Qualitative shear wave elastography in malignant breast lesions: predictive factors associated with false-negative findings and their biological characteristics
Background: Shear wave elastography (SWE) complements B-mode ultrasound (BMUS) for breast lesion characterization. However, malignant breast lesions that exhibit false-negative findings on qualitative SWE lack the expected increase in stiffness, which may contribute to missed diagnosis and underestimation of disease severity. This study aimed to identify the BMUS characteristics and clinicopathologic predictors associated with false-negative findings on qualitative SWE in malignant breast lesions, and to characterize the tumor biological behavior of these false-negative lesions, including axillary lymph node (ALN) status, histologic grade, and molecular subtype. Methods: A total of 770 patients (with 770 malignant breast lesions) were enrolled in the study between March 2019 and August 2024. All patients underwent preoperative BMUS and SWE examinations. A multivariate logistic regression analysis was conducted to identify the predictive factors associated with false-negative SWE findings. The biological behaviors of these false-negative lesions were analyzed based on the final surgical pathology results. Results: Among the 770 malignant breast lesions, 703 (91.3%) were classified as true-positive findings on SWE, and 67 (8.7%) were classified as false-negative findings on SWE. Multivariate regression analysis revealed that a maximum lesion size <20 mm (95% confidence interval (CI): 3.105–11.520; P<0.001), a circumscribed margin (95% CI: 1.522–4.763; P=0.001), and ductal carcinoma in situ (DCIS) (95% CI: 4.715–18.057; P<0.001) were significantly associated with false-negative SWE findings. Of the 67 false-negative lesions, 32 (47.8%) were diagnosed as DCIS, and 35 (52.2%) were diagnosed as invasive carcinomas. All the breast cancer patients exhibited a low ALN burden. Moreover, among the 35 invasive carcinomas, 85.7% (30/35) were the luminal A or luminal B subtype. Conclusions: A maximum breast lesion size <20 mm, a circumscribed margin, and DCIS were identified as significant predictors of false-negative SWE findings. False-negative SWE findings were associated with tumors exhibiting low invasive biological potential and lower-risk pathological profiles, including a low ALN burden, a higher proportion of DCIS, and a predominance of Luminal A and Luminal B subtypes.
Volume ratio factor: a novel CT-derived biomarker for pre-planning ipsilateral lung dose risk stratification in breast cancer radiotherapy
Background: Ipsilateral lung dose in breast cancer radiotherapy is typically evaluated only after treatment planning, limiting early risk stratification and planning optimization. This study aimed to develop and validate a novel computed tomography (CT)-derived volumetric biomarker, the volume ratio factor (VRF), for pre-planning assessment of ipsilateral lung dose risk in breast cancer radiotherapy. Methods: In this retrospective single-center study, 58 breast cancer patients receiving volumetric modulated arc therapy (VMAT) with regional nodal irradiation (RNI) were analyzed. VRF was defined as the ratio of lung internal volume (LIV) to ipsilateral lung volume (ILV) using simulation CT. Correlation analyses were performed between VRF and lung dose metrics, and predictive performance was compared with conventional anatomical parameters using receiver operating characteristic (ROC) analysis and machine learning regression models. Results: VRF demonstrated the strongest correlation with mean lung dose (MLD) among all evaluated anatomical parameters (r=0.582, P<0.01), with significant correlation also observed for V20 (r=0.485, P<0.01) and V10 (r=0.438, P<0.01). ROC analysis showed that VRF achieved the highest discriminative performance for identifying patients with MLD >13 Gy [area under the curve (AUC) =0.71]. Regression analyses further demonstrated stable predictive performance with low estimation error using repeated cross-validation [mean absolute error (MAE): 0.51±0.10 Gy; mean absolute percentage error (MAPE): 4.22%±0.90%]. Compared with seven conventional anatomical parameters, VRF reduced MAE and MAPE by 19.5% and 20.9%, respectively, across all regression frameworks and lung dose-volume endpoints. In the subgroup without internal mammary node (IMN) irradiation, the correlation between VRF and MLD further increased to r=0.637. Conclusions: VRF is a simple and interpretable CT-derived biomarker that captures three‑dimensional target‑lung spatial interaction. It enables pre‑planning risk stratification of ipsilateral lung dose and may support geometry‑informed, individualized radiotherapy planning.
Assessment of renal allograft cortical perfusion in the early postoperative period using arterial spin labeling magnetic resonance imaging
Background: The early postoperative period is critical for patients who have undergone renal transplantation. This study aimed to quantify cortical perfusion in renal allografts during this period using arterial spin labeling (ASL) magnetic resonance imaging (MRI). Methods: Between May and December 2014, ASL MRI was performed on 53 patients with renal allografts who were 2 to 4 weeks post-transplantation, as well as on 20 healthy volunteers. Recipients were stratified into two groups according to estimated glomerular filtration rate (eGFR): those with good allograft function (eGFR ≥60 mL/min/1.73 m2), and those with impaired allograft function (eGFR <60 mL/min/1.73 m2). Renal blood flow (RBF) values were compared across healthy controls and the two recipient groups. The correlation between RBF and eGFR was also evaluated. Results: Mean cortical RBF was 389.9±61.2 mL/100 g/min in healthy controls, 295.0±67.9 mL/100 g/min in recipients with good allograft function, and 195.5±88.9 mL/100 g/min in recipients with impaired allograft function. Statistically significant differences in cortical RBF were observed among the three groups (P<0.001). A moderate positive correlation between eGFR and cortical RBF was identified among renal allograft recipients (R=0.62, P<0.01). Conclusions: Renal cortical perfusion in allografts during the early postoperative period was significantly reduced compared to healthy controls, particularly in cases of impaired allograft function. ASL MRI demonstrates potential as a non-contrast imaging modality for quantifying renal cortical perfusion in the post-transplant setting.
Association of knee osseous morphometric parameters with degeneration and tear of the discoid lateral meniscus: a cross-sectional quantitative MRI study
Background: Discoid lateral meniscus (DLM) is a congenital knee variant prone to degeneration and tears owing to its unique morphology and altered biomechanics. While magnetic resonance imaging (MRI) is the gold standard for DLM evaluation, the anatomical contributors to its pathological progression remain unclear. This study aimed to explore MRI-based morphological factors associated with DLM degeneration and tears using quantitative analysis. Methods: A total of 292 patients with DLM were retrospectively divided into three groups based on MRI findings: normal, degenerative, and tear DLM. Quantitative morphometric measurements of knee bony structures were acquired, including fibular head height, tibial spine height, anteroposterior (AP) diameter, width, and height of the medial and lateral femoral condyles, as well as the AP diameter and width of the medial and lateral tibial plateaus. Statistical analyses were performed to explore the associations between these morphological parameters and DLM pathological grading. Results: There were significant differences in osseous morphometric MRI parameters between meniscus grading groups. Significant differences were found in fibular head height, medial tibial spine height, width of lateral femoral condyle, medial and lateral femoral condyle heights, AP diameters of the medial and lateral tibial plateau, and medial tibial plateau width between groups (P<0.05). The medial tibial plateau AP diameter [odds ratio (OR) 1.37, 95% confidence interval (CI): 1.22–1.55, P<0.001] and fibular head height (OR 1.31, 95% CI: 1.12–1.53, P<0.001) were independent predictors of DLM tears. The medial tibial plateau AP diameter showed superior diagnostic ability [area under the curve (AUC) 0.856, 95% CI: 0.807–0.906], followed by fibular head height (AUC 0.846, 95% CI: 0.792–0.900). The combined model yielded a higher diagnostic value (AUC 0.973, 95% CI: 0.954–0.991). Conclusions: Several bony morphological features of the knee, especially the AP diameter of the medial tibial plateau, are associated with an elevated risk of DLM degeneration and tears. These findings may facilitate early risk stratification and clinical decision-making.
A multimodal complementary feature fusion network for myocardial pathological segmentation in multi-sequence cardiac magnetic resonance images
Background: Automated myocardial pathology segmentation in multi-sequence cardiac magnetic resonance (CMR) imaging plays an indispensable role in assessing myocardial viability and managing patients with myocardial infarction (MI). However, precise delineation remains exceptionally challenging due to low soft tissue contrast, highly irregular pathological geometries, residual cross-modal spatial misalignments, and scarcity of labeled datasets. This study aimed to develop a highly accurate, stable, and unified single-stage deep learning framework to simultaneously segment the left ventricular (LV) blood pool, right ventricular (RV) blood pool, left ventricular normal myocardium (LVM), myocardial edema, and myocardial scars from multi-sequence CMR images. Methods: We propose the Multimodal CMR Image Pathological Segmentation Network (MCIPS-Net), an end-to-end framework built upon a five-layer U-Net architecture. To address inter-modal intensity distribution heterogeneity among balanced steady-state free precession (BSSFP), late gadolinium enhancement (LGE), and T2-weighted (T2) sequences, a plug-and-play multimodal feature fusion (MFF) module was introduced in the encoding path to independently extract and cascade modality-specific semantic features. Furthermore, an efficient channel attention (ECA)-based fusion module was embedded before upsampling to strengthen region of interest (ROI) focus. Training stability and performance under extreme class imbalance were optimized using a multi-scale deep supervision (DS) strategy and an adaptive composite loss function merging Dice and Focal losses. Center-cropped images (256×256 pixels) were expanded 15-fold via geometric and elastic data augmentations, and post-processing was executed to eliminate minor isolated prediction artifacts. Results: Evaluated on the MyoPS 2020 Challenge test set (20 cases, 72 slices), MCIPS-Net achieved an average Dice score of 0.655±0.238 for scar segmentation and 0.714±0.086 for edema and scar joint segmentation, yielding a comprehensive average Dice score of 0.685. Ablation studies revealed that the MFF module contributed the most significant gain, increasing the baseline average Dice score by 4.5% (from 0.574 to 0.619). On the official leaderboard, our single-stage method outperformed the leading advanced single-stage method by 0.7% while demonstrating performance equivalent to the official second-place multi-stage pipeline. Crucially, the model exhibited the lowest standard deviation (SD) in Dice scores among all participating top algorithms, highlighting its superior stability. Conclusions: The proposed MCIPS-Net establishes a simple, efficient, and robust single-stage solution that bypasses complex localization networks or cumbersome model integration. By effectively capitalizing on cross-modal complementary features, this stable pipeline offers a high-performance clinical tool for precise infarct size quantification and myocardial viability assessment.
Cascaded deep learning for automated lung cancer tumour burden quantification on [18F]FDG PET/CT
Background: Accurate assessment of tumour burden in lung cancer is critical for diagnosis, prognosis, and treatment planning. To enhance segmentation accuracy and reduce false positives (FPs), we developed a cascaded deep learning framework that combines lesion segmentation and subsequent classification, aiming to enable reliable automated tumour burden estimation on positron emission tomography/computed tomography (PET/CT). Methods: In this retrospective single-centre study, we collected 593 fluorine-18 fluorodeoxyglucose ([18F]FDG) PET/CT scans from lung cancer patients with two scanners. Scanner 1 data (N=496) were split for five-fold cross-validation (training/validation) and internal testing; Scanner 2 data (N=97) served for external testing. The proposed cascaded framework integrated a segmentation stage with a subsequent classification stage to suppress FP findings and generate lesion-level tumour segmentation for tumour burden quantification. We compared the cascaded model to standalone segmentation using Dice similarity coefficient (DSC), lesion-level precision/recall, and assessed metabolic tumour volume (MTV) and total lesion glycolysis (TLG) against manual annotations. Results: On internal and external test sets, the cascaded model achieved consistent segmentation accuracy (DSC =0.82) with improved precision compared to segmentation alone (P<0.05). Tumour burden estimation showed strong correlations with manual measurements (r=0.984 for MTV, r=0.998 for TLG; both P<0.05) and moderate agreement. Conclusions: The proposed cascaded segmentation-classification architecture significantly reduces FPs and yields reliable tumour burden quantification on PET/CT, enhancing accuracy and clinical utility.
Leveraging large language models for information extraction from free-text liver MRI reports and assessment of clinical utility: a multicenter study
Background: Large language models (LLMs) have shown considerable potential for extracting information from free-text radiology reports, enabling efficient data use, large-scale data mining, and a wide range of secondary analyses and clinical applications. This study aimed to evaluate the performance of LLMs in extracting diagnostically relevant information from multicenter free-text liver magnetic resonance imaging (MRI) reports, explore the clinical utility of LLM-generated structured reports, and investigate optimal prompting strategies for multicenter data. Methods: In this retrospective multicenter study, 800 free-text liver MRI reports from four medical centers (Beijing Friendship Hospital, Tianjin Medical University General Hospital, The Second Affiliated Hospital of Xi’an Jiaotong University, Sir Run Run Shaw Hospital) were collected to evaluate the information extraction performance of two LLMs—DeepSeek-V3 and ChatGPT-4o—using radiologist-annotated structured data as the reference standard. Three prompting strategies were applied: zero-shot prompting, global few-shot prompting (using shared examples across centers), and center-specific few-shot prompting (using examples specific to each center), with example counts set to 2–12. Model performance was evaluated using field-level F1 scores, and report-level extraction success was defined as the correct extraction of ≥80% report fields. Additionally, an exploratory clinical evaluation was conducted using 20 reports from one center, in which 10 radiologists and 10 clinicians evaluated the readability and clinical usability of free-text, manually structured, and LLM-generated reports on a 5-point Likert scale. Results: Few-shot prompting significantly outperformed zero-shot prompting for both LLMs, with the largest gains in macro F1 observed when k was increased from 0 to 2 (∆DeepSeek-V3: global 0.106, center-specific 0.127; ∆ ChatGPT-4o: global 0.086, center-specific 0.107). Performance plateaued at k=4 [DeepSeek-V3: global 0.848 (0.838–0.858), center-specific 0.865 (0.856–0.875); ChatGPT-4o: global 0.835 (0.824–0.845), center-specific 0.861 (0.851–0.870)], with adjacent-k gains <0.01. Center-specific prompting consistently outperformed global prompting (∆F1: 0.017–0.024 for DeepSeek-V3; 0.014–0.026 for ChatGPT-4o). In the exploratory clinical evaluation, structured reports received higher scores for clarity and communication than free-text reports (both P<0.001), while LLM-generated reports received scores comparable to those of manually structured reports (both P>0.05). Conclusions: LLMs demonstrated strong performance in extracting diagnostically relevant information from Chinese multicenter liver MRI reports. In the exploratory clinical evaluation, the LLM-generated structured reports showed the potential to improve report clarity and facilitate clinical communication. Global prompting showed good performance across centers, while center-specific prompting further improved accuracy by adapting to local reporting styles.
Association of intra-pancreatic fat deposition with bone marrow adiposity in young obese adults
Background: Ectopic fat accumulation is a key factor in impaired bone health in the obese population. Although hepatic fat fraction (HFF) has been established as an important marker of metabolic dysfunction, the effect of pancreatic fat fraction (PFF) on bone marrow adipose tissue (BMAT) in young obese adults remains unclear. The aim of this study was to investigate the independent association between PFF and bone marrow adiposity and to compare it with that of HFF. Methods: This cross-sectional study included 155 young obese adults [mean age 35.01±6.25 years; mean body mass index (BMI) 31.9±4.3 kg/m2]. BMAT, PFF, and HFF were quantitatively measured using chemical shift-encoded (CSE) magnetic resonance imaging (MRI-PDFF sequence). Statistical analysis employed stratified multivariate linear regression and restricted cubic spline (RCS) models to assess the independent association between PFF and HFF with BMAT. Models were adjusted for age, gender, BMI, glycosylated hemoglobin (HbA1c), and triglycerides (TG). Results: PFF exhibited only a weak correlation with HFF [Spearman correlation coefficient (rs) =0.180, P=0.037]. Consistent positive associations with BMAT were observed across all pancreatic sub-regions (head, body, and tail: rs =0.29–0.34, all P<0.001). In the fully adjusted model, PFF demonstrated a robust and independent positive association with BMAT (β=0.323, P=0.014). Although HFF remained statistically significant, its effect size was substantially smaller than that of PFF (β=0.206, P=0.016). RCS analysis revealed a continuous linear dose-response relationship between BMAT and whole pancreas (P for overall =0.006), head (P=0.013), body (P=0.009), and tail (P=0.015), yet the nonlinearity tests did not reach statistical significance (all P for nonlinear >0.05). Notably, the shape of this linear dose-response trajectory was consistent across the pancreatic head, body, and tail, suggesting a generally uniform pathological impact without an identifiable safety threshold. Conclusions: PFF is a more robust and independent predictor of BMAT than HFF in young adults with obesity. These findings imply the existence of a distinct pathogenic pathway linking the fatty pancreas to marrow degeneration. Consequently, the assessment of pancreatic fat content may serve as an independent associated factor for skeletal risk stratification in this population.
A deep learning pipeline for liver macromolecular proton fraction quantification without subject-specific B1 acquisition using spin-lock MRI
Background: Macromolecular proton fraction (MPF) is a promising noninvasive biomarker for staging liver fibrosis. However, current post-processing requires B1-inhomogeneity acquisition and manual region of interest (ROI) selection, introducing subjectivity and variability. In this study, we propose a deep learning pipeline that enables liver MPF quantification without subject-specific B1 acquisition by leveraging an atlas-derived B1 map constructed from measured B1 data. Methods: This retrospective study used data collected at one institution from April 2019 to October 2019. The pipeline contains three models: a segmentation network for obtaining liver masks, a registration network for aligning the atlas B1 map with liver masks, and a quantification network for MPF quantification. An uncertainty-guided strategy is proposed to automatically select ROIs for assessing liver MPF. The accuracy of MPF quantification was evaluated using mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). Reproducibility of liver MPF measurement was assessed by comparing manually selected ROIs from two experts with automated ROIs, employing intraclass correlation coefficient (ICC) and Bland-Altman analysis. Results: The study included 44 patients (mean age, 59.4±9.7 years; 20 male patients, 24 female patients). MAE for MPF quantification within the whole liver and ROI are 0.49%±0.31% and 0.45%±0.26%, respectively. ICC of liver MPF assessments are 0.931 [95% confidence interval (CI): 0.887, 0.963] between expert 1 analyst and automated analysis, 0.949 (95% CI: 0.924, 0.971) between the expert 2 analyst and automated analysis, and 0.938 (95% CI: 0.903, 0.959) between expert 1 and expert 2 analyst. Mean bias [95% limits of agreement (LOA)] were 0.042% (−0.514%, 0.598%), −0.029% (−0.445%, 0.388%), and 0.033% (−0.497%, 0.563%) for expert 1 vs. automated analysis, expert 2 vs. automated analysis, and expert 1 vs. expert 2, respectively. Conclusions: The proposed deep learning pipeline enables liver MPF quantification without subject-specific B1 acquisition by employing an atlas-based B1 substitution strategy, while maintaining high reproducibility in a fully automated manner.
Evaluation of benign and malignant pulmonary nodules based on clinical and contrast-enhanced computed tomography data: a multicenter study
Background: Lung cancer is a leading cause of cancer-related death worldwide, and accurate differentiation of pulmonary nodules is crucial for clinical management. Low-dose and multiphasic computed tomography (CT) provides improved nodule detection, but the incremental diagnostic value of contrast-enhanced phases remains unclear. The objective of this study was to establish a prediction model based on clinical and CT data from three phases (noncontrast phase, arterial phase, and venous phase) for differentiating benign and malignant pulmonary nodules. Methods: A total of 863 patients from multiple centers with benign or malignant pulmonary nodules pathologically confirmed between 2015 and 2024 were retrospectively analyzed. Data from The First Affiliated Hospital of Bengbu Medical University and Anqing Municipal Hospital (Anhui, China), constituted the training cohort (n=470), data from Tongde Hospital of Zhejiang Province Affiliated to Zhejiang Chinese Medical University and Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University (Zhejiang, China), were used as the test cohort (n=205), and data from an additional center, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University (Hangzhou, Zhejiang, China), served as the validation cohort (n=188). According to different CT scanning phases, the data were divided into the following four groups, from which models were constructed: noncontrast phase, arterial phase, venous phase, and noncontrast phase each combined with the enhanced dual phase. Candidate variables were screened via univariate analysis and subsequently entered into multivariable logistic regression to identify independent predictors for model construction. Model performance was evaluated via receiver operating characteristic analysis and the area under the curve (AUC). Results: A total of 863 patients with pulmonary nodules were included, comprising 518 malignant nodules and 345 benign nodules. The final models incorporated clinical and imaging predictors, including age, surgical history, hepatitis or liver cirrhosis, interstitial lung disease, bronchiectasis, obstructive pneumonia, mixed ground-glass opacity, lobulation, pleural retraction, minimum CT attenuation value, and mean CT attenuation value. Model 1 retained 11 predictors, whereas models 2–4 retained 10 predictors with identical predictor sets. In the validation cohort, the AUC, sensitivity, and specificity values for model 1 were 0.909, 0.852, and 0.740, respectively; those for models 2 and 4 were 0.904, 0.755 and 0.849, respectively; and those for model 3 were 0.904, 0.870, and 0.712, respectively. There was no significant difference in the diagnostic performance of the four models (P>0.05). Conclusions: The four prediction models established based on clinical and three-phase CT data all demonstrated high diagnostic efficiency in differentiating benign and malignant pulmonary nodules.
Trochanteric region fracture classification network (TRFC-net): a deep learning solution to increasing inexperienced residents’ trochanteric region fracture classification accuracy on X-rays
Background: Accurate classification of trochanteric region fractures on X-rays is crucial for optimal clinical management but poses certain difficulties for inexperienced physicians, with interobserver variability in fracture subtype identification being particularly challenging. Therefore, we aimed to construct a trochanteric region fracture classification network (TRFC-net) based on deep learning algorithms to provide auxiliary diagnostic support for clinicians. Methods: The TRFC-net was designed to include three task modules for the proximal femur detection module, a fracture image screening module to distinguish fractured from nonfractured proximal femur images, and a fracture classification module to categorize A1, A2, and A3 fracture subtypes. The RetinaNet object detection algorithm detects the proximal femur region on X-ray and crops it. Subsequently, the Swin transformer classification network screens the trochanteric region fracture images from the cropped images and subsequently classifies the fracture image types. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to visualize the identified fracture features. To evaluate the practical performance of TRFC-net in classification assistance, we compared the classification accuracy of three attending physicians and resident physicians with and without the use of TRFC-net. Results: TRFC-net achieved an overall classification accuracy of 0.902, and its F1-scores for non-fracture, A1, A2, and A3 classifications were 0.964, 0.875, 0.867, and 0.848, respectively. Attending physicians achieved an overall classification accuracy of 0.939±0.014; the overall accuracy between TRFC-net and attending physicians was not significantly different [95% confidence interval (CI): 0.099–0.111; P=0.105], whereas that between the attending physicians and resident physicians was (P<0.001). TRFC-net provided greater improvement in the classification accuracy of residents than of attending physicians: the overall accuracy of resident physicians increased from 0.817±0.013 to 0.924±0.010, and the difference in overall accuracy between resident physicians assisted by TRFC-net was not significantly different from that of attending physicians (95% CI: 0.782–0.798; P=0.790). This suggests that TRFC-net may provide clinical utility in patient populations similar to those examined in this study. Conclusions: Within the dataset used in this study, TRFC-net effectively assisted inexperienced residents in approaching the classification accuracy of attending physicians; however, the diagnostic performance for A3 fracture subtypes was relatively unsatisfactory, and further validation with larger-scale, multicenter cohorts is needed.
Comparative evaluation of artificial intelligence-deep learning-based cine short-axis segmentation and manual post-processing for left ventricular function and structure in cardiac magnetic resonance
Background: Cardiac magnetic resonance (CMR) is the reference standard for evaluating left ventricular (LV) function and structure. Manual post-processing is time-consuming, operator-dependent, and limited in reproducibility. Deep learning-based artificial intelligence (AI-DL) offers automated image segmentation with expert-level accuracy, but requires clinical validation against manual analysis. This study aimed to directly compare two AI-DL segmentation strategies—simplified (trabeculae and papillary muscles included in blood pool) and standard (included in myocardium)—with manual analysis to determine which approach yields superior clinical agreement. Methods: We retrospectively studied 104 patients who underwent CMR. Using both AI-DL (simplified/standard) methods and traditional manual post-processing software, we quantified LV parameters including ejection fraction (EF), end-diastolic volume and index (EDV/EDVI), end-systolic volume and index (ESV/ESVI), stroke volume and index (SV/SI), cardiac output and index (CO/CI), and end-diastolic LV-mass and index (M/MI). Analysis time, correlation, and agreement with manual results were compared. Results: AI-DL markedly reduced analysis time compared with manual post-processing following standard method (median 50.0 vs. 217.0 s; P<0.001). The AI-DL simplified method underestimated EF (54.2 vs. 60.0), mass indices (131.45 vs. 156.00, 76.45 vs. 91.25) and overestimated volumetric indices (148.10 vs. 118.00, 88.70 vs. 69.80, 62.85 vs. 46.65, 40.00 vs. 28.70, 69.90 vs. 63.30, 41.70 vs. 37.83, 5.35 vs. 4.80, 3.15 vs. 2.80) relative to manual analysis (all P<0.001). In contrast, the standard method showed no significant differences from manual values (58.3 vs. 60.0, 122.80 vs. 118.00, 72.70 vs. 69.80, 46.30 vs. 46.65, 27.75 vs. 28.70, 64.70 vs. 63.30, 37.85 vs. 37.83, 4.90 vs. 4.80, 2.90 vs. 2.80, 163.05 vs. 156.00, 93.55 vs. 91.25) (all P>0.05). Both methods demonstrated strong correlations with manual results, with the standard method achieving higher intraclass correlation coefficient (0.798–0.978 vs. 0.690–0.928) and correlation coefficients (r=0.772–0.952 vs. 0.726–0.944) (all P<0.001) as well as narrower Bland-Altman limits of agreement (LOA) (the differences were smaller and not statistically significant, all P>0.05), whereas the simplified method had wider LOA (the differences were larger and statistically significant, all P<0.001). Conclusions: AI-DL enables rapid and reliable quantification of LV function and structure in CMR. Although both simplified and standard methods show strong agreement with manual analysis, the standard method provides better agreement and narrower limits of difference, making it more clinically acceptable. These findings highlight the potential of AI-DL as a practical tool for routine cardiac functional assessment.
Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain metastases
Background: Spatial heterogeneity within the tumor drives T790M resistance mutation. This study aimed to develop a habitat radiomics-based nomogram for predicting T790M resistance mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM). Methods: A total of 301 patients from Beijing Chest Hospital, Capital Medical University and Shandong Cancer Hospital and Institute, Shandong First Medical University were retrospectively collected. Tumors were manually delineated on contrast-enhanced T1-weighted (T1-CE), and the segmentation was combined with an automatically generated 5 mm peritumoral region to form the whole region of interest (ROI). Predictive models were established using radiomics features extracted from intratumoral, peritumoral, whole ROI, and habitat-generated subregions within the whole ROI. A nomogram was developed by integrating a representative radiomics signature and meaningful clinical factors. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was used to interpret feature distribution in the habitat model, and proteomics analysis was performed to explore the biological mechanisms of the nomogram. Results: The habitat model based on three subregions achieved higher AUCs of 0.924 and 0.902 in the training and test cohorts, respectively. The habitat radiomics-based nomogram yielded the highest AUCs of 0.943 and 0.931 in the two cohorts, separately. Meanwhile, clinical and other radiomics models showed moderate performance with AUCs ranging from 0.644 to 0.800 in the test cohort. Proteomics analysis revealed that RNA processing and splicing may represent the most critical pathway distinguishing high- and low-risk groups. Conclusions: Habitat imaging combining tumoral and peritumoral radiomics was beneficial for identifying T790M resistance mutation in NSCLC BM patients. Furthermore, a habitat radiomics-based nomogram can further improve the predictive ability.
Early diastolic dysfunction in patients with ischemia and no obstructive coronary artery disease: a prospective study on hybrid positron emission tomography-magnetic resonance imaging
Background: Coronary microvascular dysfunction (CMD) has been proposed to be one of the pathological mechanisms underlying heart failure with preserved ejection fraction (HFpEF) and left ventricular (LV) diastolic dysfunction. This study aimed to clarify the relationship between CMD and LV early diastolic function in patients with ischemia and no obstructive coronary artery disease (INOCA). Methods: Consecutive patients with INOCA treated at The First Affiliated Hospital of University of Science and Technology of China between August 2024 and March 2026 were prospectively recruited. LV strain rates, myocardial flow reserve (MFR), and peak filling rate (PFR) were acquired via positron emission tomography-magnetic resonance imaging (PET/MRI). Linear regression analysis was performed to identify independent associations between LV early diastolic strain rates and CMD. Results: A total of 73 patients (mean age 55±10 years; 49.3% females) with INOCA were ultimately enrolled and divided into a CMD group and a non-CMD group based on global MFR assessment. Compared with the non-CMD group, the CMD group exhibited significantly lower radial peak early diastolic strain rate (PEDSR), circumferential PEDSR, and longitudinal PEDSR (all P values <0.001). However, no significant differences were observed in LV global strain rate parameters. Multivariate regression analysis revealed that the factors independently associated with global MFR were radial PEDSR [standardized β (95% CI) = −0.250 (−0.494 to −0.006); P=0.044] and circumferential PEDSR [standardized β (95% CI) = 0.464 (0.194 to 0.734); P=0.001]. Additionally, multivariate analysis confirmed CMD to be an independent predictor of all three directions of PEDSR (radial, circumferential, and longitudinal) (all P values <0.001). Notably, the independent predictors of radial PEDSR were PFR [standardized β (95% CI) = −0.356 (−0.523 to −0.190); P<0.001] and extracellular volume [standardized β (95% CI) = 0.289 (0.139 to 0.439); P<0.001]. Conclusions: Despite normal LV ejection fraction in patients with INOCA, CMD was significantly associated with subclinical LV early diastolic dysfunction. These physiological findings provide a mechanistic basis for future prospective studies investigating the prognostic significance of these diastolic abnormalities in patients with INOCA.
The influence of accessory pathway localization on left ventricular mechanics in adult patients with Wolff-Parkinson-White syndrome: a speckle-tracking echocardiographic investigation
Background: Ventricular pre-excitation can cause left ventricular (LV) mechanical dyssynchrony and dysfunction. However, the specific impact of accessory pathway (AP) location on LV mechanics remains incompletely characterized. This study utilized two-dimensional speckle-tracking echocardiography (2D-STE) to quantify and compare LV deformation and synchrony across these anatomical subgroups. Methods: We retrospectively analyzed 91 patients with Wolff-Parkinson-White (WPW) syndrome (age 38.2±1.4 years) and 56 matched controls using 2D-STE. Global and regional LV deformation and synchrony were compared across AP-location groups: septal (n=34), right (n=38), and left (n=19). LV deformation parameters included circumferential strain (CS), longitudinal strain (LS), and radial strain (RS). LV synchrony was assessed by peak strain dispersion (PSD) of LV segments for each strain direction. Results: All patient groups (septal: −19.0%±0.7%, right: −19.3%±0.6%, and left AP: −19.2%±0.8%) had lower global CS (GCS) compared with controls (−23.1%±0.4%, all P<0.001). The septal AP group showed lower global LS (GLS) (−17.2%±0.5%), global RS (GRS) (30.1%±2.0%), and prolonged GLS-PSD (51.3±3.1 ms) than the left AP group and controls (P<0.05). The right AP group exhibited lower GLS (−17.5%±0.5%) than the left AP group (−19.6%±0.5%, P<0.05) and prolonged GLS-PSD (46.7±2.5 ms) compared with controls (31.6±1.1 ms, P<0.001). The left AP group showed GLS (−19.6%±0.5%), GRS (40.0%±2.6%), and GLS-PSD (40.1±2.3 ms) that were not significantly different from controls (all P>0.05). LV ejection fraction (LVEF) was lower in septal (59.2%±1.2%) and right AP groups (59.8%±1.0%) vs. controls (65.0%±0.3%; P<0.001). LVEF correlated negatively with GLS (r=−0.53, P<0.001) and with GLS-PSD (r=−0.41, P<0.001). Conclusions: LV mechanical dysfunction in WPW syndrome varies by AP location. Septal and right APs lead to widespread strain impairment, dyssynchrony, and lower LVEF, whereas left APs mainly reduce CS. Reduced LVEF is closely associated with impaired LV mechanics and dyssynchrony.
Artificial intelligence-amplified contrast enhancement in brain magnetic resonance imaging for improving image quality and lesion visualization: a prospective pilot study
Background: Artificial intelligence (AI) algorithms synthesizing virtual standard-dose images from low-dose contrast-enhanced images of brain magnetic resonance imaging (MRI) have been repurposed to boost contrast from standard-dose input. This study aimed to prospectively evaluate the impact of a Food and Drug Administration (FDA)-cleared, deep learning-based software on contrast enhancement, lesion visualization, and diagnostic confidence for standard-dose contrast-enhanced images. Methods: This prospective study enrolled patients undergoing contrast-enhanced brain MRI between August 2025 and September 2025. Precontrast and standard-dose postcontrast three-dimensional T1-weighted (T1w) images were acquired. AI-amplified contrast-enhanced images were generated via an FDA-cleared deep learning software based on precontrast and standard postcontrast images. Two radiologists independently performed quantitative analyses, examining contrast-to-noise ratio (CNR), lesion-to-brain ratio (LBR), and contrast enhancement percentage (CEP). Qualitative assessments of lesion border delineation, internal morphology, and contrast enhancement, and diagnostic confidence were performed with a 4-point Likert scale. Comparisons between AI-amplified and standard-dose images were conducted via the Wilcoxon signed-rank test. Results: Forty-one patients (mean age 51.1±12.6 years) with enhancing brain lesions were included. For both readers, AI-amplified images, as compared with standard contrast-enhanced images, exhibited a significantly higher CNR (82.56±41.15 vs. 20.33±23.28; 87.91±50.30 vs. 20.76±12.00), LBR (2.86±0.64 vs. 1.78±0.48; 2.83±0.53 vs. 1.76±0.40), and CEP (Reader 1: 393.00%±188.40% vs. 201.20%±111.32%; Reader 2: 382.26%±176.71% vs. 197.57%±109.90%) (all P values <0.001). Compared with standard-dose images, AI-amplified images produced an approximately 400% higher CNR, a 60% higher LBR, and a 200% higher CEP. The qualitative evaluation of both readers indicated that the AI-amplified images provided significantly improved lesion border delineation (Reader 1: 3.38±0.61 vs. 3.13±0.59; Reader 2: 3.46±0.60 vs. 3.00±0.63) and contrast enhancement (Reader 1: 3.88±0.31 vs. 2.94±0.24; Reader 2: 3.85±0.42 vs. 2.90±0.49) (all P values <0.001). No significant differences were observed in lesion internal morphology scores between AI-amplified and standard-dose images (P>0.05 for both readers). AI-amplified images demonstrated significantly higher diagnostic confidence than did standard-dose images for both readers (Reader 1: 3.59±0.50 vs. 3.20±0.60; Reader 2: 3.48±0.64 vs. 3.05±0.74; both P values <0.001). Conclusions: AI-based contrast amplification significantly increases the quantitative contrast metrics, qualitative lesion visualization, and diagnostic confidence for standard-dose contrast-enhanced brain MRI without increasing the gadolinium dose. These findings support the use of AI-based contrast amplification as a complementary tool in routine clinical neuroimaging.
Development and validation of a logistic regression model based on multiparametric MRI for differentiating triple-negative from non-triple-negative breast cancer
Background: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis. The preoperative differentiation of TNBC from non-TNBC remains challenging using conventional imaging. This study aimed to develop and validate a logistic regression model based on multiparametric magnetic resonance imaging (MRI) features for distinguishing TNBC from non-TNBC. Methods: A total of 286 female patients (mean age 42.9±5.1 years; range, 31–69 years) with pathologically confirmed breast cancer who underwent dynamic contrast-enhanced MRI (DCE-MRI) between January 2019 and September 2024 were retrospectively included. Based on immunohistochemistry, 92 patients were classified as TNBC and 194 as non-TNBC. Multiparametric MRI features, including tumor size, margin, nipple involvement, ipsilateral axillary lymph node metastasis, and Breast Imaging Reporting and Data System (BI-RADS) category, were evaluated by two radiologists. Univariate analysis was performed using rank sum test or chi-square test. Multivariable logistic regression with backward stepwise selection was used to identify independent predictors and construct the predictive model. Model performance was assessed using receiver operating characteristic (ROC) curve analysis, with area under the ROC curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated. Internal validation was performed using 10-fold cross-validation. Results: Univariate analysis showed that larger tumor size (long diameter: 20.1±14.3 vs. 17.5±4.7 mm, P<0.001; short diameter: 19.4±12.8 vs. 14.0±9.1 mm, P<0.001), unclear tumor margin (89.1% vs. 66.5%, P<0.001), nipple involvement (32.6% vs. 1.5%, P<0.001), solitary ipsilateral axillary lymph node metastasis (53.3% vs. 14.4%, P<0.001), and higher BI-RADS category (mean, 6.75 vs. 6.087, P<0.001) were significantly associated with TNBC. Multivariable logistic regression identified four independent predictors: unclear tumor margin [odds ratio (OR) =43.649; 95% confidence interval (CI): 5.191–367.001; P=0.001], ipsilateral axillary lymph node metastasis (OR =23.901, 95% CI: 6.743–84.714, P<0.001), nipple involvement (OR =9.911; 95% CI: 2.235–44.444; P=0.007), and BI-RADS category 7 (OR =3.288; 95% CI: 1.538–7.027; P=0.002). The logistic regression model achieved an AUC of 0.907 (95% CI: 0.868–0.946). At the optimal probability threshold of 0.62 (Youden index), the model yielded a sensitivity of 0.960, specificity of 0.750, accuracy of 0.887, PPV of 0.877, and NPV of 0.911. The 10-fold cross-validated AUC was 0.891 (95% CI: 0.851–0.931), supporting internal consistency. Conclusions: The logistic regression model incorporating multiparametric MRI features—particularly unclear tumor margin, ipsilateral axillary lymph node metastasis, nipple involvement, and BI-RADS category—demonstrates good predictive performance for distinguishing TNBC from non-TNBC. This model may serve as a useful preoperative tool to assist in clinical decision-making for patients with breast cancer.
Application of 50-keV virtual monochromatic imaging combined with a personalized contrast agent protocol in computed tomography pulmonary angiography: a randomized controlled trial
Background: Computed tomography pulmonary angiography (CTPA) is the preferred imaging modality for the diagnosis of pulmonary embolism (PE). Conventional fixed contrast agent protocols are associated with high contrast volume and severe superior vena cava (SVC) artifacts. This study aimed to evaluate the value of 50-keV virtual monochromatic imaging (VMI) combined with a body weight-adjusted personalized contrast agent protocol in reducing contrast load and optimizing image quality in CTPA. Methods: A total of 76 patients undergoing CTPA were randomly allocated to the control group (Group A; n=38) or the experimental group (Group B; n=38). Since this was a small-sample exploratory study on imaging technology, it was not registered on any official platform. Both groups were scanned with a tube voltage of 120-kVp and automatic tube current modulation. Patients in group A received a fixed contrast protocol (flow rate 4.0 mL/s; volume 40 mL) with 120-kVp reconstruction. Meanwhile, patients in Group B were subjected to a body weight-adjusted protocol with weights of 40–60 kg, 60–80 kg, and 80–100 kg, having a flow rate and volume of 2.3 mL/s and 13 mL, 2.6 mL/s and 15 mL, and 2.9 mL/s and 17 mL, respectively. For all weights, 50-keV VMI reconstruction was conducted. Objective and subjective image quality was compared between the groups. Results: All images were diagnostically acceptable. Group B had a significantly lower contrast dose (15.1±0.64 mL; 62.25% reduction) and flow rate (2.55±0.18 mL/s; 36.25% reduction) (both P values <0.05). Group B also demonstrated significantly higher computed tomography (CT) values in the right pulmonary artery [406.3±112.9 vs. 345.9±102.6 Hounsfield units (HU)], right upper lobe pulmonary artery (398.3±109.3 vs. 366.7±116.1 HU), and right lower lobe pulmonary artery (441.1±111.7 vs. 357.2±120.6 HU), as well as improved signal-to-noise ratio (SNR) in the right pulmonary artery (18.01±5.70 vs. 14.33±5.23), right upper lobe pulmonary artery (17.67±5.55 vs. 15.17±5.78), and right lower lobe pulmonary artery (19.55±5.66 vs. 14.81±6.10). Similar improvements were observed in contrast-to-noise ratio (CNR) values for the corresponding regions. No significant difference was observed in pulmonary artery visualization scores between the two groups (P>0.05), but Group B had higher SVC artifact scores and a lower number of artifacts (P<0.05). Conclusions: In CTPA examination, 50-keV VMI combined with a personalized contrast agent protocol can improve image quality while substantially reducing both the contrast dose and injection flow rate.
Quantitative imaging features of pulmonary artery involvement in Takayasu arteritis on computed tomography pulmonary angiography in patients with and without pulmonary hypertension
Background: Pulmonary artery involvement (PAI) in Takayasu arteritis (TA) is often diagnosed late and may lead to pulmonary hypertension (PH). Quantitative imaging biomarkers that could aid early detection are currently lacking. This study aimed to quantify pulmonary arterial volumes (PAVs) in TA-PAI using computed tomography pulmonary angiography (CTPA) and to compare imaging features between PAI patients and age-/sex-matched controls, as well as between PAI subgroups with and without PH. Methods: This single-center retrospective study (April 2022–March 2024) included 90 patients [median age 33 years; interquartile range (IQR), 27–43 years; 91.1% female] meeting modified Ishikawa criteria and 47 age- and sex-matched controls with normal pulmonary arteries on CTPA. PAVs were measured in six non-overlapping diameter (D) strata (D ≤0.8, 0.8< D ≤1.6, 1.6< D ≤2.4, 2.4< D ≤3.2, 3.2< D ≤4.0, D >4.0 mm) using a deep learning-based segmentation method implemented on the AZE Virtual Place workstation. Cumulative threshold-based volumes and the proportional contribution of each stratum to total intraparenchymal PAV were then analyzed. Qualitative features (stenosis, occlusion, dilation, aneurysm, wall thickening, thrombosis) were recorded. PH was defined by echocardiography according to the European Society of Cardiology/European Respiratory Society (ESC/ERS) PH guidelines. Continuous variables were compared using the t-test or Mann-Whitney U test, and categorical variables using the χ2 or Fisher’s exact test, with Bonferroni correction (P<0.05). Results: Compared with the control group, the patient group showed significantly lower cumulative volumes (mL) for arteries with diameters of ≤3.2 mm [52.8 (IQR, 45.8–57.7) vs. 56.6±11.2 mL; P=0.047] and ≤4.0 mm [59.5 (IQR, 52.2–65.0) vs. 63.7±11.6 mL; P=0.028]. The proportional contribution of each non-overlapping diameter stratum to total intraparenchymal PAV was lower in patients than in controls for the 1.6< D ≤2.4 mm (patients vs. controls: 26.1%±5.5% vs. 28.6%±6.3%; P=0.021) and 2.4< D ≤3.2 mm (21.6%±3.5% vs. 23.6%±3.4%; P=0.001). Compared with non-PH patients (n=73), the PH subgroup (n=17) had larger main pulmonary artery diameters (32.3±5.4 vs. 26.9±5.3 mm; P<0.001) and higher rates of stenosis (88% vs. 63%; P=0.045), dilation (71% vs. 29%; P=0.001), and aneurysm (24% vs. 4%; P=0.007), but cumulative PAVs did not differ significantly. Conclusions: CTPA-based PAV quantification reveals marked volume loss in mid-sized pulmonary arteries in TA-PAI. PAV might be beneficial for detecting TA. However, PAV showed no significant difference between groups according to PH status, despite severe pulmonary arterial structural changes.
A multitask deep learning framework for the automated quantification of abdominal tissue morphology on ultrasound and the therapeutic monitoring of diastasis recti abdominis
Background: Diastasis recti abdominis (DRA) requires accurate ultrasound-based quantification of abdominal tissue morphology, but manual measurement is operator-dependent and inefficient for longitudinal monitoring. This study developed and evaluated a multitask deep learning model for automated abdominal ultrasound analysis and the monitoring of magnetic stimulation therapy. Methods: A retrospective dataset of 2,750 abdominal ultrasound images from 632 women was used to train a ResNet-50-encoded U-Net with dual branches for tissue segmentation (linea alba, rectus abdominis, and subcutaneous fat) and anatomical region classification. Model performance was evaluated on an independent set of 550 images. The trained model was then applied prospectively to 21 patients undergoing six sessions of targeted abdominal magnetic stimulation therapy, with ultrasound measurements obtained before treatment, 1 day after the first session, and 1 day after the sixth session. Results: The model achieved Dice coefficients of 0.979±0.013 for subcutaneous fat, 0.951±0.066 for rectus abdominis muscle, and 0.828±0.074 for the linea alba. Automated measurements showed strong agreement with expert measurements, with intraclass correlation coefficients (ICCs) of 0.988 for subcutaneous fat thickness (SET), 0.955 for the interrectus distance (IRD), and 0.917 for rectus abdominis muscle thickness (RAMT). After six therapy sessions, the IRD decreased from 20.17±7.81 to 15.22±6.58 mm (P=0.04). Left and right RAMT increased from 8.27±1.04 to 10.26±1.23 mm and from 8.22±1.11 to 10.19±1.45 mm, respectively (both P values <0.001). There was a nonsignificant (P=0.05) decrease in SET from 16.56±3.56 to 14.30±3.55 mm. Conclusions: The proposed multitask model enabled automated multitissue quantification on abdominal ultrasound images with strong agreement with expert measurements. It may support reproducible diagnosis and longitudinal monitoring of patients with DRA. Meanwhile, its ability to monitor treatment-related changes, particularly increases in short-term muscle thickness and reductions in subcutaneous fat, should be interpreted cautiously and validated in larger controlled studies.
Quantitative assessment of occlusal function before and after extraction of unilateral impacted mandibular third molars using the TeeTester occlusal analyzer
Background: Impacted teeth of mandibular third molars have been associated with disruptions in full-mouth occlusal force distribution, potentially affecting mandibular movement, masticatory muscle activity, and temporomandibular joint function. The present investigation employed the TeeTester occlusal analyzer to obtain quantitative data on the occlusal characteristics associated with impacted mandibular third molars (IM3Ms), both prior to and following surgical extraction. This approach enabled a dynamic assessment of occlusal function and provided clinical insights regarding functional changes post-intervention. Methods: A total of 54 individuals presenting with unilateral IM3M were included and stratified into three groups based on impaction type: vertical, mesioangular, and horizontal, with 18 participants allocated to each category. Occlusal parameters were measured using the TeeTester device before extraction and one week post-extraction. Results: Within the mesioangular impaction group, the mean occlusal contact time significantly decreased from 0.45 seconds pre-extraction to 0.27 seconds post-extraction (P<0.05). No statistically significant changes were detected in occlusal characteristics in either the vertical or horizontal impaction groups when comparing pre- and post-extraction measurements. Conclusions: According to the findings of this study, surgical removal of a unilaterally mesioangular IM3M resulted in a statistically significant reduction in occlusal contact time between the maxillary and mandibular dental arches on the affected side. This reduction may contribute to improved occlusal stability and function.
Wireless detector-based magnetic resonance imaging for grading assessment of the peridiscal attachments in temporomandibular joint internal derangement
Background: The relationship between the peridiscal attachments and temporomandibular joint (TMJ) internal derangement (ID) remains poorly understood. This study aimed to investigate the association between graded imaging features of the peridiscal attachments and disc displacement using wireless detector-based magnetic resonance imaging (WD-MRI). Methods: This retrospective study included 61 patients (122 TMJs) diagnosed with TMD according to the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD). All patients underwent oblique sagittal proton density-weighted imaging in both closed- and open-mouth positions using WD-MRI. Two radiologists independently assessed disc position [normal, disc displacement with reduction (DDWR), or disc displacement without reduction (DDWOR)] and performed ordinal grading (1–3 points: clearly, indistinctly, or non-visualized) of four peridiscal attachments: the anterior attachment superior (AAS), anterior attachment inferior (AAI), posterior attachment superior (PAS), and posterior attachment inferior (PAI). Inter-observer reliability between the two radiologists was evaluated using the weighted kappa coefficient. Group differences and correlations between attachment scores and disc position were analyzed using the Kruskal-Wallis H test, Mann-Whitney U test, and Spearman correlation analysis. Results: A total of 122 TMJs (12 normal, 37 DDWR, and 73 DDWOR) were evaluated using WD-MRI. Peridiscal attachment scores differed significantly among the three groups (all P<0.05). The inter-observer reliability for AAS, AAI, PAS, and PAI demonstrated substantial to almost perfect agreement, with weighted kappa values of 0.838 [95% confidence interval (CI): 0.746–0.930], 0.896 (0.825–0.967), 0.852 (0.774–0.930), and 0.799 (0.705–0.893), respectively. Mann-Whitney U tests revealed that peridiscal attachment grades in the DDWOR group were significantly higher than those in both the normal and DDWR groups across all four attachments (Z=2.571–6.572, r=0.279–0.738, all P<0.05). However, when comparing the DDWR and normal groups, a significant difference was observed only for PAI (Z=2.653, P<0.05), with no significant differences for AAS, AAI, or PAS (P>0.05). The grading of the peridiscal attachments showed significant positive correlations with disc reducibility. Specifically, the positive correlations were strong for PAS (r=0.720) and PAI (r=0.630), and moderate for AAS (r=0.467) and AAI (r=0.356). Conclusions: This study demonstrated that structural alterations in the peridiscal attachments, particularly the posterior attachment, are significantly correlated with TMJ ID severity on WD-MRI, suggesting their potential value in TMD assessment.
Association of stress hyperglycemia ratio on right ventricular dysfunction and biventricular coupling in patients with acute coronary syndrome assessed by CMR feature tracking: a cross-sectional study
Background: Among individuals diagnosed with acute coronary syndrome (ACS), the stress hyperglycemia ratio (SHR) has been demonstrated to correlate with an elevated risk of unfavorable cardiovascular events. However, the relationship between SHR, right ventricular (RV) function and biventricular interdependence has not yet been reported. The present study aimed to explore the association of high SHR with RV deformation and biventricular coupling in ACS patients via cardiac magnetic resonance (CMR). Methods: This study consisted of 103 patients with ACS and 57 age- and sex-matched controls. Based on the median of SHR values, ACS patients were stratified into low (<1.07, n=51) and high (≥1.07, n=52) SHR groups. Biventricular myocardial deformation, encompassing global peak strain (PS) in the radial, circumferential, and longitudinal directions for both left ventricle and right ventricle, was quantified and compared across groups. Linear regression and exploratory mediation analyses were conducted to evaluate the association between SHR and RV PS and to examine whether LV PS may partly explain this association. Results: ACS patients, irrespective of SHR level, had significantly decreased left ventricular (LV) and RV PS, compared to controls (all P<0.001). Among ACS patients, the high SHR group had lower LV radial PS [14.32 (10.70, 21.39) vs. 21.45 (11.02, 28.84)], LV longitudinal PS [−7.92 (−10.77, −6.75) vs. −10.26 (−13.00, −6.34)], RV radial PS [25.03 (18.55, 35.02) vs. 33.31 (20.57, 44.66)], and RV longitudinal PS (−11.92±5.40 vs. −16.60±6.57) than the low-SHR group (all P<0.05). Exploratory mediation analysis suggested that LV radial and longitudinal PS may statistically account for part of the observed association between RV radial and longitudinal PS between the low- and high-SHR groups [indirect effect of LV radial PS: −6.159 (−8.982, −3.641) for RV radial PS and 1.859 (0.970, 2.828) for RV longitudinal PS; indirect effect of LV longitudinal PS: 1.859 (0.970, 2.828) for RV radial PS, and 1.617 (0.888, 2.444) for RV longitudinal PS]. Conclusions: Higher SHR was associated with more impaired RV radial and longitudinal PS in patients with ACS. Exploratory mediation analysis suggested that LV radial and longitudinal PS may partly explain the association between higher SHR and impaired RV radial and longitudinal PS.
Construction and validation of a diagnostic model for obstructive coronary artery disease in patients with suspected unstable angina pectoris based on resting 18F-fluorodeoxyglucose positron emission tomography myocardial ischemia memory imaging
Background: Identifying obstructive coronary artery disease (OCAD) via non-invasive imaging modalities in patients with suspected unstable angina (UA) holds substantial clinical significance. This study aimed to develop and validate a diagnostic model for OCAD in patients with suspected UA, by leveraging resting 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) myocardial ischemia memory imaging combined with clinical indicators. Methods: We retrospectively analyzed 162 patients with a Global Registry of Acute Coronary Events (GRACE) score ≤140 who presented with chest pain or chest tightness and were clinically suspected of having UA. After collecting clinical indicators, predictive factors were screened using logistic regression. A diagnostic model was constructed using binary logistic regression based on the predictive factors, with internal validation via 1,000 bootstrap resamples. The discriminative ability, calibration, and clinical net benefit of the established model were evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Furthermore, the enhancement value of the final model over the basic model was quantified using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: Of the 162 enrolled patients with suspected UA, 89 (54.9%) were diagnosed with OCAD. Six predictors were incorporated into the diagnostic model, including hyperlipidemia, diabetes, typical angina pectoris, 18F-FDG PET results, serum creatinine, and left ventricular ejection fraction (LVEF). The area under the curve (AUC) of the model was 0.89 [95% confidence interval (CI): 0.84–0.94], with a sensitivity of 0.84 and a specificity of 0.81; the Brier score was 0.1319. The Hosmer-Lemeshow goodness-of-fit test revealed good model fit (χ2=6.15, P=0.63). After internal validation via the bootstrap method, the optimism-corrected AUC was 0.87 (95% CI: 0.82–0.93). Calibration curve and DCA demonstrated that the model exhibited satisfactory calibration and promising clinical utility. Conclusions: The OCAD diagnostic model for suspected UA patients, based on resting 18F-FDG PET and clinical indicators, demonstrated excellent diagnostic performance.
ASL, DTI, and T1 mapping in identifying immune and non-immune pathogenic factors of chronic allograft injury
Background: Chronic allograft injury (CAI) has a complex etiology and various clinical and pathological manifestations, posing challenges for clinical diagnosis and treatment. This study aimed to explore the value of arterial spin labeling (ASL), diffusion tensor imaging (DTI), and T1 mapping in identifying the causes of CAI. Methods: This study included 69 kidney transplant recipients. Based on the estimated glomerular filtration rate (eGFR) and biopsy results, the recipients were classified into three groups: Group A, recipients with good renal allograft function (eGFR ≥60 mL/min/1.73 m2); Group B, recipients with immune-mediated factors; and Group C, recipients with non-immune-mediated factors. ASL, DTI, and T1 mapping were performed in all patients, and magnetic resonance imaging (MRI) parameters were calculated and analyzed. Results: Renal blood flow (RBF) was higher in the recipients with normal renal allograft function than in those with CAI. Cortical and medullary apparent diffusion coefficient (ADC) values were lower in the immune factors group (2.05±0.22/2.06±0.22 ×10−3 mm2/s) than in the non-immune factors group (2.24±0.23/2.25±0.24 ×10−3 mm2/s). Conversely, cortical T1 values were higher in in the immune-mediated group than in the non-immune-mediated group. The area under the curve (AUC) value for cortical ADC, medullary ADC, and cortical T1 values in identifying CAI pathogenic factors was 0.716, 0.687, and 0.806, respectively. The combination of DTI and T1 mapping achieved better diagnostic efficiency for distinguishing between patients in Group B and Group C than DTI or T1 mapping alone [AUC =0.825, 95% confidence interval (CI): 0.699–0.952, P<0.001]. Conclusions: ASL, DTI, and T1 mapping can distinguish between recipients with normal renal allograft function and those with CAI. Further, DTI and T1 mapping can differentiate between immune and non-immune pathogenic factors of CAI.
Ultrasonic markers for screening intra-abdominal hypertension in patients with pancreatic diseases
Background: Accurately assessing intra-abdominal hypertension (IAH) is of significant clinical importance in laparoscopic pancreaticoduodenectomy (LPD) and severe acute pancreatitis (SAP), which is closely linked to complications and mortality. This study aimed to identify potential ultrasonic markers for screening IAH in patients who underwent LPD for pancreatic cancer and those with SAP. Methods: We conducted a retrospective analysis of 27 patients who were diagnosed with pancreatic cancer and underwent LPD, and 19 patients with SAP admitted to the intensive care unit (ICU) from February 2022 to August 2022. Baseline demographic, ultrasonic markers, and clinical records were obtained and analyzed to identify potential ultrasonic markers for screening IAH. The diagnostic performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUROC). Results: Comparative analysis between the two groups showed that in both the LPD and SAP groups, patients with IAH exhibited significantly lower right diaphragm thickening rate (RDTR) and significantly higher right renal interlobar arterial resistive index (RIRI) than those without IAH (all P<0.05). Univariate analysis and multivariate regression analysis showed that the RIRI and RDTR were independent predictors of IAH in the LPD group (P=0.004 and P=0.025, respectively); and the combination of RIRI and RDTR showed promising diagnostic performance for IAH, with an AUROC of 0.894, sensitivity of 93.33%, and specificity of 91.67%. Meanwhile, for the SAP group, only the RDTR served as the marker for screening IAH with an AUROC of 0.872, a sensitivity of 86.67%, and a specificity of 83.33%. Conclusions: This preliminary study suggests that clinicians should remain alert to the occurrence of IAH when abnormal ultrasonic markers are screened. Regular intra-abdominal pressure monitoring is recommended in such cases. However, these findings require validation in larger prospective cohorts.
Dual-tracer [18F]-AV133 and [18F]-FDG PET/MR multiparametric characterization of Parkinson’s disease motor subtypes
Background: Two major motor subtypes are widely recognized in Parkinson’s disease (PD): tremor-dominant (TD) subtype and the postural instability-gait difficulty (PIGD) subtype. These distinct clinical phenotypes imply divergent underlying neural mechanisms. This study aimed to investigate the neural correlates of TD and PIGD subtypes using multiparametric analysis with hybrid positron emission tomography/magnetic resonance (PET/MR) and two tracers: [18F]-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-AV133) and [18F]-fluorodeoxyglucose ([18F]-FDG). Methods: A total of 42 patients with PD (22 TD, 20 PIGD) and 27 healthy controls (HCs) were enrolled. Analysis of variance (ANOVA) and post hoc tests were performed to compare presynaptic dopaminergic function, cerebral glucose metabolism, and gray matter volume (GMV) across groups. Correlation analyses further explored relationships between imaging metrics and Movement Disorder Society-Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS-III) scores. Receiver operating characteristic (ROC) curves were plotted. Results: Relative to TD-PD patients, PIGD-PD patients showed significantly lower [18F]-AV133 binding in the bilateral putamen (contralateral anterior putamen: 1.72±0.33 vs. 2.05±0.56, P=0.033; ipsilateral anterior putamen: 1.57±0.38 vs. 1.82±0.40, P=0.044; contralateral posterior putamen: 1.69±0.40 vs. 1.43±0.17, P=0.011; ipsilateral posterior putamen: 1.37±0.14 vs. 1.55±0.33, P=0.030), with more symmetric dopaminergic impairment in the posterior putamen (0.09±0.11 vs. 0.19±0.17, P=0.025). PIGD-PD also exhibited reduced [18F]-FDG uptake in the lateral/medial prefrontal cortices (P=0.008 and 0.019), putamen (P<0.001), thalamus (P<0.001), pons (P<0.001), and cerebellum (P<0.001). Reduced GMV was noted in the bilateral cerebellum [(lobule IX, lobule IV/V) right lobule IX: P=0.035, left lobule IX: P=0.044, right lobule IV/V: P<0.001, left lobule IV/V: P<0.001], orbital superior frontal gyrus (right: P=0.007, left: P=0.003), postcentral gyrus (right: P=0.025, left: P<0.001), right cerebellar lobule VIII (P=0.048), left cerebellar lobule VIIb (P<0.001), left inferior temporal gyrus (P<0.001), and right thalamus (P=0.005). For PIGD-PD patients, correlation analyses revealed that bilateral putaminal [18F]-AV133 binding, prefrontal/putaminal [18F]-FDG uptake, and GMV in the orbital superior frontal gyrus, middle temporal gyrus, and cerebellum were significantly negatively correlated with UPDRS-III scores (r ranging from −0.833 to 0.607, all P<0.05). Multimodal integration achieved excellent discrimination [area under the curve (AUC) =0.923]. Conclusions: TD-PD and PIGD-PD exhibit distinct patterns of presynaptic dopaminergic dysfunction, cerebral hypometabolism, and gray matter atrophy. These neuroimaging differences may partially account for the divergent clinical manifestations between the two motor subtypes, Multimodal integration achieved excellent discrimination.
Ultrasound-guided transverse myofascial acupotomy for the tibialis posterior muscle in type II painful accessory navicular: a retrospective cohort study with 1-year follow-up
Background: Type II painful accessory navicular (PAN II) commonly causes medial foot pain and functional limitation, and symptoms may persist when conservative care fails to reduce posterior tibial tendon traction on the accessory navicular synchondrosis. This study evaluated whether Ultrasound-Guided Transverse Myofascial Acupotomy for Tibialis Posterior (UG-MA-TP) is associated with better 1-year clinical and biomechanical outcomes than conventional conservative therapy. Methods: This retrospective cohort study included 70 patients with PAN II treated between February 2022 and May 2024 at a single tertiary center. Patients were stratified according to treatment modality into the UG-MA-TP group (n=35) and the conventional therapy group (n=35). Outcomes included the 12-month clinical response rate and magnetic resonance imaging (MRI)-defined bone marrow edema resolution, serial Visual Analog Scale (VAS) pain scores and tibialis posterior/medial gastrocnemius muscle tone, American Orthopaedic Foot & Ankle Society (AOFAS) midfoot scores, surface electromyography-derived normalized root mean square relative values (nRMS-RV), Meary’s angle, and adverse events. Results: The UG-MA-TP group showed significant improvements in VAS and AOFAS scores and a sustained reduction in tibialis posterior muscle tone after intervention (all P<0.05). In contrast, the control group showed transient improvement only at 1 month, followed by a return toward baseline. At 12 months, the UG-MA-TP group had a higher clinical response rate, lower VAS score, higher AOFAS score, higher MRI-defined bone marrow edema resolution rate (82.86% vs. 25.71%), and lower tibialis posterior muscle tone than the control group (all P<0.05). Medial gastrocnemius muscle tone and Meary’s angle remained stable in both groups (P>0.05). Tibialis anterior nRMS-RV was lower, whereas lateral gastrocnemius nRMS-RV was higher, in the UG-MA-TP group than in both the control group and baseline values (all P<0.05). Conclusions: For PAN II, UG-MA-TP was associated with favorable mid-term (1-year) clinical and biomechanical outcomes compared with conventional therapy. It was associated with greater pain reduction, functional improvement, and tibialis posterior muscle tone regulation, with no recorded serious adverse events in this retrospective cohort. This treatment may be associated with favorable neuromuscular adaptations and may represent a minimally invasive option for selected patients with PAN II.
High-pressure-resistant totally implantable venous power ports for improving image quality in contrast-enhanced computed tomography
Background: Contrast-enhanced computed tomography (CECT) is essential for cancer screening, staging, and surveillance, but achieving optimal image quality requires the administration of high-pressure contrast injection via a reliable venous access. Peripheral intravenous catheters (PIVC) have significant limitations in oncology settings, including pain, extravasation risk, and contrast pooling that degrades image quality. Totally implantable venous power ports (TIVPPs) offer a central venous route resistant to high-pressure injection, yet quantitative evidence of their impact on image quality metrics remains scarce. This study aimed to evaluate the effect of TIVPP-based contrast injection on image quality and safety in chest CECT for oncology patients. Methods: In this retrospective fixed-order sequential study, 195 oncology patients underwent two chest CECT scans at a tertiary medical center between August 1, 2022, and July 31, 2024. Each patient first received contrast injection via a PIVC and then via a TIVPP. Objective image parameters, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and CT attenuation values, were measured. Subjective image quality scores, the presence of artifacts, and complication rates were compared. Statistical analyses were performed via paired t-tests and McNemar or Fisher exact tests. Results: The time interval between the PIVC and TIVPP examinations was 131.0±26.2 days (median 128 days; range 71–180 days). Contrast extravasation occurred in 3.1% of examinations performed with PIVCs but in none performed with TIVPPs (P=0.030). Compared to PIVCs, TIVPPs provided significantly higher aortic attenuation (405.50 vs. 363.50 HU; P<0.001), SNR (28.32 vs. 26.45; P=0.005), and CNR (25.83 vs. 21.24; P<0.001). Pulmonary vein image quality was rated as excellent in 100% of TIVPP examinations, higher than the 93.4% of PIVC examinations (P<0.001). Visualization of the subclavian artery was markedly superior with TIVPPs (97.4% excellent) compared with PIVC (0%; P<0.001). Conclusions: TIVPPs provide a safe, reliable, and diagnostically superior route for high-pressure contrast injection in chest CECT for patients with cancer. They maintain significantly superior objective and subjective image quality compared to peripheral venous access, with no contrast extravasation. However, the fixed-order sequential design might have introduced temporal confounding, which should be considered in the interpretation of our findings.
Distinct brain susceptibility alteration pattern in Parkinson’s disease with drooling: evidence from quantitative susceptibility mapping
Background: Drooling is a common non-motor symptom in Parkinson’s disease (PD) and can substantially impair quality of life, but its underlying neural mechanisms remain incompletely understood. Quantitative susceptibility mapping (QSM) provides a non-invasive approach for assessing susceptibility alterations related to brain iron deposition. This cross-sectional study aimed to investigate QSM-derived susceptibility alterations in PD patients with drooling (PD-DR) and to explore their associations with drooling severity. Methods: A total of 103 participants, including 38 PD-DR, 23 PD patients without drooling (PD-NDR), and 42 healthy controls (HC), underwent three-dimensional (3D) magnetic resonance imaging including regular sequences and QSM. Voxel-wise whole-brain analysis and region of interest (ROI)-based analysis focusing on subcortical nuclei were performed to compare magnetic susceptibility values among groups, with voxel-wise significance set at voxel-level P<0.001 and cluster-level family-wise error-corrected P<0.05. Partial correlation analyses were performed in the PD-DR group to examine associations between susceptibility alterations in identified regions and drooling severity, after adjusting for age, sex, disease stage, and motor severity. Results: Voxel-wise analysis revealed significantly increased susceptibility in the left superior temporal gyrus (STG) in the PD-DR group compared to the PD-NDR group (voxel-level P<0.001, cluster-level family-wise error-corrected P<0.05). In the PD-DR group, susceptibility values in the left STG showed a significant positive correlation with SCS-PD scores after adjusting for age, sex, Hoehn-Yahr stage, and Unified Parkinson’s Disease Rating Scale Part III score (r=0.361, P=0.036). Compared to the HC group, the PD-DR group exhibited more widespread cortical susceptibility alterations. ROI analysis indicated higher susceptibility values in the left ventral pallidum (VP) and left parabrachial pigmented nucleus (PBP) in the PD-DR group compared to that in the HC group (Bonferroni-corrected P=0.042 and P=0.043, respectively). Furthermore, susceptibility values in the left PBP were significantly positively correlated with SCS-PD scores (r=0.506, P=0.002). Conclusions: Drooling in PD is associated with a distinct pattern of brain susceptibility alterations, supporting the involvement of a distributed neural network. Key regions include the left STG and the left PBP, which may be related to sensory integration, interoception, motivation, and motor control.
Diagnostic performance of deep learning models in differentiating benign and malignant pulmonary nodules: a systematic review and meta-analysis
Background: Lung cancer is the most lethal malignant tumor globally. Accurate differentiation between benign and malignant pulmonary nodules (PNs) is the core of early screening and diagnosis for lung cancer. Deep learning (DL) models can autonomously extract high-dimensional information from medical images, demonstrating technical advantages in this task. Our study systematically evaluates the accuracy and efficacy of DL models in differentiating benign from malignant PNs, providing systematic, high-quality integrated evidence for their diagnostic performance. Methods: Our study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA) reporting checklist. We searched the databases of PubMed, Cochrane Library, Embase, and Web of Science for relevant studies published up to 24 September 2025. Two researchers independently screened the literature according to inclusion and exclusion criteria, using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) to assess the risk of bias and applicability of the included studies. The pooled sensitivity, specificity, and area under the curve (AUC) were used to evaluate the diagnostic performance of DL models. Subgroup analysis and meta-regression were employed to investigate sources of heterogeneity. Sensitivity analysis and publication bias tests were conducted concurrently, and a Fagan plot was generated to assess clinical utility. Results: A total of 21 retrospective studies were ultimately included. The QUADAS-2 quality assessment indicated that the overall risk of bias in the included studies was manageable. The combined sensitivity, specificity, and AUC of DL models for distinguishing between benign and malignant PNs were 0.92 [95% confidence interval (CI): 0.90–0.93], 0.90 (95% CI: 0.86–0.92), and 0.96 (95% CI: 0.94–0.98). The pooled sensitivity and specificity were heterogeneous (I2>75%). A threshold effect test confirmed that no significant threshold effect was observed in our study (Spearman’s r=0.221, P=0.336). Subgroup analysis and meta-regression confirmed that model validation method was the primary source of heterogeneity in our study (P=0.00), with cross-validation models demonstrating significantly higher specificity than train–test split models (0.92 vs. 0.84, P=0.00). Models based on the Transformer architecture outperformed convolutional neural networks (CNNs) in terms of both sensitivity (0.93 vs. 0.92, P=0.00) and specificity (0.91 vs. 0.89, P=0.00). Sensitivity analysis confirmed the stability of the pooled results, and the funnel plot revealed no significant publication bias (P=0.56). The Fagan plot indicated that at a pretest probability of 50%, the model achieved a positive predictive post-test probability of 90% and a negative predictive post-test probability of only 8%, demonstrating good clinical utility. Conclusions: DL models demonstrate both high sensitivity and specificity in distinguishing benign from malignant PNs, exhibiting outstanding overall diagnostic performance. With prospective validation and technical optimization in the future, their clinical utility is expected to improve further.
A YOLOv8 deep learning model for detecting labral injury via hip magnetic resonance imaging
Background: Acetabular labral injury is a common traumatic lesion of the hip joint and one of the most common hip disorders that causes hip pain. Accuracy in the diagnosis of labral injury depends on the level of experience of the interpreting radiologist. This study aimed to determine the feasibility of using the You Only Look Once version 8 (YOLOv8) deep learning model for acetabular labral injury detection on proton density-weighted fast spin-echo (PD-FSE) magnetic resonance imaging (MRI) sequences. Methods: Adult patients who underwent oblique sagittal and oblique coronal plane MRI examinations between 2021 and 2025 were enrolled. PD-FSE sequence images were annotated to construct a dataset, and the YOLOv8 algorithm was used to develop the two-dimensional (2D) slice-level deep learning model. A total of 936 PD-FSE sequence images were collected from 111 patients. The dataset was split at the patient level into training (76 patients and 675 images), validation (23 patients and 169 images), and test (12 patients and 92 images) sets. A labrum-absent test set consisting of 58 images was established to evaluate the generalization performance of the models. Results: The accuracy, sensitivity, and specificity of the optimal YOLOv8 model (YOLOv8m) were 0.88 [95% confidence interval (CI): 0.82–0.92], 1.00 (95% CI: 0.96–1.00), and 0.74 (95% CI: 0.62–0.82), respectively. No statistically significant difference was detected between the YOLOv8m model and senior chief radiologists (P=0.864), while the model significantly outperformed junior radiologists (P=0.017). The misjudgment rate of the optimal YOLOv8m model in the labrum-absent test set was 12.1%. This is the first study to establish a YOLOv8-based 2D slice-level model for detecting labral injury detection. Conclusions: The deep learning model constructed based on YOLOv8 demonstrated diagnostic performance comparable to that of senior radiologists in detecting acetabular labral injuries. This model demonstrates promising potential for future clinical decision support in suspected acetabular labral injury management.
Evaluation of the ability of hemostatic agents to prevent hemoptysis during ultrasound-guided lung biopsy
Background: Low-dose computed tomography screening has substantially improved the detection of peripheral pulmonary lesions (PPLs). Ultrasound-guided lung biopsy is widely used for diagnosis; however, postoperative hemoptysis is a critical complication, and the value of routine prophylactic hemostasis remains unclear. This study aimed to investigate the risk factors for hemoptysis following ultrasound-guided percutaneous lung biopsy and evaluate the advantages and disadvantages of prophylactic hemostatic agents in high- and low-risk patients. Methods: A total of 697 patients who underwent ultrasound-guided peripheral lung biopsy were retrospectively enrolled in this study. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for postoperative hemoptysis, and receiver operating characteristic curve analysis was used to determine the optimal cutoff value for lesion thickness. Subsequently, 126 patients were prospectively recruited and randomly divided into four subgroups: high-risk with hemostatic agents, high-risk without hemostatic agents, low-risk with hemostatic agents, and low-risk without hemostatic agents. Quantitative perioperative indicators, including hemoptysis incidence and volume, blood oxygen saturation, pulse rate, hemoglobin, platelet count, and coagulation function indices, were statistically compared among groups, and hemostatic agent-related adverse complications were recorded. Results: The overall post-biopsy hemoptysis rate was 9.03% (63/697). Multivariate regression analysis confirmed that lesion thickness <15 mm and grade 2 air bronchial signs were independent risk factors for hemoptysis (all P<0.05). The optimal cutoff value of lesion thickness was 15 mm, with an area under the curve (AUC) of 0.713, a sensitivity of 92.4%, and a specificity of 52.4%. For high-risk patients, prophylactic hemostatic agents significantly reduced the hemoptysis rate (15.15% vs. 39.29%, P=0.034) and hemoptysis volume (P=0.014), and effectively stabilized postoperative blood oxygen saturation, compared with the non-hemostatic group. In low-risk patients, no significant differences in hemoptysis incidence, hemoptysis volume, or blood oxygen saturation were observed between patients who received hemostatic agents and those who did not (all P>0.05). Additionally, no significant differences between pre- and postoperative measurements were observed in pulse rate, hemoglobin level, platelet count, or any coagulation index across all subgroups (all P>0.05). The incidence of hemostatic agent-related adverse reactions was 7.58% (5/66), and all adverse symptoms were relieved after symptomatic treatment. Conclusions: Lesion thickness <15 mm and grade 2 air bronchial signs were identified as independent risk factors for hemoptysis following ultrasound-guided biopsy of PPLs. Prophylactic hemostatic agents may help reduce the hemoptysis rate and volume in patients with at least one high-risk factor. However, given the moderate predictive performance of these risk factors (AUC =0.713), individualized clinical judgment remains essential.
Prediction of postnatal treatment strategy in fetuses with isolated pulmonary stenosis
Background: Fetuses with pulmonary stenosis (PS) may undergo surgical valvulotomy, pulmonary valvuloplasty via interventional catheterization or expectant management after birth, a predictive model for the possibility of surgery has not yet been established in previously published reports. This study aims to identify prenatal prognostic factors for a postnatal management approach in fetuses with isolated PS in a retrospective cohort of non-intervened fetuses between 24 and 34 weeks and to evaluate its performance prospectively. Methods: Baseline characteristics and outcomes of the study population were recorded. Prenatal cardiac and hemodynamic parameters were stratified by postnatal intervention treatment (surgical valvulotomy or percutaneous balloon pulmonary valvuloplasty) or expectant treatment at 1 year of age after birth. Classification and regression tree analysis was conducted to investigate the predictive capacity of prenatal echocardiographic parameters for postnatal treatment strategy at 1 year of age. We also prospectively validated it in the fetuses with isolated PS. Results: A total of 71 live-born fetuses with isolated PS were enrolled from January 2014 to September 2022 in our institution for model construction. Among them, 37 received postnatal intervention treatment, including 17 who underwent surgical valvulotomy and 20 who underwent percutaneous balloon pulmonary valvuloplasty. The classification and regression tree model based on the right ventricular (RV) pressure, which was recorded as the maximum velocity of tricuspid valve regurgitation (TR-Vmax) combined with the RV/left ventricular (LV) length ratio and prenatal peak systolic velocity of the pulmonary valve (PV-PSV), showed a sensitivity of 100% and specificity of 100% for predicting postnatal outcomes. The highest probability of postnatal intervention treatment was attained for fetuses with TR-Vmax >135.5 cm/s (100%). The predictors were validated in another 10 fetuses from October 2022 to August 2023 in our institution prospectively. The sensitivity was 100% and specificity was 100% for predicting postnatal treatment strategy. Conclusions: Prenatal RV pressure estimates, which were demonstrated as TR-Vmax combined with the RV/LV length ratio and PV-PSV, could predict the postnatal management strategy for isolated PS with high sensitivity and specificity.
Interpretable machine learning model based on non-contrast computed tomography within 24 hours of admission for early prediction of the risk of severe deterioration of acute pancreatitis
Background: Early prediction of progression to severe acute pancreatitis (SAP) remains challenging due to the lack of reliable tools within 24 hours of admission. Conventional contrast-enhanced computed tomography (CT) is often unavailable in emergency settings, and non-contrast CT alone provides limited information for severity assessment. Therefore, this study aimed to construct an early prediction model for SAP risk using intrapancreatic and peripancreatic radiomics features from non-contrast CT obtained within 24 hours of admission, combined with clinical characteristics, to provide a theoretical basis for early clinical intervention. Methods: A retrospective analysis was conducted involving 585 patients admitted within 24 hours after acute pancreatitis (AP) onset. Patients were classified according to the Revised Atlanta Classification [2012] at 48 hours after admission, resulting in 148 cases progressing to SAP and 437 cases classified as mild to moderate acute pancreatitis (MAP/MSAP). ITK-SNAP software was used to manually outline the pancreas contour on non-contrast CT images obtained within 24 hours of admission for AP. Regions were expanded outward from the pancreatic contour by 2 mm, 4 mm, and 6 mm, respectively. Radiomics features were extracted and selected via intraclass correlation coefficient (ICC >0.75), Spearman correlation (>0.9), least absolute shrinkage and selection operator (LASSO) regression, and maximum relevance minimum redundancy (mRMR) algorithms. The optimal peripancreatic expansion distance was determined by comparing predictive performance. A combined radiomics-clinical model was built using the eXtreme Gradient Boosting (XGBoost) algorithm and evaluated by area under the receiver operating characteristic (ROC) curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was used for model interpretability. Results: The 4 mm peripancreatic region showed the best predictive performance. After feature selection, 35–47 robust features were retained across different regions of interest (ROIs). The combined model, incorporating intrapancreatic plus 4 mm peripancreatic radiomics features and clinical parameters [procalcitonin (PCT), white blood cell (WBC) count, calcium (Ca), lymphocyte count (LYM), albumin (ALB), lactate dehydrogenase (LDH)], yielded an AUC of 0.852 [95% confidence interval (CI): 0.782–0.921] in the validation cohort, which was significantly higher than the clinical model alone (AUC =0.807) and the radiomics model alone (AUC =0.829). The calibration curve showed good agreement between predicted and observed probabilities, and DCA demonstrated superior net benefit of the combined model across a threshold range of 0.1–0.6. SHAP analysis revealed that intrapancreatic heterogeneity and peripancreatic density were critical predictive factors. Both SHAP analysis and the developed nomogram demonstrated robust clinical interpretability. Conclusions: Radiomics features extracted from the 4 mm peripancreatic region on non-contrast CT scans obtained within 24 hours of admission, combined with clinical characteristics, effectively predict the risk of progression to SAP. This predictive model can support clinical evaluation and guide treatment decisions.
Quantitative assessment of pancreas R2* in cirrhosis with systemic iron overload: relationship with serum iron parameters and metabolic complications
Background: Although pancreatic iron deposition is linked to metabolic dysfunction in hereditary hemochromatosis and transfusional iron overload, its significance in cirrhosis with secondary iron overload remains unclear. We quantitatively assessed pancreatic R2* in cirrhotic patients with and without systemic iron overload using magnetic resonance imaging (MRI)-based R2* relaxometry, evaluated its correlation with hepatic iron content and serum iron parameters, and examined its association with 5-year metabolic outcomes. Methods: This retrospective observational cohort study included 170 cirrhotic patients who underwent magnetic resonance elastography (MRE) with multi-echo proton density fat fraction (PDFF)/R2* mapping between January 2018 and October 2024. Pancreatic and liver R2* values were measured. Patients were categorized into iron overload and normal iron status based on iron parameters [transferrin saturation (TSAT) >45% and ferritin >200 µg/L in women and TSAT >50% and ferritin >300 µg/L in men]. Correlations between pancreatic R2*, serum iron parameters, liver function markers, and MRI-derived measures were analyzed. The 5-year cumulative incidence of metabolic complications including dyslipidemia, prediabetes, and type 2 diabetes mellitus (T2DM) was assessed. Results: Pancreatic R2* values were higher in patients with iron overload compared to those with normal iron status, but this difference was observed only in patients with alcoholic liver cirrhosis {26.2 [interquartile range (IQR), 19.5–37.9] vs. 34.7 [IQR, 24.8–345.3] s−1, P=0.001}. Among patients with iron overload, pancreatic R2* values correlated with serum ferritin {adjusted r=0.175 [95% confidence interval (CI): 0.016–0.389], P=0.014} and total iron-binding capacity (TIBC) [adjusted r=−0.175 (95% CI: −0.369 to −0.015), P=0.018] but showed no correlation with liver iron concentration (LIC) [adjusted r=0.292 (95% CI: −0.030 to 0.558), P=0.058]. In this subgroup, pancreatic R2* values also correlated with higher Child-Pugh [adjusted r=0.371 (95% CI: 0.039–0.645), P=0.014] and Model for End-Stage Liver Disease (MELD) scores [adjusted r=0.383 (95% CI: 0.098–0.622), P=0.011]. However, pancreatic R2* did not predict the development of dyslipidemia, prediabetes, or T2DM (all P>0.05). Conclusions: Pancreatic R2* values correlated with serum iron parameters and liver disease severity, but not with LIC or future metabolic complications.
Relationship between subendocardial viability ratio and cardiovascular disease risk in hypertensive patients
Background: The subendocardial viability ratio (SEVR), which correlates with coronary flow reserve, has been proposed as an indirect prognostic marker for cardiovascular disease (CVD). This study aimed to investigate the relationship between SEVR and cardiovascular risk scores, and to evaluate its value in identifying increased cardiovascular risk in hypertensive population. Methods: A total of 1,380 participants (782 hypertensive patients and 598 normotensive controls) were enrolled. Demographic characteristics, laboratory parameters, arterial stiffness, left ventricular structural and functional indices, and 10-year atherosclerotic CVD (ASCVD) risk calculated via the China Atherosclerotic CVD Risk Prediction model (China-PAR) were collected and analyzed. Multivariable linear regression and logistic regression analyses were conducted to identify independent determinants of SEVR and factors linked to high ASCVD risk (>10%). Results: Mean SEVR was significantly lower in the hypertensive group than in the control group (130.75% vs. 136.38%, P<0.001). SEVR was negatively correlated with age, heart rate (HR), systolic blood pressure (SBP), E/e' ratio, arterial velocity index, and arterial pressure-volume index (all P<0.001). Multivariable linear regression identified age (β=−0.106, P=0.001), HR (β=−0.618, P<0.001), SBP (β=0.024, P=0.023), arterial pressure volume index (API) (β=−0.338, P<0.001), and hemoglobin (β=0.219, P=0.001) as independent determinants of SEVR. Logistic regression (Model 2, adjusted for sex, smoking, SBP, and HR) showed that SEVR was independently associated with a 10-year ASCVD (10Y-ASCVD) risk >10% [odds ratio (OR) =0.953, 95% confidence interval (CI): 0.940–0.965, P<0.001]. Male sex (OR =1.973, P=0.001), current smoking (OR =2.665, P=0.001), and SBP (OR =1.015, P=0.003) were significant risk factors, while HR was protective (OR =0.949, P<0.001). In risk stratification, the high-risk group (10Y-ASCVD risk >10%) had significantly lower SEVR than the low-risk group in both hypertensive (122.72% vs. 131.92%, P<0.05) and control subjects (123.91% vs. 137.67%, P<0.05). Receiver operating characteristic analysis revealed that SEVR identified high ASCVD risk with an area under the curve of 0.706 in controls and 0.630 in hypertensives. Conclusions: SEVR is inversely associated with 10Y-ASCVD risk in individuals with hypertension and remains an independent indicator after adjustment for conventional risk factors. As a noninvasive and cost-effective marker reflecting myocardial oxygen supply-demand balance, SEVR has the potential to add complementary value to current cardiovascular risk assessment models, thereby refining risk stratification in patients with hypertension.
Association between acute infarct topography and cerebral microbleed location in acute ischemic stroke: a retrospective cross-sectional magnetic resonance imaging study
Background: Cerebral microbleeds (CMBs) are important imaging markers of cerebral small vessel disease and are frequently detected in patients with stroke. However, imaging evidence regarding spatial associations between infarct topography and CMBs detected on the index magnetic resonance imaging (MRI) examination in patients with acute ischemic stroke (AIS) remains limited. This study aims to investigate the association between acute infarct location and CMB location on brain MRI. Methods: This retrospective cross-sectional study included cranial MRI data from patients with AIS. Acute infarcts were identified on diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC) and classified into six predefined neuroanatomical regions. CMBs detected on the index MRI examination were identified on susceptibility-weighted imaging (SWI) and recorded using the same regional scheme. Two experienced readers independently interpreted the images under blinded conditions, and disagreements were resolved by consensus adjudication. Multivariable logistic regression was performed to evaluate factors associated with the presence of CMBs after limited adjustment for sex, age, and infarct location. Exploratory regression analyses were further performed to assess associations between infarct location and region-specific CMBs. Results: A total of 138 patients with AIS were included, among whom 79 (57.2%) had CMBs detected on SWI. In multivariable logistic regression analysis, after limited adjustment for sex, age, and infarct location, corona radiata infarction was associated with the presence of CMBs [odds ratio (OR) =3.61, 95% confidence interval (CI): 1.52–8.61, P=0.004]. Exploratory univariable analyses showed that cortical infarction was associated with cortical CMBs (OR =2.90, 95% CI: 1.37–6.13, P=0.005), while corona radiata infarction was associated with corona radiata CMBs (OR =3.82, 95% CI: 1.83–8.00, P<0.001). In addition, increasing age (OR =1.11, 95% CI: 1.02–1.20, P=0.013) and thalamic infarction (OR =5.36, 95% CI: 1.18–24.46, P=0.030) showed exploratory associations with cerebellar CMBs. Conclusions: In this retrospective single-center MRI study, corona radiata infarction was associated with CMBs detected on the index MRI examination after limited adjustment for demographic variables and infarct location. Region-specific associations between acute infarct location and CMB location were exploratory and should be interpreted cautiously, given the cross-sectional design, limited covariate adjustment, small sample size, and potential scanner heterogeneity. Further prospective multicenter studies using standardized MRI protocols and more comprehensive clinical and imaging covariates are needed to validate these findings.
Point-of-care ultrasound as a frontline tool for suspected acute aortic dissection: a systematic review and meta-analysis
Background: Acute aortic dissection (AD) is a life-threatening cardiovascular emergency associated with high morbidity and mortality. Although computed tomography angiography (CTA) remains the reference diagnostic modality, rapid bedside assessment is often required in hemodynamically unstable patients. Point-of-care ultrasound (POCUS) has emerged as a potential adjunctive tool for early diagnosis and risk stratification. This systematic review and meta-analysis evaluated the diagnostic performance and clinical utility of POCUS in patients with suspected acute AD. Methods: A systematic review and meta-analysis were conducted in accordance with PRISMA 2020 guidelines. Ovid MEDLINE, PubMed, EMBASE, Scopus, and Web of Science were searched for studies published between January 2005 and January 2025. Studies evaluating the diagnostic accuracy of POCUS in adult patients with suspected AD were included. Two reviewers independently performed study selection, data extraction, and quality assessment using the QUADAS-2 tool. Pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratios (DOR), and summary receiver operating characteristic (SROC) analyses were calculated. Results: Eleven studies met the inclusion criteria, and ten contributed data to the quantitative synthesis. The pooled analysis demonstrated an overall sensitivity of 0.86 [99% confidence interval (CI): 0.80–0.90] and specificity of 0.84 (99% CI: 0.78–0.89) for bedside ultrasound in the detection of acute AD. Diagnostic performance was more favourable for Stanford type A disease than for distal dissections. Visualization of an intimal flap was the most specific sonographic finding, whereas pericardial effusion, aortic root enlargement, and acute aortic regurgitation provided important complementary information for clinical assessment. Evidence from recent perspective cohorts further supports the incorporation of POCUS into structured diagnostic pathways for patients with suspected acute aortic syndromes (AAS). Conclusions: Bedside ultrasound represents a valuable component of the initial evaluation of suspected acute AD, particularly in emergency and critical care environments. Although confirmatory imaging remains mandatory for definitive diagnosis, POCUS can facilitate early recognition of AAS, support hemodynamic assessment, and help identify complications requiring urgent intervention.
Investigation of type 2 diabetes mellitus and mild cognitive impairment: a study based on diffusion tensor imaging analysis along the perivascular space and peak width of skeletonized mean diffusivity
Background: Mild cognitive impairment (MCI) is a common complication of type 2 diabetes mellitus (T2DM); however, its underlying pathogenesis remains unclear. This study aimed to employ diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) and peak width of skeletonized mean diffusivity (PSMD) to investigate changes in the perivascular space (PVS) microenvironment in T2DM. Methods: Patients with T2DM (24 with MCI and 23 without MCI) and healthy controls (HCs) (n=26) were prospectively recruited. All participants underwent the Montreal cognitive assessment (MoCA) and diffusion tensor imaging (DTI). The analysis along the perivascular space (ALPS) index was calculated using the FMRIB software library (FSL), and PSMD was derived based on tract-based spatial statistics (TBSS). One-way analysis of variance (ANOVA) was used for comparisons among three groups. Correlations of the ALPS index and PSMD with MoCA scores, fasting blood glucose (FBG) levels, and disease duration were analyzed. Results: The ALPS index was significantly lower in the type 2 diabetes mellitus with mild cognitive impairment (T2DM-MCI) (mean ± standard deviation: 1.324±0.170) and type 2 diabetes mellitus without mild cognitive impairment (T2DM-nMCI) (1.362±0.142) groups than in the HC group (1.465±0.104) (P<0.05), while PSMD (×10–4 mm2/s) was significantly higher in the T2DM-MCI group [median (25th and 75th percentile): 2.139 (1.895–2.306)] than in the T2DM-nMCI [1.868 (1.777–2.026)] and HC [1.888 (1.776–2.111)] groups (P<0.05). PSMD was negatively correlated with MoCA scores (r=–0.425, P=0.003) and positively correlated with disease duration (r=0.433, P=0.002). The ALPS index was negatively correlated with FBG levels, disease duration, and PSMD (r=–0.382, P=0.008; r=–0.420, P=0.003; r=–0.333, P=0.022, respectively). Conclusions: The combined use of DTI-ALPS and PSMD provides a robust, non-invasive dual-biomarker framework that may advance understanding of the mechanisms underlying diabetic MCI and serve as a potential imaging biomarker.
Prognostic impact of epicardial fat volume, anatomical and functional imaging across the cardiovascular risk spectrum in patients with suspected coronary artery disease: a single-center retrospective cohort study
Background: Epicardial adipose tissue (EAT) is associated with coronary artery disease (CAD) and adverse cardiovascular outcomes; however, its prognostic relevance across different cardiovascular risk scores remains uncertain. This study evaluated the long-term prognostic significance of EAT, coronary anatomical findings, and functional ischemia in patients with suspected CAD stratified by Framingham risk score (FRS) categories. Methods: A consecutive retrospective cohort of 361 symptomatic patients who underwent both single-photon emission computed tomography/computed tomography myocardial perfusion imaging (SPECT/CT MPI) and coronary angiography (CAG) or coronary computed tomography angiography (CCTA) at baseline was analyzed. Epicardial fat volume (EFV) and coronary artery calcium score (CACS) were quantified from integrated computed tomography (CT) with SPECT/CT. Ischemia burden was defined as ≥5% ischemic myocardium on MPI, and obstructive CAD as ≥50% stenosis. Major adverse cardiovascular events (MACE) were recorded during follow-up. Results: Over a median follow-up of 4.6 years, 54 patients (15%) experienced MACE. In the multivariable analysis, EFV [adjusted hazard ratio (aHR) =2.09; 95% confidence interval (CI): 1.08–4.05; P=0.029], CACS (aHR =2.58; 95% CI: 1.39–4.80; P=0.003), and obstructive CAD (aHR =3.07; 95% CI: 1.59–5.90; P<0.001) were independently associated with MACE, while ischemia burden showed borderline significance (aHR =2.27; 95% CI: 0.94–5.49; P=0.069). EFV improved risk discrimination over FRS, CACS, obstructive CAD, and ischemia burden (all P<0.05). In the stratified analyses, CACS (aHR =2.88; P=0.017) and obstructive CAD (aHR =3.69; P=0.005) predicted MACE in low-to-intermediate FRS patients, whereas EFV (aHR =3.13; P=0.020) and ischemia burden (aHR =4.67; P=0.005) were only associated with MACE in high-risk FRS patients. EFV was significantly associated with MACE in patients with diabetes (aHR =6.61; P=0.010), but not in those without diabetes (aHR =1.45; P=0.359), with ischemia showing a similar pattern. Conclusions: EFV independently predicted long-term MACE and improved prognostic discrimination beyond clinical risk, anatomical imaging, and functional ischemia. Prognostic determinants varied by cardiovascular risk level, with anatomical markers being more informative in lower-risk patients, while EFV and functional ischemia were more informative in high-risk and diabetic patients. These findings support a risk-adapted strategy for the use of quantitative imaging biomarkers in patients with suspected CAD.
Left ventricular rotational characteristics in healthy mid-term pregnancy—insights from the three-dimensional speckle-tracking echocardiographic MAGYAR-Preg Study
Background: Haemodynamic, neurohormonal, plasma volume, haematological, and structural cardiac changes occur during healthy pregnancy to varying degrees depending on the trimester. The aim of the present observational cross-sectional study was to compare left ventricle (LV) rotational mechanics in healthy pregnant subjects with those of age-matched non-pregnant women using three-dimensional speckle-tracking echocardiography (3DSTE). Methods: The initial study population consisted of 27 healthy subjects in mid-term pregnancy; however, 6 cases were excluded due to suboptimal image quality. Consequently, the final analytical cohort comprised 21 subjects (mean age: 30.7±2.7 years). This group was compared to a control group of 27 age-matched healthy non-pregnant volunteers (mean age: 28.4±6.0 years). Results: In three pregnant subjects, the near absence of LV rotational mechanics, referred to as rigid body rotation (RBR), was observed (3 of 21, 14%) with counterclockwise orientation. The remaining 18 subjects showed normally directed LV rotational mechanics (18 of 21, 86%). A significant reduction in apical LV rotation (4.0±3.9° vs. 9.2±3.2°, P<0.05) and LV twist (6.8±3.6° vs. 13.1±3.4°, P<0.05) could be detected in healthy pregnant women compared with non-pregnant subjects. Basal LV rotation did not differ significantly between the two groups (−3.2±2.3° vs. −3.8±1.8°, P=ns). Time to peak apical LV twist tended to be shorter in pregnant women; however, this difference did not reach statistical significance. Conclusions: Reduced apical LV rotation and consequent LV twist with preserved basal LV rotation could be detected in healthy mid-term pregnancy. In some cases, counterclockwise LV-RBR was observed.
Ultrasonographic identification of pelvic floor myofascial trigger points and their correlation with clinical symptoms in patients with pelvic floor myofascial pain
Background: Pelvic floor myofascial pain (MFP) is a common yet often underdiagnosed condition, and the clinical significance of ultrasonographically identified myofascial trigger points (MTrPs) remains insufficiently understood. The aim of this study was to assess the correlation between ultrasonographically identified MTrPs and clinical manifestations of pelvic floor MFP. Methods: A retrospective review was conducted on medical records of 70 patients diagnosed with MFP at Yiwu Traditional Chinese Medicine Hospital between January 2024 and June 2025. MTrPs in the obturator internus, piriformis, levator ani, and coccygeus muscles were identified using transvaginal ultrasonography. Pain scores were recorded, and correlation analyses were conducted to examine the relationships between the distribution of MTrP and reported pain symptoms. Results: Dyspareunia was significantly correlated with MTrPs in the left piriformis muscle (rs =0.222, P=0.013). Vaginal pain was significantly associated with MTrPs in the right coccygeus and right piriformis muscles (rs =0.216 and 0.217, both P<0.05). Left groin pain was correlated with MTrPs in the bilateral levator ani and left obturator internus muscles (rs =0.197, 0.226, and 0.184, all P<0.05). Buttock pain, low back pain, and lower abdominal pain bilaterally demonstrated significant correlations with MTrPs in the coccygeus, piriformis, and levator ani muscles (rs values ranging from 0.179 to 0.825, all P<0.05). Conclusions: Transvaginal ultrasonography-identified trigger point-like findings may be associated with specific pain distributions, suggesting a potential role for ultrasound-guided assessment in understanding pelvic floor pain patterns and contributing to improved pain management and quality of life among patients with MFP.
Robust phase unwrapping for Dixon magnetic resonance imaging multiresolution pixel clustering and local surface fitting
Background: Water-fat separation plays a crucial role in clinical magnetic resonance imaging (MRI) examinations. However, the Dixon technique is encumbered by the challenge of water-fat swapping due to factors such as magnetic field inhomogeneity and low image signal-to-noise ratio (SNR). This study aimed to develop a multiresolution pixel clustering and local surface fitting (MrCLOSE) phase-unwrapping method to improve the robustness of water-fat separation in two-point Dixon MRI. Methods: MrCLOSE first constructs a multiresolution pyramid via acquired in-phase and out-of-phase complex MRI. It then performs hierarchical phase unwrapping with pixel clustering and local surface fitting (CLOSE) within a coarse-to-fine multiresolution framework. The performance of MrCLOSE was systematically evaluated and compared with those of B0 mapping with magnitude-based correction for bipolar two-point Dixon (B0-NICEbd), rapid opensource minimum spanning tree algorithm (ROMEO), Graph Cut, and the original CLOSE algorithm with both simulation and in vivo datasets. Results: In the simulation data, MrCLOSE achieved robust phase unwrapping under different thresholds, varying SNRs, disconnected regions, and phase changes from 4π to 10π. In the threshold-dependence simulation, CLOSE showed high error ratios of 43.71% and 94.74% at thresholds of π/10 and π/5, whereas MrCLOSE reduced these values to 0.67% and 0.11%, respectively. In additional simulations involving varying SNRs, disconnected regions, and increasing phase changes, MrCLOSE consistently maintained low error ratios and outperformed ROMEO and Graph Cut. In the 355 in vivo MR images acquired from the ankle, lumbar spine, pelvis, and knee, the overall water-fat swap ratio was 3.38% for MrCLOSE, while it was 17.75% for B0-NICEbd, 96.34% for ROMEO, 67.61% for Graph Cut, and 16.34% for CLOSE. Conclusions: By innovatively integrating multiresolution strategies into CLOSE, MrCLOSE achieves high robustness and accuracy in phase unwrapping and provides a promising solution for phase-related MRI applications, particularly for water-fat separation in clinical scenarios.
Comparative evaluation of diaphragmatic and myocardial navigation in coronary magnetic resonance imaging
Background: Coronary magnetic resonance angiography (CMRA) is limited by respiratory motion artifacts, for which diaphragmatic navigation (dNAV) is commonly applied. Myocardial navigation offers a direct motion-tracking alternative, but its performance in different anatomical placements remains underexplored. This study aimed to compare the image quality and clinical feasibility of diaphragmatic and myocardial navigation approaches at 3.0 T. Methods: Thirty-three healthy volunteers underwent CMRA with four navigators: dNAV, left ventricle navigation (LvNAV), right atrial navigation (RaNAV), and apex navigation (ApNAV). Acquisition efficiency, scan duration, success rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were analyzed for major coronary segments: right coronary artery (RCA), left anterior descending artery (LAD), and left circumflex artery (LCX). Two observers independently scored image clarity and sharpness (4-point scale) for segments. Interobserver agreement was assessed via intra-class correlation coefficient (ICC). Results: LvNAV and RaNAV both achieved a 100% technical completion rate (30/30). This was higher than the technical completion rates for ApNAV (97%, 29/30) and dNAV (90%, 27/30), with the latter two groups experiencing failures due to navigator drift. Scan durations showed no significant differences (dNAV: 478.70±104.68 s; LvNAV: 469.62±102.64 s; P=0.332). LvNAV yielded the highest SNR and CNR (P<0.05). Proximal and mid segments of the RCA, LAD, and LCX showed comparable visualization between dNAV and LvNAV (P>0.05), whereas distal segment visibility was reduced mainly with RaNAV and ApNAV, with dNAV and LvNAV preserving better distal visualization. Coronary visualization rates differed significantly (χ2=76.563, P<0.001), with dNAV and LvNAV outperforming other techniques. Conclusions: Myocardial navigation, particularly LvNAV, yields superior image quality and visualization performance comparable to dNAV, representing a promising alternative under conditions requiring enhanced motion stability. Personalized navigator selection based on respiratory patterns may further optimize CMRA outcomes.
Evaluation of the extent of bone healing in mandibular distraction osteogenesis using pre-operative and post-operative imaging pixel value ratio: a cohort study
Background: The pixel value ratio (PVR) is a metric measured on X-ray images that has been used in many studies to assess the time of final external fixator removal. The aim of this study was to investigate the value and potential influencing factors of PVR in the early phase of mandible healing by distraction osteogenesis (DO) in Pierre Robin sequence. Methods: We performed a retrospective analysis on 342 Pierre Robin sequence patients with bilateral mandibular lengthening surgery, measured PVRs in the osteotomy position of children before surgery, and monitored PVRs in the early stage of bone regeneration at the 1st, 2nd, and 3rd month respectively after surgery. The potential effects of gender, weight, and age of fixation period were also analyzed. Results: PVRs before osteotomy and at the 1st, 2nd, and 3rd month afterward were 0.96±0.04 (R) and 0.97±0.05 (L); 0.4±0.1 (R) and 0.42±0.1 (L); 0.68±0.11 (R) and 0.71±0.11 (L); and 0.97±0.04 (R) and 0.98±0.04 (L), respectively, which had significant differences (P<0.001). We found no significant difference in PVRs by gender. In terms of age, there was a significant difference in PVRs between children aged less than 28 days and older than 120 days before surgery. There were also significant differences between those aged less than 28 days and older than 120 days, and children aged 8–28 days and 29–60 days at the 3rd month after surgery. In terms of weight, the PVRs of children with a birth weight less than 4 kg and those with an admission weight less than 2.5 kg were significantly different. Conclusions: During the infancy period, the healing process of the mandibular DO focuses on the early healing phase (1–3 months). Our results showed that PVRs gradually increased during the first 3 months after mandibular osteotomy and during the process of DO. Gender had no effect on PVRs, whereas age, birth weight, and admission weight had a greater effect. The results of this study suggest that PVRs can be used as an indicator for the assessment of early lengthening bone growth in patients with mandibular lengthening and for determining whether to remove the distraction osteogenic apparatus.
Navigating the biliary maze: a retrospective cohort study of three-dimensional navigation technology improving outcomes in congenital biliary dilatation resection
Background: Three-dimensional navigation technology (TDNT) has been previously utilized in hepatic and pancreatic surgical procedures, but its application and efficacy in the resection of congenital biliary dilatation (CBD) have not been reported. This study aims to investigate and evaluate the application value of TDNT in the resection of CBD and its short-term outcomes. Methods: Patients who underwent CBD resection surgery from January 2010 to March 2023 were included in this study. Patients were divided into the two groups based on whether TDNT was applied during the operation. After normality assessment via the Shapiro-Wilk test, continuous and categorical variables were compared using the Mann-Whitney U test and the Chi-square/Fisher’s exact test, respectively; univariate and multivariable logistic regression were used for risk factor analysis. Results: No significant baseline differences were observed between three-dimensional navigation (TDN) and non-TDN groups (P>0.05). On postoperative day 5 (POD5), TDN group had higher hemoglobin (P=0.008) and shorter drainage (P=0.013). TDN group had lower pancreatic fistula (P=0.025) and bile leakage (P=0.003), but higher blood transfusion (P=0.009). Comprehensive complication index (CCI) proportion was higher (≥26.2) in non-TDN group (P=0.02). Multivariable regression confirmed that TDNT was associated with lower risks of bile leakage [odds ratio (OR) =9.29, P=0.002], pancreatic fistula (OR =10.16, P=0.029), and high CCI (OR =4.50, P=0.026). Conclusions: TDNT in the resection of CBD is a feasible method that is associated with lower rates of postoperative pancreatic fistula and bile leakage. These findings suggest potential benefits in enhancing surgical precision and safety, though prospective studies are needed to establish causality.
Ultra-high-field 7-Tesla MRI in neuro-oncology: a narrative review of current applications and future directions
Background and Objective: Ultra-high-field 7-Tesla (7-T) magnetic resonance imaging (MRI) has emerged as an advanced neuroimaging modality that offers substantially higher signal-to-noise ratio (SNR), improved contrast resolution, and enhanced metabolic characterization compared with conventional 1.5- and 3-T MRI. These technical advantages have generated increasing interest in neuro-oncology, where accurate tumor detection, characterization, treatment planning, and response assessment are critical. This review aims to summarize current clinical and translational applications of 7-T MRI in neuro-oncology, evaluate its advantages and limitations, and discuss future directions for its integration into clinical practice. Methods: A structured narrative review was conducted using PubMed, Scopus, and Web of Science databases. Relevant studies published up to November 2025 were identified using combinations of keywords related to 7-T MRI, UHF imaging, neuro-oncology, gliomas, pituitary adenomas, sellar, skull-base tumors, advanced MRI techniques, and metabolic imaging. English-language original studies, prospective cohorts, systematic reviews, and technically significant proof-of-concept investigations were prioritized. Reference lists of eligible studies were additionally screened to identify relevant publications. Key Content and Findings: The review discusses the technical foundations of 7-T MRI and its current applications in tumor diagnosis, surgical planning, radiotherapy planning, and therapy monitoring. Evidence indicates that 7-T MRI improves visualization of tumor microvasculature, infiltrative margins, susceptibility-related abnormalities, and treatment-induced microvascular injury. Advanced techniques, including susceptibility-weighted imaging (SWI), high-resolution diffusion imaging, magnetic resonance spectroscopy (MRS), sodium MRI, phosphorus spectroscopy, and chemical exchange saturation transfer (CEST) imaging, provide enhanced metabolic and molecular characterization of brain tumors. Promising applications have been reported for gliomas, pituitary adenomas, meningiomas, metastatic disease, and selected pediatric tumors. However, most available evidence demonstrates technical and diagnostic superiority, whereas robust evidence for improved clinical outcomes remains limited. Conclusions: 7-T MRI offers significant potential to advance precision neuro-oncology through superior structural, functional, and metabolic imaging. Nevertheless, widespread clinical adoption remains constrained by technical challenges, cost, limited availability, and lack of standardized protocols. Future multicenter prospective studies, protocol harmonization, and outcome-based validation are needed to establish the clinical impact of 7-T MRI and guide future research, clinical implementation, and healthcare policy development.
Clinical applications of 4D flow magnetic resonance imaging in hepatic vascular disorders: practical biomarkers, protocol design, and translational implementation
Hepatic vascular disorders include portal hypertension in cirrhosis, Budd-Chiari syndrome (BCS), hepatic vascular malformations, and vascular complications after liver transplantation. These diseases depend on blood-flow direction, flow redistribution, and pressure transmission, and not only on gross vessel shape. Doppler ultrasound, computed tomography angiography (CTA), and digital subtraction angiography (DSA) are widely used in current clinical practice, but each has distinct limits for full hemodynamic assessment, and repeated follow-up can be difficult to implement effectively in daily clinical work. Four-dimensional flow magnetic resonance imaging (4D flow MRI) is a time-resolved three-dimensional (3D) phase-contrast MRI method with three-directional velocity encoding (VENC). It provides volumetric flow and multidirectional velocity data across the cardiac cycle, and it allows flexible retrospective plane placement and comprehensive network-level analysis. It can accurately measure flow volume, peak and mean velocity, retrograde fraction, fractional flow change, and shear-related metrics. It also supports pressure-gradient estimation when combined with physics-based modeling in selected clinical settings. Hepatic imaging still faces multiple major constraints, including VENC tradeoffs, respiratory motion, limited small-vessel resolution, and the need for unified standardized acquisition and analysis. Workflow limits from segmentation and plane placement can be greatly reduced by modern automated analysis. This review systematically summarizes technical foundations, processing steps, disease-focused applications, and practical barriers for clinical translation.
Quantitative assessment of prostate cancer using an individualized apparent diffusion coefficient intensity scale: a proposal
In this retrospective study, the diagnostic performance of an individualized apparent diffusion coefficient (ADC) signal intensity (SI) scale for differentiating prostate cancer (PCa) from benign simplified Prostate Imaging Reporting and Data System (S-PI-RADS) 4 lesions detected on biparametric magnetic resonance imaging (bpMRI) was evaluated. A total of 102 patients with biopsy-confirmed PCa and 79 patients with benign lesions (inflammation, HGPIN, ASAP) presenting as S-PI-RADS 4 were included. An individualized ADC SI scale was generated for each patient using maximum and minimum ADC values from the bladder and femoral head, respectively. The midpoint (ADC_mean) and the percentage difference (PD) between ADC_mean and lesion intensity (ADC_lesion) were calculated. Differences and diagnostic performance were assessed using Mann-Whitney U tests and receiver operating characteristic (ROC) analysis. PCa lesions demonstrated significantly lower ADC values and higher PDs compared with benign lesions (P<0.001). ROC analysis of PD yielded an area under the curve (AUC) of 0.798. Using a PD cut-off of 14.67%, sensitivity and specificity for detecting PCa were 73.5% and 94.9%, respectively. Intra-reader repeatability was high [intraclass correlation coefficient (ICC) 0.92–0.94], and PD values remained robust across variations in ADC fitting. The individualized ADC SI scale provides a patient-specific quantitative measure that effectively distinguishes malignant from benign S-PI-RADS 4 lesions. This approach complements visual bpMRI assessment, demonstrates high specificity, and has the potential to reduce unnecessary biopsies. Further prospective studies are warranted to validate its clinical utility.
Ultrasound microflow imaging for the evaluation of inflammation associated with superficial vein thrombosis: a proof-of-concept case report
Venous thrombosis is associated with an inflammatory process contributing to thrombus initiation, propagation, venous wall remodeling, and fibrous sequelae. Conventional ultrasound is the gold standard for diagnosing venous thrombosis, but primarily focuses on luminal obstruction and vein compressibility, providing limited information about vascular wall inflammation and microvascular perfusion. This proof-of-concept case study examines the potential of microvascular flow imaging (MVFI), specifically Samsung MV-Flow®, to evaluate perithrombotic inflammation, quantified by measuring the vascularity index (VI). A 40-year-old male with Behçet’s disease and a history of thrombotic episodes presented with acute thigh pain. Conventional ultrasound confirmed an occlusive thrombus in a superficial tributary of the great saphenous vein. MV-Flow imaging with an LA4-18 MHz probe revealed pronounced perivascular hyperemia. Serial follow-ups at 1, 3, and 6 months under immunosuppressive and antithrombotic therapy demonstrated clinical improvement, thrombus regression, and a consistent decrease in the VI from 11.7% to 5.15%, mirroring normalization of C-reactive protein (CRP) levels. This case demonstrates the added diagnostic value of MVFI in detecting and quantifying perithrombotic microvascularization. Compared with conventional Doppler, MVFI offers improved sensitivity for low-velocity blood flow and enables objective monitoring of inflammatory activity via the VI. These findings suggest that MVFI may represent a useful adjunct to standard venous ultrasound for assessing inflammation associated with venous thrombosis and for monitoring disease evolution, supporting further investigation in larger clinical studies.
Photodynamic therapy combined with a radiation-emitting metallic stent for extrahepatic cholangiocarcinoma: a first-in-human case report
Background: Extrahepatic cholangiocarcinoma (eCCA) carries a dismal prognosis, and the majority of patients present with unresectable disease at diagnosis. Photodynamic therapy (PDT) and radiation-emitting metallic stents (REMS) have each demonstrated palliative benefit in malignant biliary obstruction; however, their sequential combination has never previously been reported in humans. We describe herein the first-in-human application of this combined locoregional approach. Case Description: A 76-year-old male presented with a 1-month history of progressive fatigue and was diagnosed with eCCA on the basis of contrast-enhanced computed tomography (CT) and biliary biopsy. In view of his poor performance status [Eastern Cooperative Oncology Group (ECOG) score of 2] and personal refusal of surgery, he underwent percutaneous transhepatic cholangiodrainage (PTCD), followed by two sequential sessions of PDT and subsequent implantation of an iodine-125 (125I) seed-loaded REMS. At 6-month follow-up, the patient’s ECOG performance status had improved to 0, liver function tests had been normalized, serum carbohydrate antigen 19-9 (CA19-9) had decreased from 57.90 to 13.6 U/mL (within the normal reference range), and imaging demonstrated tumor regression with maintained stent patency. Conclusions: This case provides the first proof-of-concept for PDT combined with REMS implantation as a unified locoregional strategy for unresectable eCCA. The favorable short-term outcomes support the scientific and clinical rationale for this combined approach and underscore the need for prospective, controlled studies to validate its safety, efficacy, and optimal technical parameters.
Prenatal sonographic diagnosis of fetal right aortic arch with isolated left innominate artery and bilateral ductus arteriosus: a case description
Hyperperfusion of stroke-like lesions and dilatation of the corresponding arteries are an epiphenomenon and not involved in their pathophysiology
Contrast-enhanced ultrasound: a potential method for identifying the cause of postpartum hemorrhage in emergency settings
Re-intervention for type III endoleak after endovascular aneurysm repair with an ALTO stent graft: a case description
Dual-tracer positron emission tomography-computed tomography with 18F-PSMA-7Q and 18F-FDG in a patient with synchronous prostate carcinoma and undifferentiated pleomorphic sarcoma: a case description
Stereotactic body radiotherapy using the CyberKnife system for cutaneous metastasis of hepatocellular carcinoma: a case description
Laparoscopic retroperitoneal paraganglioma resection: a case description