Journal of Magnetic Resonance Imaging最新文献

{"title":"Unchanged Early Diffusion Tensor Imaging Along Perivascular Space Index After Amyloid-Targeting Disease-Modifying Therapy in Alzheimer's Disease: A Preliminary Study.","authors":"Tatsushi Oura, Hiroyuki Tatekawa, Akitoshi Takeda, Ayako Omori, Natsuko Atsukawa, Shu Matsushita, Daisuke Horiuchi, Hirotaka Takita, Taro Shimono, Daiju Ueda, Yoshiaki Itoh, Yukio Miki","doi":"10.1002/jmri.70118","DOIUrl":"10.1002/jmri.70118","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145023435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Sex-Specific Cardiac Magnetic Resonance Phenotypes in Danon Disease: A Retrospective Cohort Study\".","authors":"Jie He, Heng Ge","doi":"10.1002/jmri.70117","DOIUrl":"https://doi.org/10.1002/jmri.70117","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning-Based Fully Automated Cardiac MRI Segmentation Approach for Tetralogy of Fallot Patients.","authors":"Wen-Yen Chai, Gigin Lin, Chao-Jan Wang, Hsin-Ju Chiang, Shu-Hang Ng, Yi-Shan Kuo, Yu-Chun Lin","doi":"10.1002/jmri.70113","DOIUrl":"https://doi.org/10.1002/jmri.70113","url":null,"abstract":"<p><strong>Background: </strong>Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.</p><p><strong>Purpose: </strong>To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>427 patients with diverse cardiac conditions (305 non-ToF, 122 ToF), with 395 for training/validation, 32 ToF for internal testing, and 12 external ToF for generalizability assessment.</p><p><strong>Field strength/sequence: </strong>Steady-state free precession cine sequence at 1.5/3 T.</p><p><strong>Assessment: </strong>U-Net, Deep U-Net, and MultiResUNet were trained under three regimes (non-ToF, ToF-only, mixed), using manual segmentations from one radiologist and one researcher (20 and 10 years of experience, respectively) as reference, with consensus for discrepancies. Performance for LV, RV, and LV myocardium was evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and F1-score, alongside regional (basal, middle, apical) and global ventricular function comparisons to manual results.</p><p><strong>Statistical tests: </strong>Friedman tests were applied for architecture and regime comparisons, paired Wilcoxon tests for ED-ES differences, and Pearson's r for assessing agreement in global function.</p><p><strong>Results: </strong>MultiResUNet model trained on a mixed dataset (TOF and non-TOF cases) achieved the best segmentation performance, with DSCs of 96.1% for LV and 93.5% for RV. In the internal test set, DSCs for LV, RV, and LV myocardium were 97.3%, 94.7%, and 90.7% at end-diastole, and 93.6%, 92.1%, and 87.8% at end-systole, with ventricular measurement correlations ranging from 0.84 to 0.99. Regional analysis showed LV DSCs of 96.3% (basal), 96.4% (middle), and 94.1% (apical), and RV DSCs of 92.8%, 94.2%, and 89.6%. External validation (n = 12) showed correlations ranging from 0.81 to 0.98.</p><p><strong>Conclusion: </strong>The MultiResUNet model enabled accurate automated cardiac MRI segmentation in ToF with the potential to streamline workflows and improve disease monitoring.</p><p><strong>Evidence level: </strong>3.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of MRI in Debunking the Fallacy of \"Mild\" Traumatic Brain Injury.","authors":"Xingye Chen, David Wright, Sohae Chung, Yvonne Lui","doi":"10.1002/jmri.70083","DOIUrl":"https://doi.org/10.1002/jmri.70083","url":null,"abstract":"<p><p>Mild traumatic brain injury (mTBI) is a prevalent yet often overlooked public health concern due to the absence of detectable abnormalities on CT or conventional MRI scans. Approximately 18.3%-31.3% of mTBI patients experience persistent symptoms 3-6 months post-injury, despite normal imaging results, making diagnosis and treatment challenging. In recent years, advanced neuroimaging modalities have emerged with the potential to reveal subtle physiological and structural brain changes that are invisible to traditional imaging. Diffusion MRI (dMRI), for instance, is particularly valuable for detecting white matter injury; perfusion MRI assesses alterations in cerebral blood flow; sodium MRI (²³Na MRI) provides insights into ionic homeostasis; and functional MRI (fMRI) detects disruptions in functional brain network connectivity. In this review, we first explore the underlying mechanisms of mTBI and then summarize current evidence supporting the use of advanced MRI techniques to detect injury signatures associated with these mechanisms. Finally, we highlight populations at heightened risk for repeated injuries-underscoring the urgent need for more sensitive diagnostic tools that can identify injury early, guide return-to-activity decisions, and prevent cumulative brain damage. EVIDENCE LEVEL: N/A. TECHNICAL EFFICACY: Stage 3.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative Assessment of Extraprostatic Extension in Prostate Cancer Using an Interpretable Tabular Prior-Data Fitted Network-Based Radiomics Model From MRI.","authors":"Bai-Chuan Liu, Xiao-Hui Ding, Hong-Hao Xu, Xu Bai, Xiao-Jing Zhang, Meng-Qiu Cui, Ai-Tao Guo, Xue-Tao Mu, Li-Zhi Xie, Huan-Huan Kang, Shao-Peng Zhou, Jian Zhao, Bao-Jun Wang, Hai-Yi Wang","doi":"10.1002/jmri.70111","DOIUrl":"https://doi.org/10.1002/jmri.70111","url":null,"abstract":"<p><strong>Background: </strong>MRI assessment for extraprostatic extension (EPE) of prostate cancer (PCa) is challenging due to limited accuracy and interobserver agreement.</p><p><strong>Purpose: </strong>To develop an interpretable Tabular Prior-data Fitted Network (TabPFN)-based radiomics model to evaluate EPE using MRI and explore its integration with radiologists' assessments.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Five hundred and thirteen consecutive patients who underwent radical prostatectomy. Four hundred and eleven patients from center 1 (mean age 67 ± 7 years) formed training (287 patients) and internal test (124 patients) sets, and 102 patients from center 2 (mean age 66 ± 6 years) were assigned as an external test set.</p><p><strong>Field strength/sequence: </strong>Three Tesla, fast spin echo T2-weighted imaging (T2WI) and diffusion-weighted imaging using single-shot echo planar imaging.</p><p><strong>Assessment: </strong>Radiomics features were extracted from T2WI and apparent diffusion coefficient maps, and the TabRadiomics model was developed using TabPFN. Three machine learning models served as baseline comparisons: support vector machine, random forest, and categorical boosting. Two radiologists (with > 1500 and > 500 prostate MRI interpretations, respectively) independently evaluated EPE grade on MRI. Artificial intelligence (AI)-modified EPE grading algorithms incorporating the TabRadiomics model with radiologists' interpretations of curvilinear contact length and frank EPE were simulated.</p><p><strong>Statistical tests: </strong>Receiver operating characteristic curve (AUC), Delong test, and McNemar test. p < 0.05 was considered significant.</p><p><strong>Results: </strong>The TabRadiomics model performed comparably to machine learning models in both internal and external tests, with AUCs of 0.806 (95% CI, 0.727-0.884) and 0.842 (95% CI, 0.770-0.912), respectively. AI-modified algorithms showed significantly higher accuracies compared with the less experienced reader in internal testing, with up to 34.7% of interpretations requiring no radiologist input. However, no difference was observed in both readers in the external test set.</p><p><strong>Data conclusions: </strong>The TabRadiomics model demonstrated high performance in EPE assessment and may improve clinical assessment in PCa.</p><p><strong>Evidence level: </strong>4.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145000750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MR Passive Elastography: From Seismic Noise Tomography to Brain Tumors Characterization.","authors":"Gwenaël Pagé, Khalil Rachid, Benoît Larrat, Bruno Giammarinaro, Johannes Aichele, Guillame Lacoin, Ilyess Zemmoura, Franck Mauconduit, Angéline Nemeth, Laurent Barrantin, Alexandre Vignaud, Jean-Pierre Remenieras, Stefan Catheline, Jean-Luc Gennisson","doi":"10.1002/jmri.70072","DOIUrl":"https://doi.org/10.1002/jmri.70072","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Lung Ventilation Using Four-Dimensional Dynamic Ultrashort Echo Time MRI for Differentiating Preserved Ratio Impaired Spirometry From Non-Chronic Obstructive Pulmonary Disease Subjects.","authors":"Ziwei Zhang, Jie Li, Zekang Ding, Yi Xia, Song Jiang, Meiling Xu, Zijun Wu, Huajun She, Shiyuan Liu, Yiping P Du, Li Fan","doi":"10.1002/jmri.70064","DOIUrl":"https://doi.org/10.1002/jmri.70064","url":null,"abstract":"<p><strong>Background: </strong>Radiation-free four-dimensional (4D) dynamic ultrashort echo time MRI (UTE MRI) enables quantification of ventilation defects in chronic obstructive pulmonary disease (COPD) and preserved ratio impaired spirometry (PRISm) populations.</p><p><strong>Purpose: </strong>To quantify pulmonary ventilation using 4D UTE MRI in PRISm and COPD populations, and determine its ability to distinguish PRISm from non-COPD subjects.</p><p><strong>Study type: </strong>Prospective, cross-sectional.</p><p><strong>Subjects: </strong>90 subjects (25 COPD, 31 PRISm, and 34 with normal spirometry).</p><p><strong>Field strength/sequence: </strong>Four-dimensional dynamic UTE MRI at 3 T.</p><p><strong>Assessment: </strong>All subjects underwent pulmonary function tests, paired inspiratory and expiratory chest CT, and 4D UTE MRI. Parametric response maps (PRM) were generated and voxels were classified as normal, emphysema, or functional small airway disease (fSAD). The percentage of low attenuation area (LAA%), PRM^Emphy% and PRM^fSAD% were determined. Ventilation defect maps were generated to derive ventilation defect percentage (VDP).</p><p><strong>Statistical tests: </strong>Kruskal-Wallis test with Dunn-Bonferroni post hoc correction was used. Agreement was assessed using Bland-Altman plots (95% limits of agreement [LoA]). Correlations were analyzed by Spearman's rank correlation coefficient (r). Significant predictors were identified through binary logistic regression. The significance threshold was set at p < 0.05.</p><p><strong>Results: </strong>VDP in smokers with normal spirometry showed no significant differences compared to VDP in GOLD 1-2 COPD (p = 0.98) or PRISm (p = 1). VDP mildly correlated with PRM^Emphy% (r = 0.44), LAA% (r = 0.38), and PRM^fSAD% (r = 0.25). VDP (odds ratio = 1.27, 95% confidence interval: 1.04-1.65) was a significant predictor for PRISm. The inter-modality agreement demonstrated minimal systematic bias with mean differences of 0 (95% LoA: -2.41 to 2.41) for observer 1 and 0 (95% LoA: -2.45 to 2.45) for observer 2.</p><p><strong>Data conclusion: </strong>Free-breathing 4D UTE MRI objectively quantifies total ventilation defects and may serve as a potential imaging tool for identifying early abnormalities in subjects at risk of PRISm.</p><p><strong>Evidence level: </strong>1.</p><p><strong>Technical efficacy: </strong>Stage 2.</p><p><strong>Trial registration: </strong>ClinicalTrial.gov identifier: NCT02100800.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fetal MRI: Radiofrequency Safety Assessment at 3 Tesla.","authors":"Filiz Yetisir, Esra Abaci Turk, Henry A Feldman, Borjan Gagoski, Ryne A Didier, Carol Barnewolt, Judy A Estroff, Lawrence L Wald, Elfar Adalsteinsson, P Ellen Grant","doi":"10.1002/jmri.29797","DOIUrl":"10.1002/jmri.29797","url":null,"abstract":"<p><strong>Background: </strong>3-T MRI can improve image quality of fetal imaging compared to 1.5-T MRI. However, concerns exist regarding increased local tissue heating at 3-T.</p><p><strong>Purpose: </strong>To assess fetal MRI radiofrequency (RF) safety at 3-T by comparing simulated tissue heating to 1.5-T (using constant RF exposure) and by simulating tissue heating at 3-T using RF exposures from clinical fetal examinations.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>Seven voxelized anatomical pregnant body models (gestational age [GA] 30 ± 3 weeks [mean ± standard deviation], maternal body mass index [BMI] 27.8 ± 8.5 kg/m²) were used. Maternal whole-body average specific absorption rate (wbSAR) logs were collected from 85 clinical examinations at 3-T (GA 25 ± 6 weeks, BMI 30.3 ± 6.8 kg/m²).</p><p><strong>Field strength/sequence: </strong>3-T, 1.5-T, HASTE, VIBE, TRUFISP, EPI, DTI.</p><p><strong>Assessment: </strong>Simulated maternal and fetal peak and average SAR, temperature, and peak thermal dose were compared at 3-T and 1.5-T for 60 min 2 W/kg wbSAR using 7 body models and a 16-rung band-pass RF coil. Temperature and thermal dose were simulated in one body model using clinical wbSAR exposures at 3-T.</p><p><strong>Statistical tests: </strong>Factorial analysis of variance was performed using 28 maternal and fetal temperature measurements from 7 body models to detect a difference between 3-T and 1.5-T. p < 0.05 was considered statistically significant.</p><p><strong>Results: </strong>For constant RF exposure, we found no difference between 3-T and 1.5-T in peak maternal (1.5-T:40.38 ± 0.21°C; 3-T:40.40 ± 0.20°C; p = 0.85), peak fetal (1.5-T:39.21 ± 0.17°C; 3-T:39.09 ± 0.16°C; p = 0.19), and average maternal (1.5-T:37.32 ± 0.05°C; 3-T:37.33 ± 0.04°C; p = 0.68) temperature. We observed significantly higher average fetal temperatures at 1.5-T (1.5-T:37.75 ± 0.06°C; 3-T:37.70 ± 0.05°C). For 3-T clinical RF exposures, simulated peak temperatures exceeded the recommended limits. However, the thermal dose was below the recommended limit.</p><p><strong>Data conclusion: </strong>For the same RF coil geometry, local heating was similar at 3-T and 1.5-T for constant RF exposure. Although realistic 3-T RF exposures could cause peak temperatures above the recommended limits, thermal dose was below the recommended limit.</p><p><strong>Evidence level: </strong>1.</p><p><strong>Technical efficacy: </strong>Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"844-853"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144020768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning on Abbreviated Prostate MRI.","authors":"Patricia M Johnson, Tarun Dutt, Luke A Ginocchio, Amanpreet Singh Saimbhi, Lavanya Umapathy, Kai Tobias Block, Daniel K Sodickson, Sumit Chopra, Angela Tong, Hersh Chandarana","doi":"10.1002/jmri.29798","DOIUrl":"10.1002/jmri.29798","url":null,"abstract":"<p><strong>Background: </strong>MRI plays a critical role in prostate cancer (PCa) detection and management. Bi-parametric MRI (bpMRI) offers a faster, contrast-free alternative to multi-parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization.</p><p><strong>Purpose: </strong>To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial.</p><p><strong>Study type: </strong>Retrospective and prospective.</p><p><strong>Population: </strong>The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65 ± 8) with ground-truth verification from biopsy, prostatectomy, or long-term follow-up, alongside a prospective cohort of 142 treatment-naïve patients (mean age 65 ± 9) undergoing bpMRI, was evaluated.</p><p><strong>Field strength/sequence: </strong>3 T, Turbo-spin echo T2-weighted imaging (T2WI) and single shot EPI diffusion-weighted imaging (DWI).</p><p><strong>Assessment: </strong>The DL model, based on a 3D ResNet-50 architecture, classified csPCa using PI-RADS ≥ 3 and Gleason ≥ 7 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long-term follow-up. Real-time inference was tested on an automated MRI workflow, providing classification results directly at the scanner.</p><p><strong>Statistical tests: </strong>AUROC with 95% confidence intervals (CI) was used to evaluate model performance.</p><p><strong>Results: </strong>In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77-0.89) for PI-RADS ≥ 3 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80-0.91) for Gleason ≥ 7 classification, with 93% sensitivity and 62% specificity. Real-time implementation demonstrated a processing latency of 14-16 s for protocol recommendations.</p><p><strong>Data conclusion: </strong>The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows.</p><p><strong>Evidence level: </strong>1.</p><p><strong>Technical efficacy: </strong>Stage 2.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"858-866"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for \"Habitat Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Assessing Axillary Lymph Node Burden in Clinical T1-T2 Stage Breast Cancer: A Multicenter and Interpretable Study\".","authors":"Marialena Tsarouchi, Alexandros Vamvakas","doi":"10.1002/jmri.29809","DOIUrl":"10.1002/jmri.29809","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"765-766"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}