Journal of Magnetic Resonance Imaging最新文献

{"title":"Altered Brain Functional Networks in Patients With Breast Cancer After Different Cycles of Neoadjuvant Chemotherapy.","authors":"Jing Yang, Yongchun Deng, Daihong Liu, Yixin Hu, Yu Tang, Xiaoyu Zhou, Yong Tan, Jing Zhang, Jiang Liu, Chengfang Wang, Xiaohua Zeng, Jiuquan Zhang","doi":"10.1002/jmri.29772","DOIUrl":"10.1002/jmri.29772","url":null,"abstract":"<p><strong>Background: </strong>Cancer-related cognitive impairment (CRCI) impacts breast cancer (BC) patients' quality of life after chemotherapy. While recent studies have explored its neural correlates, single time-point designs cannot capture how these changes evolve over time.</p><p><strong>Purpose: </strong>To investigate changes in the brain connectome of BC patients at several time points during neoadjuvant chemotherapy (NAC).</p><p><strong>Study type: </strong>Longitudinal.</p><p><strong>Subjects: </strong>55 participants with BC underwent clinical assessments and fMRI at baseline (TP1), the first cycle of NAC (TP2, 30 days later), and the end (TP3, 140 days later). Two matched female healthy control (HCs, n = 20 and n = 18) groups received the same assessments. FIELD STRENGTH/SEQUENCE: rs-fMRI (gradient-echo EPI) and 3D T1-weighted magnetization-prepared rapid gradient echo sequence at 3.0 T.</p><p><strong>Assessment: </strong>Brain functional networks were analyzed using graph theory approaches. We analyzed changes in brain connectome metrics and explored the relationship between these changes and clinical scales (including emotion and cognitive test). Patients were divided into subgroups according to clinical classification, chemotherapy regimen, and menopausal status. Longitudinal analysis was performed at three time points for each subgroup.</p><p><strong>Statistical tests: </strong>An independent sample t-test for patient-HC comparison at TP1. Analysis of variance and paired t-test for longitudinal changes. Regression analysis for relations between network measurements changes and clinical symptom scores changes. Significance was defined as p < 0.05.</p><p><strong>Results: </strong>Post-NAC, BC patients showed increased global efficiency (TP2-TP1 = 0.087, TP3-TP1 = 0.078), decreased characteristic path length (TP2-TP1 = -0.413, TP3-TP1 = -0.312), and altered nodal centralities mainly in the frontal-limbic system and cerebellar cortex. These abnormalities expanded with chemotherapy progression significantly (TP2 vs. TP3). Topological parameters changes were also correlated with clinical scales changes significantly. No differences were found within or between HC groups (p = 0.490-0.989) or BC subgroups (p = 0.053-0.988) at TP1.</p><p><strong>Data conclusions: </strong>NAC affects the brain functional connectome of BC patients at TP2, and these changes persist and further intensify at TP3.</p><p><strong>Level of evidence: 2: </strong></p><p><strong>Technical efficacy: </strong>Stage 5.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"902-914"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811486","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 \"Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset\".","authors":"Wentao Yang, Tianyi Xia","doi":"10.1002/jmri.29830","DOIUrl":"10.1002/jmri.29830","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"825-826"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159664","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":"Visualization and Assessment of the Venous Plexus of Rektorzik on Ultrahigh Resolution Vessel Wall MRI.","authors":"Siddhant Dogra, Eytan Raz, Seena Dehkharghani","doi":"10.1002/jmri.70016","DOIUrl":"10.1002/jmri.70016","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"930-933"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144475648","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":"Detection of Microscopic Glioblastoma Infiltration in Peritumoral Edema Using Interactive Deep Learning With DTI Biomarkers: Testing via Stereotactic Biopsy.","authors":"Jiaqi Tu, Chuyun Shen, Jianpeng Liu, Bin Hu, Zecheng Chen, Yijiu Yan, Chao Li, Ji Xiong, Alex Michel Daoud, Xiangfeng Wang, Yuxin Li, Fengping Zhu","doi":"10.1002/jmri.70058","DOIUrl":"https://doi.org/10.1002/jmri.70058","url":null,"abstract":"<p><strong>Background: </strong>Microscopic tumor cell infiltration beyond contrast-enhancing regions influences glioblastoma prognosis but remains undetectable using conventional MRI.</p><p><strong>Purpose: </strong>To develop and evaluate the glioblastoma infiltrating area interactive detection framework (GIAIDF), an interactive deep-learning framework that integrates diffusion tensor imaging (DTI) biomarkers for identifying microscopic infiltration within peritumoral edema.</p><p><strong>Study type: </strong>Retrospective.</p><p><strong>Population: </strong>A total of 73 training patients (51.13 ± 13.87 years; 47 M/26F) and 25 internal validation patients (52.82 ± 10.76 years; 14 M/11F) from Center 1; 25 external validation patients (47.29 ± 11.39 years; 16 M/9F) from Center 2; 13 prospective biopsy patients (45.62 ± 9.28 years; 8 M/5F) from Center 1.</p><p><strong>Field strength/sequences: </strong>3.0 T MRI including three-dimensional contrast-enhanced T1-weighted BRAVO sequence (repetition time = 7.8 ms, echo time = 3.0 ms, inversion time = 450 ms, slice thickness = 1 mm), three-dimensional T2-weighted fluid-attenuated inversion recovery (repetition time = 7000 ms, echo time = 120 ms, inversion time = 2000 ms, slice thickness = 1 mm), and diffusion tensor imaging (repetition time = 8500 ms, echo time = 63 ms, slice thickness = 2 mm).</p><p><strong>Assessment: </strong>Histopathology of 25 stereotactic biopsy specimens served as the reference standard. Primary metrics included AUC, accuracy, sensitivity, and specificity. GIAIDF heatmaps were co-registered to biopsy trajectories using Ratio-FAcpcic (0.16-0.22) as interactive priors.</p><p><strong>Statistical tests: </strong>ROC analysis (DeLong's method) for AUC; recall, precision, and F1 score for prediction validation.</p><p><strong>Results: </strong>GIAIDF demonstrated recall = 0.800 ± 0.060, precision = 0.915 ± 0.057, F1 = 0.852 ± 0.044 in internal validation (n = 25) and recall = 0.778 ± 0.053, precision = 0.890 ± 0.051, F1 = 0.829 ± 0.040 in external validation (n = 25). Among 13 patients undergoing stereotactic biopsy, 25 peri-ED specimens were analyzed: 18 without tumor cell infiltration and seven with infiltration, achieving AUC = 0.929 (95% CI: 0.804-1.000), sensitivity = 0.714, specificity = 0.944, and accuracy = 0.880. Infiltrated sites showed significantly higher risk scores (0.549 ± 0.194 vs. 0.205 ± 0.175 in non-infiltrated sites, p < 0.001).</p><p><strong>Data conclusion: </strong>This study has provided a potential tool, GIAIDF, to identify regions of GBM infiltration within areas of peri-ED based on preoperative MR images.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144957276","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 \"Fetal MRI: Radiofrequency Safety Assessment at 3 Tesla\".","authors":"Jeffrey W Hand","doi":"10.1002/jmri.29800","DOIUrl":"10.1002/jmri.29800","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"854-855"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144007576","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":"Federated Learning for Renal Tumor Segmentation and Classification on Multi-Center MRI Dataset.","authors":"Dat-Thanh Nguyen, Maliha Imami, Lin-Mei Zhao, Jing Wu, Ali Borhani, Alireza Mohseni, Mihir Khunte, Zhusi Zhong, Victoria Shi, Sophie Yao, Yuli Wang, Nicolas Loizou, Alvin C Silva, Paul J Zhang, Zishu Zhang, Zhicheng Jiao, Ihab Kamel, Wei-Hua Liao, Harrison Bai","doi":"10.1002/jmri.29819","DOIUrl":"10.1002/jmri.29819","url":null,"abstract":"<p><strong>Background: </strong>Deep learning (DL) models for accurate renal tumor characterization may benefit from multi-center datasets for improved generalizability; however, data-sharing constraints necessitate privacy-preserving solutions like federated learning (FL).</p><p><strong>Purpose: </strong>To assess the performance and reliability of FL for renal tumor segmentation and classification in multi-institutional MRI datasets.</p><p><strong>Study type: </strong>Retrospective multi-center study.</p><p><strong>Population: </strong>A total of 987 patients (403 female) from six hospitals were included for analysis. 73% (723/987) had malignant renal tumors, primarily clear cell carcinoma (n = 509). Patients were split into training (n = 785), validation (n = 104), and test (n = 99) sets, stratified across three simulated institutions.</p><p><strong>Field strength/sequence: </strong>MRI was performed at 1.5 T and 3 T using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequences.</p><p><strong>Assessment: </strong>FL and non-FL approaches used nnU-Net for tumor segmentation and ResNet for its classification. FL-trained models across three simulated institutional clients with central weight aggregation, while the non-FL approach used centralized training on the full dataset.</p><p><strong>Statistical tests: </strong>Segmentation was evaluated using Dice coefficients, and classification between malignant and benign lesions was assessed using accuracy, sensitivity, specificity, and area under the curves (AUCs). FL and non-FL performance was compared using the Wilcoxon test for segmentation Dice and Delong's test for AUC (p < 0.05).</p><p><strong>Results: </strong>No significant difference was observed between FL and non-FL models in segmentation (Dice: 0.43 vs. 0.45, p = 0.202) or classification (AUC: 0.69 vs. 0.64, p = 0.959) on the test set. For classification, no significant difference was observed between the models in accuracy (p = 0.912), sensitivity (p = 0.862), or specificity (p = 0.847) on the test set.</p><p><strong>Data conclusion: </strong>FL demonstrated comparable performance to non-FL approaches in renal tumor segmentation and classification, supporting its potential as a privacy-preserving alternative for multi-institutional DL models.</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":"814-824"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093957","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":"Current State of Evidence for Use of MRI in LI-RADS.","authors":"Ameya Madhav Kulkarni, Danielle Kruse, Kelly Harper, Eric Lam, Hoda Osman, Danyaal H Ansari, Umaseh Sivanesan, Mustafa R Bashir, Andreu F Costa, Matthew McInnes, Christian B van der Pol","doi":"10.1002/jmri.29748","DOIUrl":"10.1002/jmri.29748","url":null,"abstract":"<p><p>The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this review, the pathogenesis of hepatocellular carcinoma and the use of MRI in LI-RADS is discussed, including specifically the LI-RADS diagnostic algorithm, its components, and its reproducibility with reference to the latest supporting evidence. The LI-RADS treatment response algorithms are reviewed, including the more recent radiation treatment response algorithm. The application of artificial intelligence, points of controversy, LI-RADS relative to other liver imaging systems, and possible future directions are explored. After reading this article, the reader will have an understanding of the foundation and application of LI-RADS as well as possible future directions.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"640-653"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143468313","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":"Feasibility of MRI-Guided Transperineal Implantation of Microdevices for Drug Delivery and Response Assessment in Prostate Cancer.","authors":"Quinn Rainer, Kemal Tuncali, Wooseok Ahn, Fanni Viktoria Santa, Michelle Hirsch, Sharath Bhagavatula, Fumitaro Masaki, Clarissa Therese Young, Courtney Marlin, Samantha Martin, Destiny U Matthew, Filipe De Carvalho, Christine A Dominas, Benjamin V Stone, Clare Tempany, Oliver Jonas, Adam Stuart Kibel, Nobuhiko Hata","doi":"10.1002/jmri.29784","DOIUrl":"10.1002/jmri.29784","url":null,"abstract":"<p><strong>Background: </strong>Prostate cancer (PCa) treatment often involves systemic therapies with varying mechanisms of action, affecting individuals differently. Implantable microdevices (IMDs) are designed to test multiple drugs within a patient's tumor, but the feasibility of MRI-guided placement in PCa has not been evaluated.</p><p><strong>Purpose: </strong>To provide proof of concept for placing IMDs into lesions with MRI guidance to predict patient-specific responses to therapies.</p><p><strong>Study type: </strong>Prospective.</p><p><strong>Population: </strong>Fifteen participants undergoing prostatectomy for PCa.</p><p><strong>Field strength/sequence: </strong>3T MRI With T2-weighted (T2W).</p><p><strong>Assessment: </strong>In-bore MRI-targeted placement of IMDs was performed. Intra-procedural MRI scans were reviewed by a radiologist, using needle artifacts on T2W images to guide IMD placement. A genitourinary pathologist performed Gleason scoring around the IMDs. Drug response analysis included Enzalutamide + Nivolumab, Enzalutamide + Docetaxel, and single-agent Enzalutamide.</p><p><strong>Statistical tests: </strong>Mann-Whitney U test for continuous variables, p < 0.05 for significance.</p><p><strong>Results: </strong>Of 53 IMDs implanted into suspicious lesions in 14 participants, 48 (90%) were successfully placed within the lesions. The average distance from the needle tip to the tumor was 8.32 ± 4.02 mm. Larger lesion size (p = 0.009) and lower prostate imaging-reporting and data system score (p = 0.031) were significantly associated with successful IMD placement. Of the 53 IMDs, 49 (92.4%) were retrieved for histopathology and drug response analysis. In four participants, Gleason scores around the device were lower than preplacement biopsy in two and equal in two. Additionally, drug analysis in one patient demonstrated the feasibility of drug response analysis, revealing differences in apoptotic index, lymphocyte infiltration, dysplastic cell composition, and cellular profiles for each treatment. No complications or adverse events occurred.</p><p><strong>Conclusion: </strong>IMDs can be effectively and safely placed in prostate lesions using MRI guidance, with feasible histological and drug response analyses.</p><p><strong>Evidence level: </strong>2. Technical Efficacy: Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"707-718"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996284","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":"Multi-Center, Multi-Vendor Validation of Simultaneous MRI-Based Proton Density Fat Fraction and R2* Mapping Using a Combined Proton Density Fat Fraction-R2* Phantom.","authors":"Jitka Starekova, David Rutkowski, Won C Bae, Hung Do, Ananth J Madhuranthakam, Vadim Malis, Sheng Qing Lin, Suraj Serai, Takeshi Yokoo, Scott B Reeder, Jean H Brittain, Diego Hernando","doi":"10.1002/jmri.29775","DOIUrl":"10.1002/jmri.29775","url":null,"abstract":"<p><strong>Background: </strong>Fat and iron deposition confound measurements of R2* and proton density fat fraction (PDFF), respectively, yet their combined impact on reproducibility is poorly understood.</p><p><strong>Purpose: </strong>To evaluate the multi-center, multi-vendor reproducibility of PDFF and R2* quantification using a PDFF-R2* phantom.</p><p><strong>Study type: </strong>Prospective multi-center, phantom study.</p><p><strong>Phantom: </strong>Commercial PDFF-R2* phantom with simultaneously controlled combination of PDFF (0%-30%) and R2* (50-600 s^-1) values.</p><p><strong>Field strength/sequence: </strong>1.5-T and 3-T, three-dimensional (3D) multi-echo, spoiled-gradient-echo sequences, in four different centers, each with a different vendor.</p><p><strong>Assessment: </strong>Two acquisition protocols were used, optimized for moderate R2* (Protocol 1) and high R2* (Protocol 2), respectively. The phantom was imaged multiple times at one of the centers to assess its stability.</p><p><strong>Statistical tests: </strong>Intraclass correlation coefficient (ICC), linear regression analysis, reproducibility coefficient (RDC) and repeatability coefficient (RC).</p><p><strong>Results: </strong>Excellent agreement was observed for PDFF measurements between centers, vendors, field strengths, and protocols (ICC = 0.97). Stratified by protocol, excellent agreement was observed, with ICC = 0.96 (RDC = 6.2%) for Protocol 1 and ICC = 0.99 (RDC = 3.8%) for Protocol 2. Increased variability in PDFF measurements was observed with increasing PDFF and especially with higher R2*. Excellent agreement was observed for R2* between centers, vendors, field strengths, and protocols (ICC = 0.99). Stratified by protocol, strong agreement was observed, with ICC = 0.988 (RDC = 66.7 s^-1) for Protocol 1 and ICC = 0.99 (RDC = 57.7 s^-1) for Protocol 2. Higher variability in R2* measurements was observed in vials with higher PDFF or R2*. Stability tests demonstrated an ICC = 1.0 for PDFF and R2*, and RC of 0.4% for PDFF and 12 s^-1 for R2*.</p><p><strong>Data conclusion: </strong>Excellent PDFF and R2* reproducibility was observed across centers, vendors, field strengths, and acquisition protocols. Reproducibility decreased slightly with increasing PDFF and R2*, especially for PDFF measurements in vials with high R2*.</p><p><strong>Evidence level: </strong>N/A.</p><p><strong>Technical efficacy: </strong>Stage 1.</p>","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"800-811"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011189","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":"Broad Consent in Healthcare Research: What Is Efficient, What Is Right?","authors":"Jitka Starekova, Mark E Schweitzer","doi":"10.1002/jmri.70000","DOIUrl":"10.1002/jmri.70000","url":null,"abstract":"","PeriodicalId":16140,"journal":{"name":"Journal of Magnetic Resonance Imaging","volume":" ","pages":"856-857"},"PeriodicalIF":3.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144248271","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}