{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:47:16Z","timestamp":1786978036309,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82171923"],"award-info":[{"award-number":["82171923"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82371952"],"award-info":[{"award-number":["82371952"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20757"],"award-info":[{"award-number":["U24A20757"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,5]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Accurate preoperative assessment of lymphovascular invasion (LVI) remains challenging due to the high heterogeneity of gastric cancer (GC).<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To evaluate the feasibility of a subregion\u2010based radiomics model using multiparametric MRI (mpMRI) for preoperative evaluation of LVI and to further assess its prognostic value.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Subjects<\/jats:title>\n A total of 878 GC patients from four centers: 313 training, 133 internal test, and 432 external validation cases.<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 1.5\u2009T and 3\u2009T\/mpMRI including T2\u2010weighted imaging (FSE\/TSE), diffusion\u2010weighted imaging (SS\u2010EPI), and contrast\u2010enhanced T1\u2010weighted imaging (FFE\/VIBE).<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n The fuzzy c\u2010means clustering was applied to subregion generation after manual segmentation. The subregional radiomics model was established using LVI\u2010related features from a four\u2010step extracted pipeline, with logistic regression, random forest, and support vector machine algorithms. The corresponding intra\u2010tumoral subregion (ITS) index for each patient was obtained from the optimal subregional model. Subsequently, a combined model incorporating the ITS index and independent clinical characteristics was developed. Performance was further validated in test and validation cohorts. Additionally, the prognostic utility for overall survival (OS) and disease\u2010free survival (DFS) was assessed in the follow\u2010up cohort.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Model area under the receiver operating characteristic curves (AUCs) was compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Kaplan\u2013Meier survival analyses were conducted for prognostic evaluation.\n p<\/jats:italic>\n \u2009<\u20090.05 was considered statistically significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Pathological LVI\u2010positive was detected in 448 (51.0%) patients. The combined model demonstrated satisfactory discrimination of LVI, achieving AUCs of 0.814 (training), 0.769 (test), and 0.758\u20130.783 (validation), outperforming the optimal subregional model with positive NRI and IDI across all cohorts. Furthermore, the ITS index maintained a significant association with OS (HR 33.50) and DFS (HR 30.00).<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n The combined model, which integrated the ITS index derived from subregional radiomics with clinical factors, demonstrated robust performance in evaluating both LVI and survival outcomes in GC patients.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 3.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70236","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T17:00:18Z","timestamp":1768064418000},"page":"1466-1479","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Subregional Radiomics Analysis on Multiparametric\n MRI<\/scp>\n for Evaluating Lymphovascular Invasion and Survival in Gastric Cancer: A Multicenter Study"],"prefix":"10.1002","volume":"63","author":[{"given":"Ruirui","family":"Song","sequence":"first","affiliation":[{"name":"Department of Radiology Shanxi Province Cancer Hospital\/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences\/Cancer Hospital Affiliated to Shanxi Medical University Taiyuan China"},{"name":"Faculty of Graduate Studies Shanxi Medical University Taiyuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Feng","sequence":"additional","affiliation":[{"name":"Faculty of Graduate Studies Shanxi Medical University Taiyuan China"},{"name":"Department of Radiology, Changzhi People's Hospital Changzhi Medical College Changzhi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erli","family":"Pei","sequence":"additional","affiliation":[{"name":"Department of General Surgery, Yangpu Hospital, School of Medicine Tongji University Shanghai 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