{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:04:05Z","timestamp":1775801045464,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"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 assessment of 90\u2010day functional outcomes after anterior circulation large vessel occlusion (LVO) stroke remains challenging. Conventional models relying on a single data dimension have limited assessment power, suggesting that a multidimensional integration strategy could enhance evaluations.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To develop and validate an interpretable machine learning model that integrates radiomics, infarct location, brain frailty, and clinical variables for assessing 90\u2010day functional outcomes in LVO stroke.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n \n 1051 patients with anterior circulation LVO stroke (mean age 63\u2009\u00b1\u200913\u2009years; 722 males) from five centers (2018\u20132023). Eight hundred and seventy\u2010five patients from four centers formed the training (\n n<\/jats:italic>\n \u2009=\u2009612) and internal validation (\n n<\/jats:italic>\n \u2009=\u2009263) cohorts, while 176 from the fifth center comprised the external validation cohort.\n <\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n T1\u2010weighted spin\u2010echo imaging (T1WI), T2\u2010weighted spin\u2010echo imaging (T2WI), T2\u2010weighted fluid\u2010attenuated inversion recovery (FLAIR) imaging, and diffusion\u2010weighted echo\u2010planar imaging (DWI).<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Infarct volume and radiomic features were extracted from DWI. Infarct location was assessed using the Alberta Stroke Program Early CT Score. Brain frailty was evaluated using cortical\/subcortical atrophy, white matter hyperintensity (WMH), and old infarcts. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Chi\u2010square, Fisher's exact,\n t<\/jats:italic>\n \u2010test, Mann\u2013Whitney\n U<\/jats:italic>\n , area under the receiver operating characteristic curve (AUC), DeLong test, decision curve analysis, calibration curves, sensitivity, specificity, positive predictive value, negative predictive value,\n F<\/jats:italic>\n 1 score. Significance level\n p<\/jats:italic>\n \u2009<\u20090.05.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The fused model outperformed all single\u2010dimension models (\u0394AUC\u2009=\u20090.12\u20130.22), achieving AUCs of 0.87 (training), 0.84 (internal validation), and 0.86 (external validation). The fused model achieved a sensitivity and a specificity of 0.80 in the external validation cohort. Features with the highest mean absolute Shapley Additive Explanations (SHAP) values included lentiform nucleus lesion burden (SHAP\u2009=\u20090.083), WMH (SHAP\u2009=\u20090.080), and lesion burden in the M6 region (posterior middle cerebral artery territory; SHAP\u2009=\u20090.061).<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n Integration of infarct location, brain frailty, radiomics, and clinical features improved the 90\u2010day outcome assessment in anterior circulation LVO stroke, providing an interpretable tool for personalized prognosis.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy Stage<\/jats:title>\n 2.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70233","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T20:47:16Z","timestamp":1767818836000},"page":"1308-1320","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Outcome Assessment in Stroke Using Multiparametric MRI: Integrating Infarct Location, Radiomics, and Global Brain Frailty"],"prefix":"10.1002","volume":"63","author":[{"given":"JiaNan","family":"Li","sequence":"first","affiliation":[{"name":"Department of Medical Imaging Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University Nanjing Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JianRui","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University Nanjing Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"LiJun","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Radiology Shenzhen Xinhua Hospital Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"LiYing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Nanjing Jinling Hospital, the First School of Clinical Medicine, Southern Medical University Nanjing Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"LuSiYing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Nanjing Jinling Hospital, XuZhou Medical University Nanjing Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SiJia","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Nanjing Jinling Hospital, XuZhou Medical University Nanjing Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lulu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Neurology Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University Nanjing Jiangsu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZeHong","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Research and Development Shanghai United Imaging Intelligence Co., Ltd. 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