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However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To investigate whether integrating multiparametric MRI (mp\u2010MRI) with clinical factors improves NMIBC 5\u2010year recurrence risk assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>One hundred ninety\u2010one patients (median age, 65\u2009years; age range, 54\u201373\u2009years; 27 females) underwent mp\u2010MRI between 2011 and 2017, and received \u22655\u2010year follow\u2010ups. They were divided into a training cohort (N\u2009=\u2009115) and validation\/testing cohorts (N\u2009=\u200938 in each). Recurrence rates were 23.5% (27\/115) in the training cohort and 23.7% (9\/38) in both validation and testing cohorts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3\u2010T, fast spin echo T2\u2010weighted imaging (T2WI), single\u2010shot echo planar diffusion\u2010weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast\u2010enhanced (DCE) sequences.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp\u2010MRI. Four models were developed, including clinical, cROI\u2010based radiomics, DL, and clinical\u2010radiomics\u2010DL (CRDL) models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Student's <jats:italic>t<\/jats:italic>\u2010tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan\u2013Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A <jats:italic>P<\/jats:italic>\u2010value &lt;0.05 was considered statistically significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The cROI\u2010based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792\u20130.985) compared to other models in the testing cohort for assessing 5\u2010year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749\u20130.859) for estimating recurrence\u2010free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>Integrating cROI\u2010based radiomics and DL features from preoperative mp\u2010MRI with clinical factors could improve 5\u2010year recurrence risk assessment in NMIBC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 3<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29574","type":"journal-article","created":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T22:45:31Z","timestamp":1724280331000},"page":"1442-1456","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multiparametric <scp>MRI<\/scp>\u2010Based Deep Learning Radiomics Model for Assessing 5\u2010Year Recurrence Risk in Non\u2010Muscle Invasive 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