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However, interpreting prostate mpMRI remains subjective, labor\u2010intensive, and more complex compared to traditional transrectal ultrasound. These challenges will likely grow as MRI is increasingly adopted for PCa screening and diagnosis. This development has sparked interest in non\u2010invasive artificial intelligence (AI) support, as larger and better\u2010labeled datasets now enable deep\u2010learning (DL) models to address important tasks in the prostate MRI workflow. Specifically, DL classification networks can be trained to differentiate between benign tissue and PCa, identify non\u2010clinically significant disease versus clinically significant disease, and predict high\u2010grade cancer at both the lesion and patient levels. This review focuses on the integration of DL classification networks with mpMRI for PCa assessment, examining key network architectures and strategies, the impact of different MRI sequence inputs on model performance, and the added value of incorporating domain knowledge and clinical information into MRI\u2010based DL classifiers. It also highlights reported comparisons between DL models and the Prostate Imaging Reporting and Data System (PI\u2010RADS) for PCa diagnosis and the potential of AI\u2010assisted predictions, alongside ongoing efforts to improve model explainability and interpretability to support clinical trust and adoption. It further discusses the potential role of DL\u2010based computer\u2010aided diagnosis systems in improving the prostate MRI reporting workflow while addressing current limitations and future outlooks to facilitate better clinical integration of these systems.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Evidence Level:<\/jats:bold>\n                    N\/A.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Technical Efficacy:<\/jats:bold>\n                    Stage 2.\n                  <\/jats:p>","DOI":"10.1002\/jmri.70004","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T10:45:41Z","timestamp":1751625941000},"page":"1603-1648","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing Prostate Cancer Classification: A Comprehensive Review of Multiparametric\n                    <scp>MRI<\/scp>\n                 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