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This study aims to develop and validate a transformer\u2010based model combining multi\u2010regional magnetic resonance imaging (MRI) features and clinical data to improve BCR prediction after radical prostatectomy in prostate cancer (PCa).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We enrolled 232 Pca patients from three medical centers between 2013 and 2020. Multi\u2010parametric MRI sequences, including T2\u2010weighted fat\u2010suppressed and diffusion\u2010weighted imaging, were collected before surgery. Patients from centers 1 and 2 were assigned to a discovery cohort (training:\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009146; internal validation:\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200936), and patients from center 3 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200950) formed an external test cohort. A transformer\u2010based deep learning (DL) model was developed using imaging features from intratumoral, peritumoral, and periprostatic regions, combined with clinical variables. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity with 95% confidence intervals (CI), and prognostic value was assessed via Kaplan\u2013Meier (K\u2010M) survival analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The combined three\u2010region MRI\u2010based model demonstrated superior predictive performance for BCR risk across all sets, with an AUC of 0.801 (0.663\u20130.920) on the external test set and an AUC of 0.799 (0.715\u20130.872) on the validation set. Notably, the integrated DL model, which combined MRI data with clinical characteristics, outperformed the MRI\u2010based and clinical models. The three\u2010region with clinical combined model improved AUCs from 0.801 (95% CI: 0.663\u20130.920) to 0.863 (95% CI: 0.697\u20130.975) on the external test set and from 0.799 (95% CI: 0.715\u20130.872) to 0.832 (95% CI: 0.746\u20130.904) on the validation set. K\u2010M curve analysis revealed that the integrated model provided significantly better predictions than the MRI\u2010based and clinical models (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By combining intratumoral, peritumoral, and periprostatic regions from multi\u2010parametric MRI with clinical data, the transformer\u2010based DL model provides a multimodal approach for predicting BCR in PCa patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.72342","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:19:54Z","timestamp":1792664394000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformer\u2010Based Deep Learning Integrating Multi\u2010Regional\n                    <scp>MRI<\/scp>\n                    and Clinical Features for Predicting Biochemical Recurrence After Radical Prostatectomy: A Multicenter Study"],"prefix":"10.1002","volume":"15","author":[{"given":"Mengting","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital, Southern Medical University  Guangzhou Guangdong China"},{"name":"Department of Ultrasound Huizhou Central People's Hospital  Huizhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Computing and Data Science The University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhua","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital, Southern Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoyao","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital, Southern Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bomiao","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Radiology Zhujiang Hospital of Southern Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinmei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital, Southern Medical University  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Huizhou Central People's Hospital  Huizhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9315-6527","authenticated-orcid":false,"given":"Lequan","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, School of Computing and Data Science The University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9969-1670","authenticated-orcid":false,"given":"Yinghua","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiology The Third Affiliated Hospital, Southern Medical University  Guangzhou Guangdong 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