{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:10:06Z","timestamp":1791915006448,"version":"4.3.4"},"reference-count":59,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"E.J. Whitten Postgraduate Research Scholarship"},{"name":"Epworth Prostate Cancer Research Grant"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Biochemical recurrence (BCR) after radical prostatectomy (RP) is a significant predictor of distal metastases and mortality in prostate cancer (PCa) patients. This systematic review aims to evaluate the accuracy of artificial intelligence (AI) in predicting BCR post-RP. Methods: Adhering to PRISMA guidelines, a comprehensive literature search was conducted across Medline, Embase, Web of Science, and IEEE Xplore. Studies were included if they utilised AI to predict BCR in patients post-RP. Studies involving patients who underwent radiotherapy or salvage RP were excluded. This systematic review was registered on PROSPERO (International prospective register of systematic reviews) under the ID CRD42023482392. Results: After screening 9764 articles, 24 met the inclusion criteria. The included studies involved 27,216 patients, of whom 7267 developed BCR. AI algorithms developed using radiological parameters demonstrated higher predictive accuracy (median AUROC of 0.90) compared to algorithms based solely on pathological variables (median AUROC of 0.74) or clinicopathological variables (median AUROC of 0.81). According to the Prediction Model Risk of Bias Assessment Tool (PROBAST), the overall risk of bias was unclear in three studies due to ambiguous inclusion criteria and the exclusion of many patients because of missing follow-up data. In seven studies, the developed AI outperformed or was at least equivocal to traditional methods of BCR prediction. Conclusions: AI shows promise in predicting BCR post-RP, particularly when radiological data were used in its development. However, the significant variability in AI performance and study methodologies highlights the need for larger, standardised prospective studies with external validation prior to clinical application.<\/jats:p>","DOI":"10.3390\/cancers16213596","type":"journal-article","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T03:46:04Z","timestamp":1729827964000},"page":"3596","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5520-2011","authenticated-orcid":false,"given":"Jianliang","family":"Liu","sequence":"first","affiliation":[{"name":"E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne 3002, Australia"},{"name":"Department of Urology, The Royal Melbourne Hospital, The University of Melbourne, Melbourne 3052, Australia"},{"name":"Department of Surgery, The University of Melbourne, Melbourne 3052, Australia"},{"name":"Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne 3051, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne 3002, Australia"},{"name":"Department of Surgery, The University of Melbourne, Melbourne 3052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dixon T. S.","family":"Woon","sequence":"additional","affiliation":[{"name":"E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne 3002, Australia"},{"name":"Department of Surgery, The University of Melbourne, Melbourne 3052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1138-6389","authenticated-orcid":false,"given":"Marlon","family":"Perera","sequence":"additional","affiliation":[{"name":"E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne 3002, Australia"},{"name":"Department of Surgery, The University of Melbourne, Melbourne 3052, Australia"},{"name":"Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne 3051, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8553-5618","authenticated-orcid":false,"given":"Nathan","family":"Lawrentschuk","sequence":"additional","affiliation":[{"name":"E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne 3002, Australia"},{"name":"Department of Urology, The Royal Melbourne Hospital, The University of Melbourne, Melbourne 3052, Australia"},{"name":"Department of Surgery, The University of Melbourne, Melbourne 3052, Australia"},{"name":"Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne 3051, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.juro.2006.10.097","article-title":"Variation in the definition of biochemical recurrence in patients treated for localized prostate cancer: The American Urological Association Prostate Guidelines for Localized Prostate Cancer Update Panel report and recommendations for a standard in the reporting of surgical outcomes","volume":"177","author":"Cookson","year":"2007","journal-title":"J. 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