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Current diagnostic methods often lack sufficient sensitivity and specificity, leading to unnecessary biopsy. With growing use of MRI and EAU guideline recommendations, this review synthesised evidence on MRI-based risk calculators (RCs) for PCa diagnosis and compared their performance with traditional clinical RCs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic search of Embase, Medline, Scopus, Cochrane Library, and Web of Science databases assessed the discriminatory ability of MRI-based RCs using Area Under the Curve (AUC). A meta-analysis was conducted to pool AUC estimates, assess heterogeneity, and compare the differences in discriminatory ability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of 2049 papers, 16 met the inclusion criteria. MRI-based RCs showed increased discrimination, with an AUC of 0.84 (95% CI: 0.81\u20130.86) for clinically significant PCa (csPCa), compared to 0.76 (95% CI: 0.73\u20130.79) for clinical models, and an AUC of 0.81 (95% CI: 0.78\u20130.84) for all PCa, compared to 0.74 (95% CI: 0.68\u20130.79). The pooled logit(AUC) difference was 0.49 units for csPCa and 0.37 units for all PCa. High heterogeneity was noted, likely due to PCa variability, and 31% of the studies had a high or unclear risk of bias, potentially affecting generali\n                      <jats:bold>s<\/jats:bold>\n                      ability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      MRI-based RCs improve the diagnostic accuracy for PCa with the potential to reduce unnecessary biopsies and optimi\n                      <jats:bold>s<\/jats:bold>\n                      e healthcare resources, thereby supporting their integration into clinical practice.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41391-025-01014-2","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T12:55:25Z","timestamp":1756212925000},"page":"247-257","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Clinical impact of MRI-based risk calculators for prostate cancer diagnosis: a systematic review and meta-analysis"],"prefix":"10.1038","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1990-5198","authenticated-orcid":false,"given":"Ciar\u00e1n","family":"Courtney O\u2019Toole","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nancy Fosua","family":"Boakye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ailish","family":"Hannigan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-8261","authenticated-orcid":false,"given":"Amirhossein","family":"Jalali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"1014_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.811044","volume":"10","author":"L Wang","year":"2022","unstructured":"Wang L, Lu B, He M, Wang Y, Wang Z, Du L. 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