{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T20:06:09Z","timestamp":1793217969513,"version":"4.5.0"},"reference-count":37,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Siemens Healthineers","award":["Siemens Healthineers Individual Project Agreement No. 21"],"award-info":[{"award-number":["Siemens Healthineers Individual Project Agreement No. 21"]}]},{"name":"Siemens Healthineers","award":["FastMRI LSHM20103-H054"],"award-info":[{"award-number":["FastMRI LSHM20103-H054"]}]},{"name":"HealthHolland","award":["Siemens Healthineers Individual Project Agreement No. 21"],"award-info":[{"award-number":["Siemens Healthineers Individual Project Agreement No. 21"]}]},{"name":"HealthHolland","award":["FastMRI LSHM20103-H054"],"award-info":[{"award-number":["FastMRI LSHM20103-H054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background: Deep learning (DL)-based models have demonstrated an ability to automatically diagnose clinically significant prostate cancer (PCa) on MRI scans and are regularly reported to approach expert performance. The aim of this work was to systematically review the literature comparing deep learning (DL) systems to radiologists in order to evaluate the comparative performance of current state-of-the-art deep learning models and radiologists. Methods: This systematic review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Studies investigating DL models for diagnosing clinically significant (cs) PCa on MRI were included. The quality and risk of bias of each study were assessed using the checklist for AI in medical imaging (CLAIM) and QUADAS-2, respectively. Patient level and lesion-based diagnostic performance were separately evaluated by comparing the sensitivity achieved by DL and radiologists at an identical specificity and the false positives per patient, respectively. Results: The final selection consisted of eight studies with a combined 7337 patients. The median study quality with CLAIM was 74.1% (IQR: 70.6\u201377.6). DL achieved an identical patient-level performance to the radiologists for PI-RADS \u2265 3 (both 97.7%, SD = 2.1%). DL had a lower sensitivity for PI-RADS \u2265 4 (84.2% vs. 88.8%, p = 0.43). The sensitivity of DL for lesion localization was also between 2% and 12.5% lower than that of the radiologists. Conclusions: DL models for the diagnosis of csPCa on MRI appear to approach the performance of experts but currently have a lower sensitivity compared to experienced radiologists. There is a need for studies with larger datasets and for validation on external data.<\/jats:p>","DOI":"10.3390\/life12101490","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T01:51:49Z","timestamp":1664329909000},"page":"1490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI: A Systematic Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3575-4465","authenticated-orcid":false,"given":"Christian","family":"Roest","sequence":"first","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5454-5052","authenticated-orcid":false,"given":"Stefan J","family":"Fransen","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas C","family":"Kwee","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4920-9441","authenticated-orcid":false,"given":"Derya","family":"Yakar","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA A Cancer J. 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