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Calculating PV from MRI is a time-consuming, radiologist-dependent task. The aim of this study was to assess whether a deep learning algorithm can replace PI-RADS 2.1 based ellipsoid formula (EF) for calculating PV.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Eight different measures of PV were retrospectively collected for each of 124 patients who underwent radical prostatectomy and preoperative MRI of the prostate (multicenter and multi-scanner MRI\u2019s 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PV<jats:sub>DL<\/jats:sub>) and ellipsoid formula by two radiologists (PV<jats:sub>EF1<\/jats:sub> and PV<jats:sub>EF2<\/jats:sub>) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PV<jats:sub>MPE<\/jats:sub>). A sensitivity analysis was performed using a prostatectomy specimen as the reference standard. Inter-reader agreement was evaluated between the radiologists using the ellipsoid formula and between the expert and inexperienced radiologists performing manual planimetry.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>PV<jats:sub>DL<\/jats:sub> showed better agreement and precision than PV<jats:sub>EF1<\/jats:sub> and PV<jats:sub>EF2<\/jats:sub> using the reference standard PV<jats:sub>MPE<\/jats:sub> (mean difference [95% limits of agreement] PV<jats:sub>DL<\/jats:sub>: \u22120.33 [\u221210.80; 10.14], PV<jats:sub>EF1<\/jats:sub>: \u22123.83 [\u221219.55; 11.89], PV<jats:sub>EF2<\/jats:sub>: \u22123.05 [\u221218.55; 12.45]) or the PV determined based on specimen weight (PV<jats:sub>DL<\/jats:sub>: \u22124.22 [\u221222.52; 14.07], PV<jats:sub>EF1<\/jats:sub>: \u22127.89 [\u221230.50; 14.73], PV<jats:sub>EF2<\/jats:sub>: \u22126.97 [\u221230.13; 16.18]). Inter-reader agreement was excellent between the two experienced radiologists using the ellipsoid formula and was good between expert and inexperienced radiologists performing manual planimetry.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:italic>\u2022 A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-022-09239-8","type":"journal-article","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T22:02:57Z","timestamp":1668290577000},"page":"2519-2528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4663-8520","authenticated-orcid":false,"given":"Erik","family":"Thimansson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Bengtsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Baubeta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Engman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Flondell-Sit\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Bjartell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Zackrisson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"9239_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.5152\/dir.2014.13322","volume":"20","author":"B Garvey","year":"2014","unstructured":"Garvey B, T\u00fcrkbey B, Truong H et al (2014) Clinical value of prostate segmentation and volume determination on MRI in benign prostatic hyperplasia. 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Anders Bjartell has received grants\/research supports from Ferring, Bayer and Merck. AB has received honoraria or consultation fees from Astellas, AstraZeneca, Bayer, Janssen, Merck, Recordati and Sandoz. AB has participated in company sponsored speaker\u00b4s bureau by Astellas, Bayer, IPSEN, Janssen, Recordati and Sandoz. AB is stock shareholder in LIDDS Pharma, Glactone Pharma and WntResearch.All other authors declare no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Andrea Dahl Sturedahl kindly provided statistical advice for this manuscript.No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 retrospective\u2022 diagnostic or prognostic study\u2022 multicenter study","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}