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Objectives: Prostate volume (PV) in combination with prostate specific antigen (PSA) yields PSA density which is an increasingly important biomarker. 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., Methods: 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's 1.5 and 3 T). Agreement between volumes obtained from the deep learning algorithm (PV DL ) and ellipsoid formula by two radiologists (PV EF1 and PV EF2 ) was evaluated against the reference standard PV obtained by manual planimetry by an expert radiologist (PV MPE ). 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., Results: PV DL showed better agreement and precision than PV EF1 and PV EF2 using the reference standard PV MPE (mean difference [95% limits of agreement] PV DL : -0.33 [-10.80; 10.14], PV EF1 : -3.83 [-19.55; 11.89], PV EF2 : -3.05 [-18.55; 12.45]) or the PV determined based on specimen weight (PV DL : -4.22 [-22.52; 14.07], PV EF1 : -7.89 [-30.50; 14.73], PV EF2 : -6.97 [-30.13; 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., Conclusion: Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI., Key Points: • A commercially available deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI. • The deep-learning algorithm was previously untrained on this heterogenous multicenter day-to-day practice MRI data set., (© 2022. The Author(s).)

A correction for the article "Deep learning algorithm performs similarly to radiologists in the assessment of prostate volume on MRI " is presented, which is published in the December 6, 2022 issue of the periodical.