Introduction The urethra is a recommended avoidance structure for prostate cancer treatment. However, even subspecialist physicians often struggle to accurately identify the urethra on available imaging. Automated segmentation tools show promise, but a lack of reliable ground truth or appropriate evaluation standards has hindered validation and clinical adoption. This study aims to establish a reference-standard dataset with expert consensus contours, define clinically meaningful evaluation metrics, and assess the performance and generalizability of a deep-learning-based segmentation model.

Materials and Methods A multidisciplinary panel of four experienced subspecialists in prostate MRI generated consensus contours of the male urethra for 71 patients across six imaging centers. Four of those cases were previously used in an international study (PURE-MRI), wherein 62 physicians attempted to contour the prostate and urethra on the patient images. Separately, we developed a deep-learning AI model for urethra segmentation using another 151 cases from one center and evaluated it against the consensus reference standard and compared to human performance using Dice Score, percent urethra Coverage, and Maximum 2D (axial, in-plane) Hausdorff Distance (HD) from the reference standard.

Results In the PURE-MRI dataset, the AI model outperformed most physicians, achieving a median Dice of 0.41 (vs. 0.33 for physicians), Coverage of 81% (vs. 36%), and Max 2D HD of 1.8 mm (vs. 1.6 mm). In the larger dataset, performance remained consistent, with a Dice of 0.40, Coverage of 89%, and Max 2D HD of 2.0 mm, indicating strong generalizability across a broader patient population and more varied imaging conditions.

Conclusion We established a multidisciplinary consensus benchmark for segmentation of the urethra. The deep-learning model performs comparably to specialist physicians and demonstrates consistent results across multiple institutions. It shows promise as a clinical decision-support tool for accurate and reliable urethra segmentation in prostate cancer radiotherapy planning and studies of dose-toxicity associations.

DJAM reports a clinical advisor role for Stratagen Bio and an ad hoc consultant role for Guerbet and Promaxo. SAW was supported by funding from the ARRS Scholarship for professional development and has received investigator-initiated research grants (paid to the institution) from Siemens. Sophia C. Kamran' spouse is employed by Sanof. AMD is a founder of and holds equity interest in CorTechs Labs and serves on its scientific advisory board. He is also a member of the Scientific Advisory Board of Healthlytix and receives research funding from General Electric Healthcare (GEHC). Rebecca Rakow-Penner Human Longevity Inc: Consultant, Cortechs Labs: Stock options, Curemetrix: Stock options, consultant Imagine Scientific, advisory board. SBIR GE Healthcare, research agreement Bayer consultant. Michael Liss Founder/President of Oncobiomix with no relation to this manuscript. TMS reports honoraria from Varian Medical Systems, WebMD, MJH Life Sciences, GE Healthcare, Blue Earth Diagnostics, and Janssen; he has an equity interest in CorTechs Labs, Inc. and serves on its Scientific Advisory Board; he receives research funding from GE Healthcare and Blue Earth Diagnostics, as well as in-kind research support from Quibim, Inc., both through the University of California San Diego. These companies might potentially benefit from the research results. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict-of-interest policies. The remaining authors have no conflicts of interest to disclose.

National Institutes of Health, Grant/Award Numbers: NIH/NIBIB K08EB026503, NIHUL1TR000100; American Society for Radiation Oncology; Prostate Cancer Foundation; Department of Defense, Grant/Award Number: DOD/CDMRPPC220278

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