Development and prospective validation of a prostate cancer detection, grading, and workflow optimization system at an academic medical center

Ramin Nateghi; Ruoji Zhou; Madeline Saft; Marina Schnauss; Clayton Neill; Ridwan Alam; Nicole Handa; Mitchell Huang; Eric V Li; Jeffery A Goldstein; Edward M Schaeffer; Menatalla Nadim; Fattaneh Pourakpour; Bogdan Isaila; Christopher Felicelli; Vikas Mehta; Behtash G Nezami; Ashley Ross; Ximing Yang; Lee AD Cooper

arXiv · 2410.23642

Development and prospective validation of a prostate cancer detection, grading, and workflow optimization system at an academic medical center

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Abstract

Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commercial alternatives. From August 2021 to March 2023, we scanned 21,396 slides from 1,147 patients receiving prostate biopsy. We developed models for cancer detection, grading, and screening of equivocal cases for IHC ordering. We compared the performance of task-specific prostate models with general-purpose foundation models in a prospectively collected dataset that reflects our patient population. We also evaluated the contributions of a bespoke model designed to improve sensitivity to small cancer foci and perception of low-resolution patterns. We found high concordance with pathologist ground-truth in detection (area under curve 98.5%, sensitivity 95.0%, and specificity 97.8%), ISUP grading (Cohen's kappa 0.869), grade group 3 or higher classification (area under curve 97.5%, sensitivity 94.9%, specificity 96.6%). Screening models could correctly classify 55% of biopsy blocks where immunohistochemistry was ordered with a 1.4% error rate. No statistically significant differences were observed between task-specific and foundation models in cancer detection, although the task-specific model is significantly smaller and faster. Institutions like academic medical centers that have high scanning volumes and report abstraction capabilities can develop highly accurate computational pathology models for internal use. These models have the potential to aid in quality control role and to improve resource allocation and workflow in the pathology lab to help meet future challenges in prostate cancer diagnosis.

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Ramin Nateghi, Ruoji Zhou, Madeline Saft, Marina Schnauss, Clayton Neill, Ridwan Alam, Nicole Handa, Mitchell Huang, Eric V Li, Jeffery A Goldstein, Edward M Schaeffer, Menatalla Nadim, Fattaneh Pourakpour, Bogdan Isaila, Christopher Felicelli, Vikas Mehta, Behtash G Nezami, Ashley Ross, Ximing Yang, Lee AD Cooper. 2025-03-16. Development and prospective validation of a prostate cancer detection, grading, and workflow optimization system at an academic medical center. https://arxiv.org/abs/2410.23642

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