Abstract 7669: Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category

Abstract Background: Current tools for predicting prostate cancer biochemical recurrence (BCR) after RP often depend on parameters determined by pathologists, such as tumor grade, which is known to vary between reviewers. Genomic risk classifiers provide useful information but require substantial tissue, are costly, and are not commonly accessible in many medical centers. The study presents an artificial intelligence (AI) model for automated risk assessment of biochemical recurrence (BCR) by analyzing nuclear morphologic patterns from Pca archival H 0. 0019), and D2 (HR = 2. 72, 95% CI: 1. 53-4. 82, p 0. 0032). Furthermore, the model effectively stratified the Decipher low-risk group in D2 into distinct low and high-risk categories (HR = 4. 52, 95% confidence interval CI: 1. 70-12. 05, p 0. 0085). The most prognostic features identified included five related to nuclei shape diversity (Minor Axis Length, Eccentricity, Equivalent Diameter, Solidity, and Circularity) in conjunction with nine nuclear texture features. Conclusions: The AI model's findings suggest that a digital image-based prognostic classifier for prostate cancer could serve as an alternative or complementary method to molecular-based companion risk tests. Additional, independent multi-site and prospective validation of these findings are warranted. Citation Format: Kamal Hammouda, Tilak Pathak, Tuomas Mirtti, Priti Lal, Shilpa Gupta, Anant Madabhushi. Use of computational pathology to predict biochemical recurrence in prostate cancer (Pca) (Rp) patients following radical prostatectomy: Evaluation in low decipher risk category abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 7669.