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

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&E slides. Nuclear morphometric features are essential for disease diagnosis and gaining insights into cellular functions. Specifically, we sought to evaluate whether the computational pathology approach could provide additional granular risk stratification within the low-risk group identified by Decipher. Methods: The study employed two cohorts from the University of Pennsylvania D1 and D2. D1 comprised 180 patients, while D2 comprised 175 patients, including 63 individuals from the Decipher low-risk group. None of the patients in either cohort had received any pre-operative treatments. Within the Decipher cohort, a tumor segmentation model was created using the U-net deep learning network. The HoVer-Net model was used to both segment and classify nuclei. The feature extracted included a combination of nuclear texture and quantitative measurements of the spatial arrangement of nuclei. The eleven most significant features, selected via the least absolute shrinkage and selection operator (LASSO), were utilized to build a Cox regression model for predicting the risk of BCR in D1. In D2, patients were classified as either low-risk or high-risk for BCR based on the median risk score derived from D1. Results: Patients from D1 identified as high risk by the AI model experienced shorter survival, with a median BCR time of 27 months, in contrast to 55 months for those classified as low risk. The AI model demonstrated significant prognostic value for BCR in both D1 (HR = 6.82, 95% confidence interval [CI]: 3.54-13.12, p < 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(6_Suppl):Abstract nr 7669.