A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers. (22nd February 2022) - Huang, Wei; Randhawa, Ramandeep; Jain, Parag; Hubbard, Samuel; Eickhoff, Jens; Kummar, Shivaani; Wilding, George; Basu, Hirak; Roy, Rajat
Abstract : PURPOSE: To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS: Deep convolutional neural networks were used to develop the AI model. The AI model was trained on The Cancer Genome Atlas Prostatic Adenocarcinoma (TCGA-PRAD) whole slide images (WSI) and data set (n = 243) to predict 3-year biochemical recurrence after radical prostatectomy (RP) and was subsequently validated on WSI from patients with PCa (n = 173) from the University of Wisconsin-Madison. RESULTS: Our AI-powered platform can extract visual and subvisual morphologic features from WSI to identify driver regions predictive of early recurrence of PCa (regions of interest [ROIs]) after RP. The ROIs were ranked with AI-morphometric scores, which were prognostic for 3-year biochemical recurrence (area under the curve [AUC], 0.78), which is significantly better than the GGG overall (AUC, 0.62). The AI-morphometric scores also showed high accuracy in the prediction of recurrence for low- or intermediate-risk PCa—AUC, 0.76, 0.84, and 0.81 for GGG1, GGG2, and GGG3, respectively. These patients could benefit the most from timely adjuvant therapy after RP. The predictive value of the high-scored ROIs was validated by known PCa biomarkers studied. With this focused biomarker analysis, a potentially new STING pathway–related PCa biomarker—TMEM173—was identified. CONCLUSION: Our study introduces a novel approach for identifying patients with PCa at risk for early recurrence regardless of their GGG status and for identifying cancer drivers for focused evolution-aware novel biomarker discovery. Abstract :