Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.

Abstract

Purpose: Artificial intelligence (AI) tools that identify pathologic features from digitized whole-slide images (WSIs) of prostate cancer (CaP) generate data to predict outcomes. The objective of this study was to evaluate the clinical validity of an AI-enabled prognostic test, PATHOMIQ_PRAD, using a clinical cohort from the Cleveland Clinic.

Methods: We conducted a retrospective analysis of PATHOMIQ_PRAD using CaP WSIs from patients who underwent radical prostatectomy (RP) between 2009 and 2022 and did not receive adjuvant therapy. Patients also had Decipher genomic testing available. WSIs were deidentified, anonymized, and outcomes were blinded. Patients were stratified into high-risk and low-risk categories on the basis of predetermined thresholds for PATHOMIQ_PRAD scores (0.45 for biochemical recurrence [BCR] and 0.55 for distant metastasis [DM]).

Results: The study included 344 patients who underwent RP with a median follow-up of 4.3 years. Both PathomIQ and Decipher scores were associated with rates of biochemical recurrence-free survival (BCRFS; PathomIQ score >0.45 v ≤0.45, P <.001; Decipher score >0.6 v ≤0.6, P = .002). There were 16 patients who had DM, and 15 were in the high-risk PathomIQ group (Mets Score >0.55). Both PathomIQ and Decipher scores were associated with rates of metastasis-free survival (PathomIQ score >0.55 v ≤0.55, P <.001; Decipher score >0.6 v ≤0.6, P = .0052). Despite the low event rates for metastasis, multivariable regression demonstrated that high PathomIQ score was significantly associated with DM (>0.55 v ≤0.55, hazard ratio, 10.10 [95% CI, 1.28 to 76.92], P = .0284).

Conclusion: These findings independently validate PATHOMIQ_PRAD as a reliable predictor of clinical risk in the postprostatectomy setting. PATHOMIQ_PRAD therefore merits prospective evaluation as a risk stratification tool to select patients for adjuvant or early salvage interventions.