Validation of a Digital Pathology-Based Multimodal Artificial Intelligence Biomarker in a Prospective, Real-World Prostate Cancer Cohort Treated with Prostatectomy.

Abstract

A multimodal artificial intelligence (MMAI) biomarker was developed using clinical trial data from North American men with localized prostate cancer treated with definitive radiation, using biopsy digital pathology images and key clinical information (age, PSA, and T-stage) to generate prognostic scores. This study externally validates the biomarker in a prospective, real-world dataset of men who underwent radical prostatectomy (RP) for localized prostate cancer at a tertiary referral center in Sweden. Association between the MMAI scores (continuous and categorical) and endpoints of interest was assessed with Fine-Gray and cumulative incidence analyses for biochemical recurrence (BCR) and logistic regression for adverse pathology (AP) at RP. The analysis included 143 patients with evaluable biopsy pathology images and complete clinical data to generate MMAI scores. The median follow-up was 8.8 years. At diagnosis, the median PSA was 7.5 ng/mL, the median age was 64 years, 29% had a Gleason grade group ≥3, and 88 men were evaluable for AP at RP. MMAI was significantly associated with BCR [subdistribution HR, 2.45; 95% confidence interval (CI), 1.77-3.38; P < 0.001] and AP at RP (OR, 4.85; 95% CI, 2.54-10.78; P < 0.001). Estimated 5-year BCR rates for MMAI intermediate to high versus low were 25% (95% CI, 16%-36%) versus 4% (95% CI, 1%-11%), respectively. The MMAI biomarker, previously shown to be prognostic for distant metastasis and prostate cancer-specific mortality in men receiving definitive radiation, was prognostic for post-RP endpoints: BCR and AP. This biomarker validation study further supports the use of MMAI biomarkers in men with prostate cancer outside North America and those treated with RP.