External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol.

Abstract

Background: Effective prognostication improves selection of patients with prostate cancer for treatment combinations. We aimed to evaluate whether a previously developed multimodal artificial intelligence (MMAI) algorithm was prognostic in very advanced prostate cancer using data from four phase 3 trials of the STAMPEDE platform protocol.

Methods: We included patients starting androgen-deprivation therapy in the docetaxel, docetaxel plus zoledronic acid, abiraterone, or abiraterone plus enzalutamide trials. Patients were recruited at 112 sites. We combined all standard-of-care control patients (including those allocated to standard of care [SOC-ADT] consisting of testosterone suppression with luteinising hormone-releasing hormone agonists or antagonists, and radiotherapy when indicated), and we combined the rest of the patients into docetaxel-treated or abiraterone-treated groups. Patients had either metastatic disease or were at very high-risk of metastatic disease, determined by node-positivity or, if node-negative, by T stage, serum prostate-specific antigen (PSA) level, and Gleason score. We used the locked ArteraAI Prostate MMAI algorithm that combined these clinical variables, age, and digitised prostate biopsy pathology images. We performed Fine-Gray and Cox regression adjusted for treatment allocation and cumulative incidence analyses at 5 years to evaluate associations with prostate cancer-specific mortality (PCSM) for continuous (per SD increase) and categorical (quartile-Q) scores. The STAMPEDE platform protocol is registered with ClinicalTrials.gov, NCT00268476.

Findings: Of 5213 eligible patients recruited from Oct 5, 2005, to March 31, 2016, 3167 were included in this analysis (1575 [49·7%] with non-metastatic disease, 1592 [50·3%] with metastatic disease; median follow-up 6·9 years [IQR 5·9-8·0]) with all datapoints available for score generation. The MMAI algorithm (per SD increase) was strongly associated with PCSM (hazard ratio [HR] 1·40, 95% CI 1·30-1·51, p<0·0001). On ad-hoc inspection, the highest scoring quartile of patients in each disease and treatment allocation group (MMAI Q4; vs the bottom three quartiles, Q1-3) had the highest PCSM risk in both patients with non-metastatic disease (HR 2·12, 1·61-2·81, p<0·0001) and those with metastatic disease (HR 1·62, 1·39-1·88, p<0·0001). MMAI quartile stratification split patients categorised by disease burden into groups with notably different risks of 5-year PCSM: patients with non-metastatic disease that were node-negative could be further stratified by MMAI score quartile Q1-3 (3%, 2-4) versus Q4 (11%, 7-15), those with non-metastatic disease that were node-positive could be stratified by Q1-3 (11%, 8-14) versus Q4 (20%, 13-26), those with metastatic disease with low-volume could be stratified by Q1-3 (27%, 23-31) versus Q4 (43%, 36-51), and those with metastatic disease with high-volume could be stratified by Q1-3 (48%, 44-52) versus Q4 (68%, 62-75).

Interpretation: Diagnostic prostate biopsy samples contain prognostic information in patients with, or at high-risk of, radiologically overt metastatic prostate cancer. MMAI algorithm combined with disease burden improves prognostication of advanced prostate cancer.

Funding: Prostate Cancer UK, UK Medical Research Council, Cancer Research UK, John Black Charitable Foundation, Prostate Cancer Foundation, Sanofi Aventis, Janssen, Astellas, Novartis, Artera.