Validation of a digital pathology-based multimodal artificial intelligence model in oligometastatic castration-sensitive prostate cancer, including in patients from the STOMP and ORIOLE phase II randomized clinical trials.
Authors: Sutera Philip Anthony , Deek Matthew Pierre , Mendes Adrianna , Song Yang , Shetty Amol , Van der Eecken Kim , Chen Emmalyn , Showalter Timothy N , Royce Trevor Joseph , Todorovic Tamara , Huang Huei-Chung , Houck Scott , Yamashita Rikiya , Kiess Ana Ponce , Song Daniel , Lotan Tamara L. , Esteva Andre , Feng Felix Y , Tran Phuoc T. , Ost Piet 
Published: June 2024

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Background: Oligometastatic castration-sensitive prostate cancer (omCSPC) is a state of limited metastatic disease. Randomized trials have demonstrated improvements in progression-free survival in patients with omCSPC treated with metastasis-directed therapy (MDT). However, clinical outcomes remain heterogenous and response to MDT is variable, raising the need for prognostic/predictive biomarkers. A multimodal artificial intelligence (MMAI) biomarker (ArteraAI Prostate Test) was recently trained using data from patients with localized prostate cancer and found to be prognostic. Here, we evaluated this biomarker in omCSPC. Methods: We performed an international multi-institution retrospective review of 221 men with omCSPC who were evaluated with MMAI scoring. The primary objective was to compare overall survival (OS) between patients with high- and low-MMAI score (stratified by median score). OS was defined as the time from diagnosis of omCSPC to death of any cause, calculated with the Kaplan-Meier method and compared using the log-rank test and Cox regression. The secondary objective was to evaluate MMAI score as predictive for MDT treatment effect in a subset of patients enrolled in the STOMP and ORIOLE randomized clinical trials. Given too few OS events for this subset, we evaluated MMAI for metastasis-free survival (MFS), defined as time of randomization to development of a new metastasis or death of any cause and analyzed using Cox regression. An interaction test was performed between treatment arm and MMAI score. Results: Median follow-up of the surviving patients was 38.0 months. Patients with high MMAI (>0.527) were found to have higher PSA (5.8 vs 2.9, p=0.005), higher Gleason score (68.2% vs 38.8% Grade Group ≥4, p<0.001), more likely to have de novo metastatic disease (28.2% vs 8.1%, p<0.001), and more likely to have bone metastases (55.5% vs 39.6%, p=0.019). Patients with a high MMAI had a significantly worse OS (HR=4.38, 95% CI=1.24-15.56; p=0.022) with a median OS of 108.4 mo. vs “not reached” (p=0.012). In the STOMP and ORIOLE subset (N=51; median follow-up=61 months), MMAI was not prognostic for MFS (HR=1.24, 95% CI=0.64-2.43, p=0.52). MMAI however was predictive for MDT benefit as patients with high (HR=0.32, 95% CI=0.12-0.90; p=0.03), but not low (HR=1.59, 95% CI=0.63-4.04; p=0.33) MMAI demonstrated improvement in MFS when treated with MDT (p-interaction=0.02). Conclusions: We have shown for the first time that the ArteraAI MMAI biomarker is prognostic for OS in patients with omCSPC. Further, MMAI appears to be predict benefit of MDT with high MMAI demonstrating a greater improvement in MFS following MDT over observation. Further work in validating these findings is warranted to allow for greater personalization in the management of patients with omCSPC.