ASCO 2023: Digital Histopathology-Based Multimodal Artificial Intelligence Scores Predict Risk of Progression in a Randomized Phase III Trial in Patients with Nonmetastatic Castration-Resistant Prostate Cancer

( Dr. Felix Feng presented on the use of digital histopathology-based multimodal artificial intelligence scores to predict risk of progression in a randomized phase III trial in patients with nonmetastatic castration-resistant prostate cancer.


As background, Dr. Feng noted that apalutamide has been approved for the treatment of nmCSPC based on the strength of the SPARTAN trial.1 Ultimately many patients treated with ADT or ARPI will eventually progress.

He noted that AI-enabled tools developed to predict oncological outcomes from digitized whole-slide images (WSIs) of H&E-stained tissues are a practical alternative to costly genomic testing tools that require sufficient tumor material.

In this study, he and his collaborators applied a previously-reported digital histopathology-based multimodal AI (MMAI) algorithm developed based on 5 phase III randomized trials, validated, and accepted to NCCN guidelines for localized prostate cancer, to evaluate whether MMAI could define risk of progression among nmCRPC patients treated with APA or placebo in the SPARTAN trial.

The utilized data from patients enrolled in the SPARTAN trial. Available H&E-stained biopsy slides from their primary diagnosis were included. H&E slides were digitized. Baseline clinical parameters to generate MMAI scores were Gleason score, age, T stage, and PSA. MMAI scores for distant metastasis (DM) were generated, ranging from 0 to 1. Pts were further categorized into MMAI non-high-risk and high-risk groups using a previously established score cutoff. Kaplan Meier estimates were calculated for PFS2 and MFS; comparisons were performed using log-rank test and Cox proportional-hazards regression for treatment arms and MMAI risk groups. Two-way ANOVA was used to evaluate the interaction between treatment arms and risk groups.

Consort diagram below highlights the study design:

ASCO 2023 Feng Digital AI SPARTAN_0 


The study included 471 pts with 1051 biopsy pathology slides: 311 pts treated with APA, and 156 with placebo. 55 pts were excluded due to missing treatment (n=4) or clinical data (n=49) and inadequate H&E images (n=2), resulting in 273 evaluable APA-treated and 147 placebo-treated pts.

Baseline demographics are below, similar to the original SPARTAN study data.

ASCO 2023 Feng Digital AI SPARTAN_1 

63% of pts were MMAI high risk and 37% MMAI non-high risk based on clinical and histologic data.

MMAI high risk pts demonstrated significant improvement in MFS with APA (HR 0.19 (95% CI: 0.12-0.29, p<0.005)), but not in PFS2 (HR 0.76 (95% CI: 0.45-1.28, p=0.30)).

Figures 2-4 highlight key oncologic outcomes stratified by MMAI risk category:

ASCO 2023 Feng Digital AI SPARTAN_2 


There was a significant interaction between MMAI risk group and treatment for MFS (p=0.02). Among the placebo-treated cohort, MMAI high risk status was associated with shorter MFS (HR 2.98 (95% CI: 1.72-5.18, p<0.005)) and PFS2 (HR 1.83 (95% CI: 1.09-3.09, p=0.02)). For APA-treated pts, MMAI risk group was not associated with MFS and PFS2.

As expected, the MMAI risk score accurately predicted patients at risk of progression – hence the difference in outcomes in the placebo group. However, these finding suggest that MMAI may provide prognostic risk stratification for nmCRPC pts and that MMAI high-risk pts may benefit most from treatment with APA. MMAI low risk patients may not gain much added benefit from the addition of apalutamide.

The current results represent the first evaluation of this MMAI classifier in the nmCSPC setting. Prospective validation is warranted.

This is a very interesting study and may help de-escalate therapy for a subset of patient with nmCSPC – patients with low MMAI risk may not benefit from the addition of apalutamide and perhaps that can safely be withheld.


Presented by: Felix Feng, MD, Vice Chair for Translational Research, Department of Radiation Oncology; Professor of Radiation Oncology, Urology, and Medicine, UCSF


Written by: Thenappan (Thenu) Chandrasekar, MD – Urologic Oncologist, Associate Professor of Urology, University of California, Davis, @tchandra_uromd @UCDavisUrology on Twitter during the 2023 American Society of Clinical Oncology (ASCO) Annual Meeting, Chicago, IL, Fri, June 2 – Tues, June 6, 2023.

  1. Smith MR, Saad F, Chowdhury S, et al. N Engl J Med. 2018;378(15):1408-1418. doi:10.1056/NEJMoa1715546.