Predicting long term outcomes following radical prostatectomy using a validated pathology-based multimodal artificial intelligence biomarker.
Authors: Cooperberg Matthew R. , Shee Kevin , Cowan Janet E , Todorovic Tamara , Tenggara Imelda , Tang Siyi , Yamashita Rikiya , Royce Trevor , Chen Emmalyn , Tierney Meghan , Ma Xiao , Huang Huei-Chung Rebecca , Feng Felix Y , Ding Chien-Kuang Cornelia , Showalter Timothy , Carroll Peter 
Published: February 2025

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Background:
Over the past decade, multiple biomarkers have been developed and validated to improve precision of prostate cancer prognosis estimation. However, these require access to archival tissue and central laboratory processing for RNA extraction, and as a result are labor-intensive and expensive. Deep learning models based on image analysis of standard clinical pathology slides may offer similar accuracy with much improved access and lower cost. Multimodal artificial intelligence (MMAI) models (Artera, Los Altos, CA) have previously been validated to prognosticate as well as predict response to intensified treatment among patients undergoing radiation therapy and have been endorsed in the NCCN prostate cancer guidelines. We determined the accuracy of an MMAI model in prognosticating outcomes after radical prostatectomy (RP) using tissue microarray (TMA) specimens.
Methods:
We previously built a TMA from 424 mostly low- to intermediate-risk RP cases with rich clinical annotation and long-term follow-up. We scanned this TMA at high resolution and determined MMAI (scored 0 to 1) and Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S, scored 0-12) scores. We performed logistic regression to determine the MMAI’s score association with adverse pathology (pT≥3a and/or grade group ≥3), and Cox proportional hazards regression to determine the independent ability of the MMAI score to predict biochemical recurrence (two PSA tests ≥0.2 ng/ml or any second treatment) and metastasis after RP. Odds ratios (OR) and hazard ratios (HR) were determined per 0.1 increase in MMAI score.
Results:
The TMA was successfully segmented and images and MMAI scores were able to be generated for 414 (98%) of cases. Median (IQR) follow-up was 13.2 (7.8-18.3) years. By CAPRA-S, 273 (66%), 114 (28%), and 24 (6%) were low (0-2), intermediate (3-5), and high (≥6) risk, respectively. Recurrence-free and metastasis-free survival were 74% and 96% at 10 years, respectively. Median (IQR) MMAI risk scores were 0.25 (0.18-0.33). On logistic regression, the MMAI score was significantly associated with risk of adverse pathology (OR: 1.05, 95% CI 1.03-1.07, p<0.01). On Cox regression adjusting for CAPRA-S, MMAI risk score was significantly associated with risk of both recurrence (HR 1.04, 95% CI 1.02-1.06, p<0.01) and metastasis (HR 1.05, 95% CI 1.02-1.07, p<0.01).
Conclusions:
In this cohort of largely lower-risk RP patients, the MMAI score derived from RP TMA samples —originally developed among radiation therapy patients on prostate biopsy samples—adds independent prognostic information above a well-validated multivariable clinical risk model. The MMAI platform allows for rapid, non-destructive analysis of standard pathology slides and should compare favorably to RNA-based platforms in terms of access, speed, and cost.