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An AI-Digital Pathology Algorithm Predicts Survival after Radical Prostatectomy from the PLCO Trial.

About this Publication
Title
An AI-Digital Pathology Algorithm Predicts Survival after Radical Prostatectomy from the PLCO Trial.
Pubmed ID
39841869 (View this publication on the PubMed website)
Digital Object Identifier
Publication
J Urol. 2025 Jan 22; Pages 101097JU0000000000004435
Authors
Li EV, Ren Y, Griffin J, Han C, Yamashita R, Mitani A, Zhou R, Huang HC, Yang X, Feng FY, Esteva A, Patel HD, Schaeffer EM, Cooper LAD, Ross AE
Affiliations
  • Northwestern University Feinberg School of Medicine, Department of Urology.
  • Artera, Inc.
  • University of California in Los Angeles, Department of Pathology.
  • Northwestern University Feinberg School of Medicine, Department of Pathology.
  • University of California San Francisco, Department of Radiation Oncology.
Abstract

PURPOSE: Clinical variables alone have limited ability to determine which patients will have recurrence after radical prostatectomy (RP). We evaluated the ability of locked multimodal artificial intelligence (MMAI) algorithms trained on prostate biopsy specimens to predict prostate cancer specific mortality (PCSM) and overall survival (OS) among patients undergoing radical prostatectomy with digitized RP specimens.

MATERIALS AND METHODS: The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Randomized Controlled Trial randomized subjects from 1993-2001 to cancer screening or control. A subset of patients who underwent RP with available digitized histopathological images and subsequent survival data were identified. Distant metastasis (DM) and PCSM MMAIs originally trained on biopsy slides for patients undergoing radiation were evaluated for prediction of PCSM and OS. Cox proportional hazards modeling and Kaplan Meier survival curve analysis were utilized.

RESULTS: 1032 patients who underwent RP with median follow up of 17 years (IQR 14.3, 19.3 years) were identified. MMAI algorithms for PCSM and DM both predicted PCSM (HR 2.31, 95% confidence interval [CI] 1.6-3.35, p<0.001, and HR 1.96, 95% CI 1.35-2.85, p<0.001, respectively). Similarly, DM and PCSM MMAI predicted OS (HR 1.22, 95% CI 1.01-1.47, p=0.04 and HR 1.19, 95% CI 1.02-1.4, p=0.03).

CONCLUSIONS: Locked MMAI algorithms previously developed and validated on biopsy specimens from patients undergoing radiation for prostate cancer successfully predicted clinical outcomes when applied to RP specimens from patients treated with surgery. MMAI models and other biomarkers may help select patients who may benefit from post-operative treatment intensification with androgen deprivation therapy or radiation.

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