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2022 Nelson & Claudia Peltz – PCF Young Investigator Award

Leveraging Artificial Intelligence for Optimization of Active Surveillance and Genetic Testing in Patients with Localized Prostate Cancer

Udit Singhal, MD
University of Michigan

Mentors: Todd Morgan, MD, Felix Feng, MD, Daniel Lin, MD

Description:

  • Active surveillance is an established management strategy for patients with low-risk prostate cancer. However, no reliable biomarkers exist to predict progression on active surveillance with high fidelity.
  • There also remains a critical need to develop a strategy for improved detection of inherited mutations in patients with localized prostate cancer.
  • Dr. Udit Singhal is developing and validating novel artificial intelligence (AI) biomarkers using digital pathology images for prediction of progression on active surveillance and for detection of heritable germline prostate cancer risk alterations.
  • In this project, Dr. Singhal will develop an AI algorithm that can evaluate digital pathology slides and predict risk of progression for patients on active surveillance for prostate cancer.
  • An AI biomarker to identify patients with localized prostate cancer likely to harbor underlying germline mutations in DNA damage repair genes will be developed and validated.
  • If successful, this project will lead to the development of automated, AI biomarkers for predicting progression on active surveillance and detecting germline pathogenic variations from digital pathology in patients with localized prostate cancer.

What this means to patients: The integration of population-wide, AI-based biomarkers into the routine clinical care of patients with localized prostate cancer will improve risk stratification and predict therapy responses. Dr. Singhal and team will develop AI-based digital pathology methods to predict progression on active surveillance and to detect germline pathogenic variations in patients with localized prostate cancer. These findings will help facilitate prostate cancer prognostication and guide treatment decisions based on germline mutational status, to ultimately improve patient outcomes.