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<p>This prognostic study created optimized ensembles of calibrated random forest models to predict clinically significant prostate cancer (csPCa, grade group ≥2 PCa) using total prostate-specific antigen (PSA), free PSA, negative biopsy status, and age, with or without DRE and MRI data. Observational data were aggregated from cohorts in six organizations in Canada, the USA, and Czechia. Prostate biopsies were performed between 2009 and 2024. Risk models (ClarityDX Prostate + DRE, ClarityDX Prostate + MRI, and ClarityDX Prostate + MRI + DRE) were derived (training cohorts <i>n</i> = 1626 to 2191) and validated (validation cohorts <i>n</i> = 378 to 1318) from different clinical sites. The models had ROC AUC values ≥ 0.80. Adding DRE improved the ROC AUC to 0.82 while models using MRI features had ROC AUC values of 0.87 (without DRE) and 0.88 (with DRE) in the validation cohort. These four ClarityDX Prostate models offer high accuracy in predicting csPCa in individuals in variable clinical settings.</p>

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Predicting clinically significant prostate cancer with or without digital rectal exam and MRI data using ClarityDX Prostate models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月17日

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  • Robert J. Paproski,
  • Adam Kinnaird,
  • M. Eric Hyndman,
  • Adrian Fairey,
  • Leonard Marks,
  • Christian P. Pavlovich,
  • Sean A. Fletcher,
  • Roman Zachoval,
  • Vanda Adamcova,
  • Jiri Stejskal,
  • Armen Aprikian,
  • Christopher J. D. Wallis,
  • Desmond Pink,
  • Catalina Vasquez,
  • Perrin H. Beatty,
  • John D. Lewis

摘要

This prognostic study created optimized ensembles of calibrated random forest models to predict clinically significant prostate cancer (csPCa, grade group ≥2 PCa) using total prostate-specific antigen (PSA), free PSA, negative biopsy status, and age, with or without DRE and MRI data. Observational data were aggregated from cohorts in six organizations in Canada, the USA, and Czechia. Prostate biopsies were performed between 2009 and 2024. Risk models (ClarityDX Prostate + DRE, ClarityDX Prostate + MRI, and ClarityDX Prostate + MRI + DRE) were derived (training cohorts n = 1626 to 2191) and validated (validation cohorts n = 378 to 1318) from different clinical sites. The models had ROC AUC values ≥ 0.80. Adding DRE improved the ROC AUC to 0.82 while models using MRI features had ROC AUC values of 0.87 (without DRE) and 0.88 (with DRE) in the validation cohort. These four ClarityDX Prostate models offer high accuracy in predicting csPCa in individuals in variable clinical settings.