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Related Experiment Video

Updated: Feb 9, 2026

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Predicting Patient-Reported Outcomes Following Treatment for Localized Prostate Cancer: Model Development and

Adam B Weiner1, Shannon C Martin2, Holly Wilhalme2

  • 1Department of Urology, David Geffen School of Medicine, University of California, Los Angeles, CA; Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA.

Clinical Genitourinary Cancer
|February 7, 2026
PubMed
Summary
This summary is machine-generated.

Predicting patient outcomes after prostate cancer treatment using baseline factors showed modest accuracy in a global study. Large international data highlights heterogeneity, suggesting a need for region-specific predictive models.

Keywords:
Clinical decision-makingProstatic neoplasms/diagnostic imagingProstatic neoplasms/epidemiologyProstatic neoplasms/therapyWatchful waiting

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Area of Science:

  • Oncology
  • Health Services Research
  • Biostatistics

Background:

  • Shared decision-making for localized prostate cancer (PCa) necessitates understanding factors predicting patient-reported outcomes (PROs).
  • Previous predictive models for PCa treatment outcomes were limited by small, single-region patient cohorts.
  • The Movember True North Global Registry (TNGR) offers a large, multi-national dataset for developing robust predictive models.

Purpose of the Study:

  • To develop and evaluate predictive models for 12-month PROs in localized PCa patients.
  • To utilize the extensive, multi-national TNGR dataset for enhanced model development.
  • To identify baseline factors that predict functional outcomes after PCa treatment.

Main Methods:

  • A cohort of 27,499 men with localized PCa from 15 countries (2016-2022) was analyzed.
  • Patients were randomly assigned to training (n=18,332) and validation (n=9,167) cohorts.
  • Multivariable linear regressions integrated baseline PROs, demographics, country, treatment modality, and tumor characteristics to predict changes in 5 EPIC-26 domains.

Main Results:

  • Baseline function, treatment type, and tumor characteristics significantly influenced 12-month functional changes.
  • Models explained 15% (urinary incontinence), 14% (irritative urinary), 19% (bowel), 32% (sexual), and 28% (hormonal) of the variance in the validation cohort.
  • Performance varied across different functional domains, indicating differential predictability.

Conclusions:

  • The first global analysis of functional outcomes post-PCa treatment revealed modest predictive accuracy due to significant regional and practice heterogeneity.
  • These findings highlight limitations of universal predictive models and the importance of large-scale international data for outcome benchmarking.
  • Future research using TNGR data will focus on region-specific models to support personalized patient counseling and quality improvement.