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Personalized Dynamic Prediction Model for Biopsy Timing in Patients With Prostate Cancer During Active Surveillance.

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Summary

A new dynamic model predicts prostate cancer (PC) reclassification risk during active surveillance (AS), potentially reducing unnecessary biopsies. This tool aids personalized care for PC patients on AS.

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Area of Science:

  • Oncology
  • Urology
  • Medical Informatics

Background:

  • Active surveillance (AS) for prostate cancer (PC) relies on fixed biopsy schedules, increasing risks of missing disease progression or unnecessary invasive procedures.
  • A personalized approach is crucial to balance biopsy burden with the risk of reclassification to significant disease.

Purpose of the Study:

  • To develop and externally validate a dynamic prediction model for PC reclassification risk during AS.
  • To provide a tool for personalized, risk-based AS strategies.

Main Methods:

  • A joint model for longitudinal and time-to-event data was employed using data from the Prostate Cancer Research International: Active Surveillance (PRIAS) study and external validation cohorts.
  • Predictors included baseline and repeated clinical characteristics, PSA levels, MRI findings, and biopsy results.
  • Model performance was assessed using time-dependent area under the receiver operating characteristic curve (AUC) and negative predictive value (NPV).

Main Results:

  • The dynamic model identified key risk factors for reclassification, including age, PSA velocity, prostate volume, and MRI findings.
  • The model demonstrated strong performance, with NPVs ranging from 86% to 97% and time-dependent AUCs between 0.81-0.84 in the development cohort and 0.52-0.90 in external validation.
  • External validation confirmed the model's generalizability across diverse patient cohorts.

Conclusions:

  • The developed dynamic risk model effectively identifies patients with a low risk of PC reclassification during AS.
  • This validated model has the potential to support personalized, risk-based AS protocols.
  • Implementation of this model may significantly reduce the need for unnecessary prostate biopsies, improving patient management and reducing healthcare burdens.