Development and External Validation of the STRATified CANcer Surveillance (STRATCANS) Multivariable Model for Predicting Progression in Men with Newly Diagnosed Prostate Cancer Starting Active Surveillance

Author:

Light AlexanderORCID,Lophatananon Artitaya,Keates Alexandra,Thankappannair Vineetha,Barrett Tristan,Dominguez-Escrig Jose,Rubio-Briones Jose,Benheddi Toufik,Olivier Jonathan,Villers Arnauld,Babureddy Kirthana,Abdelmoteleb Haitham,Gnanapragasam Vincent J.

Abstract

For men with newly diagnosed prostate cancer, we aimed to develop and validate a model to predict the risk of progression on active surveillance (AS), which could inform more personalised AS strategies. In total, 883 men from 3 European centres were used for model development and internal validation, and 151 men from a fourth European centre were used for external validation. Men with Cambridge Prognostic Group (CPG) 1–2 disease at diagnosis were eligible. The endpoint was progression to the composite endpoint of CPG3 disease or worse (≥CPG3). Model performance at 4 years was evaluated through discrimination (C-index), calibration plots, and decision curve analysis. The final multivariable model incorporated prostate-specific antigen (PSA), Grade Group, magnetic resonance imaging (MRI) score (Prostate Imaging Reporting & Data System (PI-RADS) or Likert), and prostate volume. Calibration and discrimination were good in both internal validation (C-index 0.742, 95% CI 0.694–0.793) and external validation (C-index 0.845, 95% CI 0.712–0.958). In decision curve analysis, the model offered net benefit compared to a ‘follow-all’ strategy at risk thresholds of ≥0.08 and ≥0.04 in development and external validation, respectively. In conclusion, our model demonstrated good accuracy and clinical utility in predicting the progression on AS at 4 years post-diagnosis. Men with lower risk predictions could subsequently be offered less-intense surveillance. Further external validation in larger cohorts is now required.

Publisher

MDPI AG

Subject

General Medicine

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