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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.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Lophatananon, Artitaya 
Keates, Alexandra 
Thankappannair, Vineetha 
Barrett, Tristan 

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.

Description

Keywords

Cambridge Prognostic Groups (CPG), MRI, PSA, active surveillance, biopsy, non-metastatic disease, prostate cancer, risk model, risk prediction

Journal Title

J Clin Med

Conference Name

Journal ISSN

2077-0383
2077-0383

Volume Title

12

Publisher

MDPI AG