Predicting contralateral extraprostatic extension in unilateral high-risk prostate cancer: a multicentric external validation study.

Abstract

Accurate prediction of extraprostatic extension (EPE) is crucial for decision-making in radical prostatectomy (RP), especially in nerve-sparing strategies. Martini et al. introduced a three-tier algorithm for predicting contralateral EPE in unilateral high-risk prostate cancer (PCa). The aim of the study is to externally validate this model in a multicentric European cohort of patients. The data from 208 unilateral high-risk PCa patients diagnosed through magnetic resonance imaging (MRI)-targeted and systematic biopsies, treated with RP between January 2016 and November 2021 at eight referral centers were collected. The evaluation of model performance involved measures such as discrimination (AUC), calibration, and decision-curve analysis (DCA) following TRIPOD guidelines. In addition, a comparison was made with two established multivariable logistic regression models predicting the risk of side specific EPE for assessment purposes. Overall, 38%, 48%, and 14% of patients were categorized as low, intermediate, and high-risk groups according to Martini et al.'s model, respectively. At final pathology, EPE on the contralateral prostatic lobe occurred in 6.3%, 12%, and 34% of patients in the respective risk groups. The algorithm demonstrated acceptable discrimination (AUC 0.68), comparable to other multivariable logistic regression models (p = 0.3), adequate calibration and the highest net benefit in DCA. The limitations include the modest sample size, retrospective design, and lack of central revision. Our findings endorse the algorithm's commendable performance, supporting its utility in guiding treatment decisions for unilateral high-risk PCa patients.