External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification

期刊论文
发表日期：

2024年6月12日

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Romain Diamand,
Karsten Guenzel,
Teddy Jabbour,
Arthur Baudewyns,
Henri-Alexandre Bourgeno,
Yolène Lefebvre,
Mariaconsiglia Ferriero,
Giuseppe Simone,
Alexandre Fourcade,
Georges Fournier,
Alexandre Patrick Bui,
Fayek Taha,
Marco Oderda,
Paolo Gontero,
Katerina Rysankova,
Adrian Bernal-Gomez,
Alessandro Mastrorosa,
Jean-Baptiste Roche,
Gaelle Fiard,
Rawad Abou Zahr,
Guillaume Ploussard,
Olivier Windisch,
Quentin Novello,
Daniel Benamran,
Gina Delavar,
Julien Anract,
Nicolas Barry Delongchamps,
Adam Halinski,
Charles Dariane,
Léonidas Vlahopoulos,
Gregoire Assenmacher,
Thierry Roumeguère,
Alexandre Peltier

摘要

Purpose

Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa).

Methods

A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups.

Results

Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models’ performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version.

Conclusions

We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.