In World journal of urology ; h5-index 40.0
PURPOSE : To develop new selection criteria for active surveillance (AS) in intermediate-risk (IR) prostate cancer (PCa) patients.
METHODS : Retrospective study including patients from 14 referral centers who underwent pre-biopsy mpMRI, image-guided biopsies and radical prostatectomy. The cohort included biopsy-naive IR PCa patients who met the following inclusion criteria: Gleason Grade Group (GGG) 1-2, PSA < 20 ng/mL, and cT1-cT2 tumors. We relied on a recursive machine learning partitioning algorithm developed to predict adverse pathological features (i.e., ≥ pT3a and/or pN + and/or GGG ≥ 3).
RESULTS : A total of 594 patients with IR PCa were included, of whom 220 (37%) had adverse features. PI-RADS score (weight:0.726), PSA density (weight:0.158), and clinical T stage (weight:0.116) were selected as the most informative risk factors to classify patients according to their risk of adverse features, leading to the creation of five risk clusters. The adverse feature rates for cluster #1 (PI-RADS ≤ 3 and PSA density < 0.15), cluster #2 (PI-RADS 4 and PSA density < 0.15), cluster #3 (PI-RADS 1-4 and PSA density ≥ 0.15), cluster #4 (normal DRE and PI-RADS 5), and cluster #5 (abnormal DRE and PI-RADS 5) were 11.8, 27.9, 37.3, 42.7, and 65.1%, respectively. Compared with the current inclusion criteria, extending the AS criteria to clusters #1 + #2 or #1 + #2 + #3 would increase the number of eligible patients (+ 60 and + 253%, respectively) without increasing the risk of adverse pathological features.
CONCLUSIONS : The newly developed model has the potential to expand the number of patients eligible for AS without compromising oncologic outcomes. Prospective validation is warranted.
Baboudjian Michael, Breda Alberto, Roumeguère Thierry, Uleri Alessandro, Roche Jean-Baptiste, Touzani Alae, Lacetera Vito, Beauval Jean-Baptiste, Diamand Romain, Simone Guiseppe, Windisch Olivier, Benamran Daniel, Fourcade Alexandre, Fiard Gaelle, Durand-Labrunie Camille, Roumiguié Mathieu, Sanguedolce Francesco, Oderda Marco, Barret Eric, Fromont Gaëlle, Dariane Charles, Charvet Anne-Laure, Gondran-Tellier Bastien, Bastide Cyrille, Lechevallier Eric, Palou Joan, Ruffion Alain, Van Der Bergh Roderick C N, Peltier Alexandre, Ploussard Guillaume
2023-Mar-15
Active surveillance, Intermediate risk, Machine learning, Oncological outcomes, Prostate cancer
