ArticleWorld journal of urology2023
Expanding inclusion criteria for active surveillance in intermediate-risk prostate cancer: a machine learning approach.
Article in World journal of urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Focal radiation therapies for localized prostate cancer: a systematic review of clinical evidence and outcomes.Prostate cancer and prostatic diseases · 2026Pooled it
- Multicenter Validation of a Risk Classification Cluster for Unfavorable Pathology in Prostatectomy Specimens of Patients with an Active Surveillance Expanded Inclusion Criteria.Diagnostics (Basel, Switzerland) · 2026Article
- Radical prostatectomy outcomes in patients with PI-RADS 5 lesions and favorable prostate cancer.World journal of urology · 2025Article
- Standardizing Prostate MRI Reporting in Active Surveillance for Prostate Cancer: The PRECISE Framework.Journal of the Korean Society of Radiology · 2025Review
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Authors and funding
30 authors at 17 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop new selection criteria for active surveillance (AS) in intermediate-risk (IR) prostate cancer (PCa) patients.
methodsRetrospective 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).
resultsA 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.
conclusionsThe newly developed model has the potential to expand the number of patients eligible for AS without compromising oncologic outcomes. Prospective validation is warranted.
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