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Expanding inclusion criteria for active surveillance in intermediate-risk prostate cancer: a machine learning approach

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World Journal of Urology Aims and scope Submit manuscript

Abstract

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.

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Data availability

Data are available upon request to the corresponding author.

Code availability

None.

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Funding

The study was not funded.

Author information

Authors and Affiliations

Authors

Contributions

Study concept and design: BP. Acquisition of data: AB, TR, AU, J-BR, AT, VL, J-BB, RD, GS, OW, DB, AF, GF, CD-L, MR, FS, MO, EB, GF, CD, A-LC, BG-T, CB, EL, JP, AR, RCNVDB, AP. Analysis and interpretation of data: MB, UP. Drafting of the manuscript: MB, GP. Critical revision of the manuscript for important intellectual content: AB, TR, AU, J-BR, AT, VL, J-BB, RD, GS, OW, DB, AF, GF, CD-L, MR, FS, MO, EB, GF, CD, A-LC, BG-T, CB, EL, JP, AR, RC VDB, AP. Statistical analysis: BU.

Corresponding author

Correspondence to Michael Baboudjian.

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Conflict of interest

The authors report no conflict of interest.

Ethical approval

All procedures performed in this study were in approval with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Patient consent was not required due to the study design (retrospective).

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All authors approve the submission.

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Baboudjian, M., Breda, A., Roumeguère, T. et al. Expanding inclusion criteria for active surveillance in intermediate-risk prostate cancer: a machine learning approach. World J Urol 41, 1301–1308 (2023). https://doi.org/10.1007/s00345-023-04353-8

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  • DOI: https://doi.org/10.1007/s00345-023-04353-8

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