A Novel Machine Learning-Based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.

Autor: Ordones FV; Tauranga Public Hospital - Tauranga - Bay of Plenty - New Zealand; University of Auckland - Faculty of Medicine and Health Sciences; Urology Departament - UNESP - São Paulo State University - Botucatu - SP - Brazil. Electronic address: fvordones@gmail.com., Kawano PR; Urology Departament - UNESP - São Paulo State University - Botucatu - SP - Brazil., Vermeulen L; Tauranga Public Hospital - Tauranga - Bay of Plenty - New Zealand., Hooshyari A; Tauranga Public Hospital - Tauranga - Bay of Plenty - New Zealand., Scholtz D; Tauranga Public Hospital - Tauranga - Bay of Plenty - New Zealand., Gilling PJ; Tauranga Public Hospital - Tauranga - Bay of Plenty - New Zealand; University of Auckland - Faculty of Medicine and Health Sciences., Foreman D; College of Medicine and Public Health, Flinders University, Bedford Park, South Australia 5042., Kaufmann B; Department of Urology, University Hospital of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland; Department of Urology, Icahn School of Medicine at Mount Sinai New York, 1 Gustave L. Levy Place New York New York 10029, United States., Poyet C; Department of Urology, University Hospital of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland., Gorin M; Department of Urology, Icahn School of Medicine at Mount Sinai New York, 1 Gustave L. Levy Place New York New York 10029, United States., Barbosa AMP; Department of Internal Medicine - UNESP - São Paulo State University - Botucatu - SP - Brazil., da Rocha NC; Department of Internal Medicine - UNESP - São Paulo State University - Botucatu - SP - Brazil., de Andrade LGM; Department of Internal Medicine - UNESP - São Paulo State University - Botucatu - SP - Brazil.
Jazyk: angličtina
Zdroj: Urology [Urology] 2024 Nov 11. Date of Electronic Publication: 2024 Nov 11.
DOI: 10.1016/j.urology.2024.11.001
Abstrakt: Objectives: To create a machine learning predictive model combining PI-RADS score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).
Methods: We evaluated a cohort of patients who underwent prostate biopsy for suspected prostate cancer (PCa) in New Zealand, Australia, and Switzerland. We collected data on age, body mass index (BMI), PSA level, prostate volume, PSA density (PSAD), PI-RADS scores, previous biopsy, and corresponding histology results. The dataset was divided into derivation (training) and validation (test) sets using random splits. An independent dataset was obtained from the Harvard Dataverse for external validation. A cohort of 1272 patients was analyzed. We fitted a Lasso model, XGBoost, and LightGBM to the training set and assessed their accuracy.
Results: All models demonstrated ROC AUC values ranging from 0.830 to 0.851. LightGBM was considered the superior model, with an ROC of 0.851 [95%CI: 0.804 - 0.897] in the test set and 0.818 [95% CI: 0.798 - 0.831] in the external dataset. The most important variable was PI-RADS, followed by PSA density, history of previous biopsy, age, and BMI.
Conclusions: We developed a predictive model for detecting csPCa that exhibited a high ROC-AUC value for internal and external validations. This suggests that the integration of the clinical parameters outperformed each individual predictor. Additionally, the model demonstrated good calibration metrics, indicative of a more balanced model than the existing models.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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