Mp31-02 Machine Learning Outperform Traditional Approaches in Predicting Clinically Significant Prostate Cancer

You have accessJournal of UrologyProstate Cancer: Detection XGBoost - accuracy 0.74 and ROC-AUC 0.80 95% CI: 0.69-0.90; LightGBM - accuracy 0.75 and ROC-AUC 0.83 95% CI: 0.72-0.91. Although ROC-, LightGBM had superior discriminant and calibration metrics and was selected as the final model. The best predictors identified were the size of the MRI index lesion size, PSA density, age, and PI-RADS Score. When classifying CSPCa, using PIRADS alone and PSA density resulted in ROC-AUC values of 0.79 95% CI: 0.69-0.89 and 0.65 95% CI: 0.52-0.78, respectively. CONCLUSIONS: Combining clinical parameters with PiRads (Score and lesion size) and PSAd significantly improves the performance of machine learning models in predicting patients with CSPCa. Our model achieved higher ROC-AUC values and demonstrated excellent calibration, indicating its superior predictive ability. Source of Funding: Nil © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e503 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Flavio Vasconcelos Ordones More articles by this author Lodewikus Vermeulen More articles by this author Ali Hooshyari More articles by this author David Scholtz More articles by this author Paulo Kawano More articles by this author Gustavo Modelli de Andrade More articles by this author Abner Barros More articles by this author Peter Gilling More articles by this author Expand All Advertisement PDF downloadLoading ...