Urology · 2024
A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Vasconcelos Ordones Flavio, Kawano Paulo Roberto, Vermeulen Lodewikus, Hooshyari Ali, Scholtz David, Gilling Peter John, Foreman Darren, Kaufmann Basil, Poyet Cedric, Gorin Michael, Barbosa Abner Macola Pacheco, da Rocha Naila Camila, de Andrade Luis Gustavo Modelli
Аннотация
OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (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.
CONCLUSION: 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.
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.
CONCLUSION: 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.