Development and validation of an interpretable machine learning model for predicting Gleason score upgrade in prostate cancer.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
추출되지 않음
I · Intervention 중재 / 시술
radical prostatectomy (RP) at Shanghai General Hospital and West China Hospital
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[RESULTS] The LightGBM model demonstrated strong predictive performance, achieving an area under the ROC curve of 84.53% in the test set and 76.61% in external validation. Significant factors associated with GSU included the International Society of Urological Pathology (ISUP) grade, age, clinical tumor stage (T stage…
[BACKGROUND] The high incidence of Gleason score upgrade (GSU) can lead urologists to underestimate tumor aggressiveness, resulting in suboptimal treatment decisions.
APA
Li SF, Zhao JG, et al. (2025). Development and validation of an interpretable machine learning model for predicting Gleason score upgrade in prostate cancer.. Translational andrology and urology, 14(6), 1631-1644. https://doi.org/10.21037/tau-2025-178
MLA
Li SF, et al.. "Development and validation of an interpretable machine learning model for predicting Gleason score upgrade in prostate cancer.." Translational andrology and urology, vol. 14, no. 6, 2025, pp. 1631-1644.
PMID
40687645 ↗
Abstract 한글 요약
[BACKGROUND] The high incidence of Gleason score upgrade (GSU) can lead urologists to underestimate tumor aggressiveness, resulting in suboptimal treatment decisions. This study aimed to develop an interpretable machine learning model to predict the risk of GSU in individuals with prostate cancer (PCa) based on readily available clinical parameters.
[METHODS] A retrospective analysis was conducted on patients who underwent radical prostatectomy (RP) at Shanghai General Hospital and West China Hospital. Data from Shanghai General Hospital were categorized into a training set (80%) and a test set (20%), while data from West China Hospital were used for external validation. Preoperative clinical and pathological data were collected. Nine machine learning models [including random forest (RF) and light gradient boosting machine (LightGBM)], were developed, and the model demonstrating the best predictive performance was selected as the final model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curves, and SHapley Additive exPlanations (SHAP) interpretation.
[RESULTS] The LightGBM model demonstrated strong predictive performance, achieving an area under the ROC curve of 84.53% in the test set and 76.61% in external validation. Significant factors associated with GSU included the International Society of Urological Pathology (ISUP) grade, age, clinical tumor stage (T stage), body mass index, prostate-specific antigen (PSA), free-to-total PSA ratio (f/t PSA), platelet-to-lymphocyte ratio (PLR), and bilateral tumor involvement. An online prediction tool was developed based on this model.
[CONCLUSIONS] A machine learning model and an online prediction tool were developed to accurately predict GSU and identify factors associated with this process. This approach may assist clinicians in identifying individuals at high-risk for GSU and facilitating evidence-based treatment decisions.
[METHODS] A retrospective analysis was conducted on patients who underwent radical prostatectomy (RP) at Shanghai General Hospital and West China Hospital. Data from Shanghai General Hospital were categorized into a training set (80%) and a test set (20%), while data from West China Hospital were used for external validation. Preoperative clinical and pathological data were collected. Nine machine learning models [including random forest (RF) and light gradient boosting machine (LightGBM)], were developed, and the model demonstrating the best predictive performance was selected as the final model. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curves, and SHapley Additive exPlanations (SHAP) interpretation.
[RESULTS] The LightGBM model demonstrated strong predictive performance, achieving an area under the ROC curve of 84.53% in the test set and 76.61% in external validation. Significant factors associated with GSU included the International Society of Urological Pathology (ISUP) grade, age, clinical tumor stage (T stage), body mass index, prostate-specific antigen (PSA), free-to-total PSA ratio (f/t PSA), platelet-to-lymphocyte ratio (PLR), and bilateral tumor involvement. An online prediction tool was developed based on this model.
[CONCLUSIONS] A machine learning model and an online prediction tool were developed to accurately predict GSU and identify factors associated with this process. This approach may assist clinicians in identifying individuals at high-risk for GSU and facilitating evidence-based treatment decisions.
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