Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator

作者：Dengke Li, Baoyuan Chang, Qunlian Huang · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-85963-7 · 被引用次数：9 · 研究领域：Prostate Cancer Diagnosis and Treatment、Prostate Cancer Treatment and Research、Urologic and reproductive health conditions

To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels < 20 ng/ml to minimize prostate biopsy-related discomfort and risks. Data from 655 patients who underwent transperineal prostate biopsy at the First Affiliated Hospital of Wannan Medical College from July 2021 to January 2023 were collected and analyzed. After applying the Synthetic Minority Over-sampling TEchnique class balancing on the training set, multiple machine learning models were constructed by using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection to identify the significant variables. The best-performing model was selected and evaluated through tenfold cross-validation to ensure interpretability. Finally, the performance was assessed using the test set data for validation. The age, prostate-specific antigen mass ratio (PSAMR), Prostate Imaging-Reporting and Data System, and prostate volume were selected as the variables for model construction based on the LASSO regression. The receiver operating characteristic (ROC) results for multiple models in the validation set were as follows: XGBoost: 0.93 (0.88-0.97); logistic: 0.89 (0.83-0.95); LightGBM: 0.87 (0.80-0.93); AdaBoost: 0.90 (0.85-0.96); GNB: 0.88 (0.82-0.95); CNB: 0.79 (0.71-0.87); MLP: 0.78 (0.69-0.86); and Support Vector Machine: 0.81 (0.73-0.89). XGBoost was selected as the best model and reconstructed with tenfold cross-validation on the training data, resulting in the following RO...