Li Shu-Feng, Zhao Jin-Ge, Jiang Chen-Yi, Wang Shi-Yuan, Liu Si-Yu, Zhang Yi-Jun, Zeng Hao, Zhao Fu-Jun
Department of Urology, Shanghai General Hospital, Shanghai, China.
Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Transl Androl Urol. 2025 Jun 30;14(6):1631-1644. doi: 10.21037/tau-2025-178. Epub 2025 Jun 26.
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.
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.
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.
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.
Gleason评分升级（GSU）的高发生率可能导致泌尿外科医生低估肿瘤侵袭性，从而做出次优的治疗决策。本研究旨在基于易于获得的临床参数，开发一种可解释的机器学习模型，以预测前列腺癌（PCa）患者发生GSU的风险。
对在上海交通大学医学院附属仁济医院和四川大学华西医院接受根治性前列腺切除术（RP）的患者进行回顾性分析。仁济医院的数据分为训练集（80%）和测试集（20%），华西医院的数据用于外部验证。收集术前临床和病理数据。开发了9种机器学习模型[包括随机森林（RF）和轻梯度提升机（LightGBM）]，并选择表现最佳的模型作为最终模型。使用受试者工作特征（ROC）曲线、校准曲线、决策曲线和SHapley值加法解释（SHAP）解释来评估模型性能。
LightGBM模型表现出强大的预测性能，在测试集中ROC曲线下面积为84.53%，在外部验证中为76.61%。与GSU相关的显著因素包括国际泌尿病理学会（ISUP）分级、年龄、临床肿瘤分期（T分期）、体重指数、前列腺特异性抗原（PSA）、游离PSA与总PSA比值（f/t PSA）、血小板与淋巴细胞比值（PLR）以及双侧肿瘤累及情况。基于该模型开发了一个在线预测工具。
开发了一种机器学习模型和一个在线预测工具，以准确预测GSU并识别与此过程相关的因素。这种方法可能有助于临床医生识别GSU高危个体，并促进基于证据的治疗决策。
