Yuan Yuyang, Zheng Fuchun, Yao Jiming, Zhou Kun, Yang Jiaqing, Liu Xiaoqiang, Wan Hao, Chen Luyao, Hu Jieping, Zhou Lizhi, Fu Bin
Department of Urology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Jiangxi Institute of Urology, Nanchang, China.
Transl Androl Urol. 2025 Mar 30;14(3):808-819. doi: 10.21037/tau-2024-665. Epub 2025 Mar 26.
Cystitis glandularis (CG) is a rare urological condition characterized by glandular metaplasia of the bladder mucosa. Recurrence following transurethral resection (TUR) is a significant clinical challenge. Traditional predictive models often fail to capture the complexity of the data, resulting in insufficient accuracy. In contrast, machine learning (ML) has demonstrated substantial potential in medical prediction by identifying and analyzing complex patterns that are undetectable by conventional methods. This study aims to develop and evaluate an interpretable ML model to predict recurrence after TUR for CG, thereby improving clinical decision-making and patient outcomes.
We analyzed predictors of recurrence using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. We developed and tested seven ML-based models: Cox proportional hazards model (CoxPH), LASSO regression, decision tree (rpart), random survival forest (RSF), gradient boosting machine (GBM), support vector machine (SVM), and extreme gradient boosting (XGBoost). Participants were diagnosed with CG by pathology following TUR and treated from 2012 to 2018. Model discrimination was assessed using the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), while model preference was evaluated through the Brier score (BS). Decision curve analysis (DCA) was used for model comparison. The SHapley Additive exPlanations (SHAP) method was employed for interpretation, providing insights into recurrence prediction and prevention strategies. Finally, user-friendly platform was developed, allowing users to predict CG recurrence by entering feature values into designated text boxes on the webpage.
The RSF model demonstrated the best performance in predicting recurrence, as indicated by superior ROC, DCA, and BS metrics. In SHAP, postoperative regular instillation (PRI) contributed the most to model construction.
The RSF model effectively predicts CG recurrence, offering a framework for individualized treatment strategies. PRI was identified as the most significant risk factor influencing recurrence.
腺性膀胱炎（CG）是一种罕见的泌尿系统疾病，其特征为膀胱黏膜的腺性化生。经尿道切除术（TUR）后复发是一项重大的临床挑战。传统的预测模型往往无法捕捉数据的复杂性，导致准确性不足。相比之下，机器学习（ML）通过识别和分析传统方法无法检测到的复杂模式，在医学预测中显示出巨大潜力。本研究旨在开发和评估一种可解释的ML模型，以预测CG患者TUR后的复发情况，从而改善临床决策和患者预后。
我们使用最小绝对收缩和选择算子（LASSO）和多变量逻辑回归分析复发的预测因素。我们开发并测试了七种基于ML的模型：Cox比例风险模型（CoxPH）、LASSO回归、决策树（rpart）、随机生存森林（RSF）、梯度提升机（GBM）、支持向量机（SVM）和极端梯度提升（XGBoost）。参与者在2012年至2018年期间接受TUR后经病理诊断为CG并接受治疗。使用受试者工作特征（ROC）曲线和ROC曲线下面积（AUC）评估模型的辨别力，同时通过Brier评分（BS）评估模型偏好。决策曲线分析（DCA）用于模型比较。采用SHapley加性解释（SHAP）方法进行解释，以深入了解复发预测和预防策略。最后，开发了用户友好的平台，允许用户通过在网页上指定的文本框中输入特征值来预测CG复发。
RSF模型在预测复发方面表现最佳，其ROC、DCA和BS指标均更优。在SHAP分析中，术后定期灌注（PRI）对模型构建的贡献最大。
RSF模型可有效预测CG复发，为个体化治疗策略提供了框架。PRI被确定为影响复发的最显著危险因素。
