Zhang Xiaosong, Ma Limin
Department of Urology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, People's Republic of China.
Department of Urology, Nantong Tongzhou District People's Hospital, Nantong, 226300, People's Republic of China.
Cancer Manag Res. 2024 May 31;16:537-546. doi: 10.2147/CMAR.S457269. eCollection 2024.
Recurrence is the main factor for poor prognosis of bladder cancer. Therefore, it is necessary to develop new biomarkers to predict the prognosis of bladder cancer. In this study, we used machine learning (ML) methods based on a variety of clinical variables to screen prognostic biomarkers of bladder cancer.
A total of 345 bladder cancer patients were participated in this retrospective study and randomly divided into training and testing group. We used five supervised clustering ML algorithms: decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) to obtained prediction information through 34 clinical parameters.
By comparing five ML algorithms, we found that total bilirubin (TBIL) and CA50 had the best performance in predicting the recurrence of bladder cancer. In addition, the combined predictive performance of the two is superior to the performance of any single indicator prediction.
ML technology can evaluate the recurrence of bladder cancer. This study shows that the combination of TBIL and CA50 can improve the prognosis prediction of bladder cancer recurrence, which can help clinicians make decisions and develop personalized treatment strategies.
复发是膀胱癌预后不良的主要因素。因此，有必要开发新的生物标志物来预测膀胱癌的预后。在本研究中，我们使用基于多种临床变量的机器学习（ML）方法来筛选膀胱癌的预后生物标志物。
共有345例膀胱癌患者参与了这项回顾性研究，并随机分为训练组和测试组。我们使用了五种监督聚类ML算法：决策树（DT）、随机森林（RF）、自适应增强（AdaBoost）、梯度提升机（GBM）和极限梯度提升（XGBoost），通过34个临床参数获得预测信息。
通过比较五种ML算法，我们发现总胆红素（TBIL）和CA50在预测膀胱癌复发方面表现最佳。此外，两者的联合预测性能优于任何单一指标预测的性能。
ML技术可以评估膀胱癌的复发。本研究表明，TBIL和CA50的联合使用可以改善膀胱癌复发的预后预测，这有助于临床医生做出决策并制定个性化治疗策略。
