Xiaosong Zhang,1,2 Limin Ma1 

1Department of Urology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226001, People’s Republic of China; 2Department of Urology, Nantong Tongzhou District People’s Hospital, Nantong, 226300, People’s Republic of China

Correspondence: Xiaosong Zhang; Limin Ma, Email 13951314220@163.com; Email ntmalimin@163.com

Purpose: 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.
Patients and Methods: 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.
Results: 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.
Conclusion: 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.

Keywords: bladder cancer, recurrence, machine learning, biomarkers, retrospective study