基于机器学习筛选的总胆红素和CA50对膀胱癌患者复发的预测价值

Predictive Value of the Total Bilirubin and CA50 Screened Based on Machine Learning for Recurrence of Bladder Cancer Patients.

作者信息

Zhang Xiaosong, Ma Limin

机构信息

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.

出版信息

Cancer Manag Res. 2024 May 31;16:537-546. doi: 10.2147/CMAR.S457269. eCollection 2024.

DOI:10.2147/CMAR.S457269

PMID:38835478

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11149634/

Abstract

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.

摘要