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. 2024 May 31:16:537-546.
doi: 10.2147/CMAR.S457269. eCollection 2024.

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

Xiaosong Zhang  1   2 , Limin Ma  1
Affiliations

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

Xiaosong Zhang et al. Cancer Manag Res. .

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.

Keywords: biomarkers; bladder cancer; machine learning; recurrence; retrospective study.

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Conflict of interest statement

All of the authors declare that they have no conflicts of interest for this work.

Figures

Figure 1
Figure 1
The schematic diagram of the overall workflow.
Figure 2
Figure 2
Correlation between variables. The gradient from green to red represents a gradient from positive to negative correlation.
Figure 3
Figure 3
Variable importance of features included in five ML algorithms for predicting the recurrence of bladder cancer. (A) DT. (B) RF. (C)AdaBoost. (D) GBM. (E) XGBoost.
Figure 4
Figure 4
ROC curve analysis. (A) CA50 predicts the AUC value of recurrence. (B) TBIL predicts the AUC value of recurrence. (C) CA50 and TBIL predicts the AUC value of recurrence.
See this image and copyright information in PMC

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