DISCUSSION
cancer, tumor, prostate cancer
TUMOR, CANCER, DISEASE, REGRESSION, PROSTATE CANCER
In this study, we constructed four predictive models, including three models using ML algorithms for the prediction of the 5‐year survival status among nonmetastatic CC patients by combining seven routine clinical and treatment features selected by the LASSO regression model. Furthermore, although four predictive models performed in a nearly similar manner, Delong's comparison and DCAs revealed that the XGBoost algorithm had the best prediction ability compared with the other three models. Finally, the feature importance analysis of the XGBoost model indicated that the tumor stage based on the 2014 FIGO system of staging was the most important variable by combining big data with freely available demographic and clinicopathological characteristics in the SEER database.The current clinic study did not set as a primary objective to describe in depth the differences between the algorithms within the four classifiers. In terms of the benefits of ML algorithms in cancer management, a similar application using ML algorithms as classifiers for the prediction of specific clinical endpoints was reported in recent years, which is mainly because some ML algorithms like XGBoost are gradually being added to clinical implementations. Wei and his colleagues evaluated four predictive models including LR, SVM, DT, and XGBoost for predicting lymph node metastasis (LNM) in prostate cancer using data gathered from the SEER database from 2010 to 2015. The XGBoost model showed the best predictive performance and the highest net benefit compared with the other three algorithms, with an AUC value of 0.883. The DCA plot also indicated that the XGBoost algorithm had the highest net benefit across the entire range of threshold probabilities compared with the other three predictive models.On the other hand, the variable importance analysis of the XGBoost algorithm revealed that tumor stage was the most important feature for predicting 5‐year survival status among patients with CC. Based on our previous comparison of the prognostic impact of FIGO 2014 and 2018 staging systems for Stage IIB–IVA of CC, no significant difference in survival risk classification was observed for the updated 2018 FIGO staging system over the 2014 FIGO staging system.The ML algorithms have been proven to provide more accurate disease classification and prediction of survival for CC patients, leading to a series of studies investigating how they can work with more clinical data such as radiomics