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. 2022 Sep 29;12:942320. doi: 10.3389/fonc.2022.942320

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Copyright © 2022 Li, Liu, Li, Zhao, Wang, Chen, Wang, Sun, Feng, Cai, Wu, Du, Zhang, Zhang and Qu

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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XGBoost model evaluationROC: receiver operating characteristic curve; AUC: area under the curve (A) ROC curve for the 3-year prognostic model of XGBoost algorithm (test data), (B) ROC curve for the 5-year prognostic model of XGBoost algorithm (test data), (C) The ranking of clinical characteristics in terms of importance in the 3-year prognostic model. The x-axis of the graph is the feature importance score. The closer the score is to 1, the more important the feature in predicting the prognosis of the patient in the model. (D) The ranking of clinical characteristics in terms of importance in the 5-year prognostic model. The x-axis of the graph is the feature importance score. The closer the score is to 1, the more important the feature in predicting the prognosis of the patient in the model.