Efficacy of surgery combined with radiotherapy for upper urinary tract urothelial carcinoma in the elderly and survival prediction based on machine learning
Objective To explore the effects and survival prediction of surgery combined with radi-ation therapy in elderly patients with upper urinary tract urothelial carcinoma,using machine learning ap-proaches.Methods Data were collected from the surveillance,epidemiology,and end results(SEER)database.Propensity score matching was utilized to balance information between groups.Univariate and multivariate Cox regression analyses compared cancer specific survival(CSS)and overall survival(OS)be-tween patients who did and did not receive radiation therapy.A total of 6 machine learning models were de-veloped,with extreme gradient boosting(XGBoost)identified as providing the best performance in predic-ting 5-year CSS for patients post-radiation therapy.Results Univariate and multivariate analyses showed decreased tumor-specific survival for patients not receiving radiation therapy[95％confidence interval(CI):1.151-1.618],with factors such as age over 80(95％CI:1.052-1.278),being single(95％CI:1.134-2.321),divorce(95％CI:1.255-1.877),T3-4 stage(95％CI:1.572-2.382),N2-3 stage(95％CI:1.162-1.987),not receiving chemotherapy(95％CI:1.108-1.608),invasive urothelial carci-noma(95％CI:1.082-2.185),and N1 stage(95％CI:1.229-2.282).The XGBoost model outper-formed 5 other machine learning models in predicting 5-year CSS,with an accuracy of 0.922,precision of 0.923,sensitivity of 0.931,Fl score of 0.901,and area under curve of 0.901.Conclusion By screen-ing factors related to tumor-specific survival,the best machine learning prediction model for postoperative radiotherapy survival is further selected.
NETL
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万方数据
