XGBoost model in predicting recurrence of patients with laparoscopic hepatectomy for hepatocellular carcinoma
10.3760/cma.j.cn115396-20210123-00029
- VernacularTitle:XGBoost模型对腹腔镜肝切除术治疗肝细胞癌复发的预测因素分析
- Author:
Kai CHEN
;
Zhuqing ZHANG
;
Tao MA
;
Xuejun ZHANG
;
Aijun YU
;
Jinlong LIU
;
Jian LI
;
Hua FU
- From:
International Journal of Surgery
2021;48(4):247-254,F4
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study aimed to establish an eXtreme Gradient Boosting(XGBoost) model that can predict the recurrence of hepatocellular carcinoma(HCC)patients after laparoscopic hepatectomy (LH) surgery.Methods:A total of 440 patients with primary HCC who received LH treatment for the first time from January 2013 to September 2016 in Affiliated Hospital of Chengde Medical University were selected as the research objects. The diagnosis method was pathological diagnosis. Research objects were divided into training group ( n=88) and verification group ( n=352) at a ratio of 2∶8 by random number table method. The Kaplan-Meier method was used to draw the recurrence-free survival curve, and the Log-rank test was used to compare the survival of the two groups; the training group was used to establish the COX regression model and the XGBoost model to screen independent predictors of recurrence after LH; receiver operating characteristic(ROC) curve was used to analyze the predictive abilities of the two models, and conducted internal verification in the verification group; Hosmer and Lemeshow Test was used to evaluate the calibration of the two models, and P>0.05 was used as a good fit between the model and the actual situation. Results:Both the COX regression model and the XGBoost model screened out tumor thrombus, low degree of differentiation, tumor microvascular infiltration (MVI), number of tumors, large tumors, and positive hepatitis B surface antigen were independent predictors of tumor recurrence( HR=2.477, 0.769, 1.786, 1.905, 1.544, 1.805; 95% CI: 1.465-4.251, 0.619-0.819, 1.263-2.546, 1.354-2.704, 1.272-1.816, 1.055-2.555). The XGboost model scores were 32 points, 29 points, 24 points, 18 points, 16 points, 11 points, respectively. In the training group, the area under the curve (AUC) of the COX regression model and XGBoost model for predicting recurrence were 0.746 (0.730-0.762) and 0.802 (0.785-0.818), respectively. The XGBoost model had strong predictive ability and was confirmed in the validation cohort. Conclusions:This study had established and verified the XGBoost model that can predict the recurrence of HCC patients after receiving LH for the first time. It can be used in clinics to assist doctors in formulating personalized postoperative monitoring programs for patients. Early detection, early diagnosis and early treatment of tumors and strengthening of postoperative follow-up are important measures to improve the prognosis of patients.
