Machine learning-based short-term DFS-associated characteristic factor screening and model construction for patients with gallbladder cancer after radical surgery.

Abstract

Gallbladder cancer (GBC) is a malignancy with a bleak prognosis, and radical surgery remains the primary treatment option. However, the high postoperative recurrence rate and the lack of individualized risk assessment tools limit the effectiveness of current treatment strategies. This study aims to identify risk factors affecting the short-term disease-free survival (DFS) of GBC patients using machine learning methods and to build a prediction model. A retrospective analysis was conducted on the clinical data from 328 GBC patients treated at the First Affiliated Hospital of Huzhou University from 2008 to 2021. Patients were randomly divided into a training set (n=230) and a validation set (n=98). Clinical data, laboratory indexes, and follow-up data were collected. Univariate Cox regression analysis identified age, tumor T-staging, lymph node metastasis, differentiation degree, and CA199 level as prognostic factors affecting DFS (all P<0.05). A prediction model constructed using the LASSO regression achieved AUCs of 0.827 and 0.801 for predicting 1-year and 3-year DFS, respectively. Notably, the XGBoost regression model showed higher prediction accuracy with AUCs of 0.922 and 0.947, respectively. The Delong test confirmed that the XGBoost model had significantly higher AUC values compared to the LASSO model (all P<0.001). In the validation set, the XGBoost model demonstrated AUCs of 0.764 and 0.761 for predicting 1-year and 3-year DFS, respectively. Overall, the XGBoost regression model demonstrates high accuracy and clinical value in predicting short-term DFS in GBC patients after radical surgery, offering a valuable tool for personalized treatment.