Construction and evaluation of machine learning models for postoperative prognosis prediction of biliary tract
Objective:
This study aimed to construct a new machine learning model based on multiple risk factors to predict the prognosis of patients with biliary tract malignancies after surgery.
Materials And Methods:
This study included 6,951 patients in the SEER database from 2004 to 2015, and 245 patients in our medical center from 2015 to 2022. The patients in the SEER database were divided into a training set and a test set in a 7:3 ratio. The data from this research center serves as the external validation set. The clinical outcome was overall survival (OS). Univariate and multivariate Cox regression analyses were used in the training set to screen out independent prognostic factors. Then, multiple machine learning models were constructed to predict the survival rates of patients at 1 year, 3 years and 5 years. The performance of the model was evaluated using the area under the curve (AUC), calibration curves, and the decision curve analysis (DCA). SHAP analysis was used to identify features with greater contributions to model prediction. Subgroup analysis and survival curves were used to evaluate the benefits of chemotherapy for patients with positive lymph nodes.
Results:
Age, sex, marital status, tumor site, tumor differentiation, AJCC stage, regional nodes examined, regional nodes positive, and LN surgery Scope were all independent risk factors affecting the OS of patients with biliary tract malignancies. Among the various models constructed, the random forest (RF) model showed good AUC values across different datasets. Moreover, the calibration curves and decision curve analysis of the RF model in different sets indicate that it has excellent performance. SHAP analysis indicates regional nodes positive, AJCC stage, tumor site, and age play a crucial role in the survival analysis of biliary tract malignancies. Chemotherapy was associated with improved OS among patients with positive lymph nodes in the training and test cohorts.
Conclusions:
This study developed a new machine learning model based on multiple risk factors, which can effectively predict the prognosis of patients with biliary tract malignancies after surgery. This model may assist postoperative risk stratification and individualized adjuvant treatment decision-making.
