A Multi-parametric Prognostic Model Based on Clinical Features and Serological Markers Predicts Overall Survival in Non-small Cell Lung Cancer Patients With Chronic Hepatitis B Viral Infection

Abstract Background To develop and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. Methods The prognostic model was generated by using Lasso regression in training cohort. The incremental predictive value of the model to traditional TNM staging and clinical treatment for individualized survival was evaluated by concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). A model risk score nomogram for OS was built by combining TNM staging and clinical treatment. Then we stratified patients into high and low risk subgroups according to the model risk score. Difference in survival between subgroups was analyzed using Kaplan–Meier survival analysis. Furthermore, correlations between the prognostic model and TNM staging or treatment were analysed. Results The C-index values of the model for predicting OS were 0.769 and 0.676 in the training and validation cohorts, respectively, which were higher than that of TNM staging, and treatment, the tdROC curve and DCA also showed the model had good predictive accuracy and discriminatory power than TNM staging and treatment. And the nomogram shown some clinical net benefit. According to the model risk score, we divided the patients into low risk and high risk subgroups. The differences of OS rates were significant in the subgroups. Furthermore, the model was positive correlation with TNM staging and treatment. Conclusions The proposed prognostic model showed favorable performance than traditional TNM staging and clinical treatment for estimating OS in NSCLC (HBV+) patients.