Establishment and validation of a predictive nomogram model for non-small cell lung cancer patients with chronic hepatitis B viral infection

Shulin Chen, Yanzhen Lai, Zhengqiang He, Jianpei Li, Xia He, Rui Shen, Qiuying Ding, Hao Chen, Songguo Peng, Wanli Liu

This study aimed to establish an effective predictive nomogram for non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. The nomogram was based on a retrospective study of 230 NSCLC patients with chronic HBV infection. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (C-index), calibration plot and decision curve analysis and were compared with the current tumor, node, and metastasis (TNM) staging system. Independent factors derived from Kaplan-Meier analysis of the primary cohort to predict overall survival (OS) were all assembled into a Cox proportional hazards regression model to build the nomogram model. The final model included age, tumor size, TNM stage, treatment, apolipoprotein A-I, apolipoprotein B, glutamyl transpeptidase and lactate dehydrogenase. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with the actual observations. The C-index of the model for predicting OS had a superior discrimination power compared with the TNM staging system [0.780 (95% CI 0.733-0.827) vs. 0.693 (95% CI 0.640-0.746), P < 0.01], and the decision curve analyses showed that the nomogram model had a higher overall net benefit than did the TNM stage. Based on the total prognostic scores (TPS) of the nomogram, we further subdivided the study cohort into three groups: low risk (TPS ≤ 13.5), intermediate risk (13.5 < TPS ≤ 20.0) and high risk (TPS > 20.0). The proposed nomogram model resulted in more accurate prognostic prediction for NSCLC patients with chronic HBV infection.