Abstract
Chronic inflammation plays an important role in tumor progression. The aim of this study was to develop an effective predictive dynamic nomogram integrated with inflammation-based factors to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. We retrospectively analyzed NSCLC patients with HBV infection from Sun Yat-sen University Cancer Center between 2008 and 2010. Univariate and multivariate Cox survival analyses were performed to identify prognostic factors associated with OS of patients. All of the independent prognostic factors were utilized to build the dynamic nomogram. The predictive accuracy of the dynamic nomogram was evaluated concordance index (C-index), decision curve analysis and were compared with previous reported model and traditional TNM staging system. According to the total points (TPS) by dynamic nomogram, we further stratified patients into different risk groups. A total of 203 patients were included. Multivariate Cox analysis showed TNM stage (P = 0.019), treatment (P < 0.001), C-reactive protein (P = 0.020) and platelet (P = 0.012) were independent prognostic factors of OS. The dynamic nomogram was established by involving all the factors above. The C-index of dynamic nomogram for predicting OS was 0.76 (95%CI: 0.72-0.80), which was statistically higher than that of traditional TNM staging system (0.70, 95%CI: 0.66-0.74, P<0.001). Decision curve analysis demonstrated that the dynamic nomogram was better than the TNM staging system. The predictive accuracy of the current model keeping almost the same accuracy as previous one. Based on the total points (TPS) of dynamic nomogram, we divided the patients into 3 subgroups: low risk (TPS ≤ 107), intermediate risk (107< TPS ≤ 149), and high risk (TPS > 149). The differences of OS rates were significant in the subgroups. We propose a novel dynamic nomogram model based on inflammatory prognostic factors that is highly predictive of OS in NSCLC patients with HBV infection and outperforms the traditional TNM staging system.