BackgroundThis study aimed to establish an effective predictive nomogram for non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection.MethodsThe 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.ResultsIndependent 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).ConclusionThe proposed nomogram model resulted in more accurate prognostic prediction for NSCLC patients with chronic HBV infection.

Background Despite recent advances in the treatments of hepatocellular carcinoma (HCC), the prognosis of HCC patients remains controversial. The purpose of this study was to investigate the prognostic performance of pretreatment albumin to C-reactive protein ratio (ACR) in patients with HCC. Methods This study included 409 initially diagnosed HCC patients retrospectively. The optimal cut-off points for distinguishing high and low ACR value was determined by the X-tile software. The chi-squared test was used for comparing the baseline clinicopathologic parameters in different groups and subgroups. The Cox regression with log-rank tests was used to analyze OS and DFS, and Kaplan-Meier curves was used to estimate the prognosis of HCC patients. Results Patients with lower ACR were significantly correlated with advanced clinical parameters, using a cut-off points of 5.4 (high ACR, n = 236 vs. low ACR, n = 173). Multivariate analysis demonstrated that ACR was associated with OS (HR = 0.544, 95% CI: 0.385–0.769, p = 0.001), with DFS (HR = 0.550, 95% CI: 0.392–0.772, p = 0.001). Treatment exposure (HR = 2.191; 95% CI: 1.533–3.132; p <  0.001), tumor size (HR = 1.973; 95% CI: 1.230–3.164; p = 0.005), serum AFP level (HR = 1.752; 95% CI: 1.277–2.403; p = 0.001), and TNM stage (HR = 0.470; 95% CI: 0.319–2.504; p <  0.001), were independent factors for OS in HCC patients. Treatment exposure (HR = 2.244; 95% CI: 1.590–3.166; p <  0.001), TNM stage (HR = 2.075; 95% CI: 1.436–3.000; p <  0.001), serum AFP level (HR = 1.819; 95% CI: 1.340–2.469; p = 0.001), tumor size (HR = 1.730; 95% CI: 1.113–2.689; p = 0.015), and ACR (HR = 0.550; 95% CI: 0.392–0.772; p = 0.001) were independent factors for DFS in HCC patients. Conclusions Pretreatment ACR is a convenient and useful parameter for HCC patients predicting OS and DFS. Lower ACR was associated with advanced TNM stage, larger tumor size, and a high concentration of AFP. These results may help to design strategies to personalize management approaches among HCC patients.