Abstract
BackgroundTo establish and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate the overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection.MethodsThe prognostic model was established by using Lasso regression analysis in the training cohort. The incremental predictive value of the model compared to traditional TNM staging and clinical treatment for individualized survival was evaluated by the concordance index (C-index), time-dependent ROC (tdROC) curve, and decision curve analysis (DCA). A prognostic model risk score based nomogram for OS was built by combining TNM staging and clinical treatment. Patients were divided into high-risk and low-risk subgroups according to the model risk score. The difference in survival between subgroups was analyzed using Kaplan–Meier survival analysis, and correlations between the prognostic model, TNM staging, and clinical treatment were analysed.ResultsThe C-index of the model for OS is 0.769 in the training cohorts and 0.676 in the validation cohorts, respectively, which is higher than that of TNM staging and clinical treatment. The tdROC curve and DCA show the model have good predictive accuracy and discriminatory power compare to the TNM staging and clinical treatment. The prognostic model risk score based nomogram show some net clinical benefit. According to the model risk score, patients are divided into low-risk and high-risk subgroups. The difference in OS rates is significant in the subgroups. Furthermore, the model show a positive correlation with TNM staging and clinical treatment.ConclusionsThe prognostic model showed good performance compared to traditional TNM staging and clinical treatment for estimating the OS in NSCLC (HBV+) patients.
Highlights
To establish and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate the overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection
The following relevant clinical and serological data were collected for each enrolled patient at the time of diagnosis and before any treatment: age, gender, family history, body mass index (BMI), tumor size, clinical treatment, Tumor Node Metastasis stage (TNM stage) [17], white blood cells (WBC), neutrophils (N), lymphocytes (L), platelet (PLT), hepatitis B surface antigen (HbsAg), hepatitis B surface antibody (HBsAb), hepatitis B envelope antigen (HBeAg), hepatitis B envelope antibody(HBeAb), hepatitis B core antibody (HBcAb), hepatitis B core antigen (HBcAb), albumin (ALB), alkaline phosphatase (ALP), apolipoprotein AI (APOA), apolipoprotein B (APOB), C-reactive protein (CRP), lactic dehydrogenase (LDH), glutamyl transpeptidase (GGT), total bilirubin (TBIL), and direct bilirubin (DBIL)
The Neutrophil/lymphocyte ratio (NLR) represented the ratio of neutrophils to lymphocytes ratio [18]; the Platelet/lymphocyte ratio (PLR) represented the ratio of platelets to lymphocytes [18]; the AST/ALT ratio (SLR) was the ratio of aspartate aminotransferase (AST) to alanine transaminase (ALT) [19]; APOA/APOB ratio (ABR) was the ratio of APOA to APOB [20]; C-reactive protein/albumin ratio (CAR) was the ratio of CRP to ALB ratio [21]; prognostic index (PI): score 0 for CRP mg/L or less and a WBC count of × 109/L or less, patients with only one of these abnormalities were allocated a score of 1, and patients with an elevation of both levels were elevated were allocated a score of 2 [22]
Summary
To establish and validate a multi-parametric prognostic model based on clinical features and serological markers to estimate the overall survival (OS) in non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. Previous studies have shown that HBV associated with several extra-hepatic cancers [5,6,7], In addition, diffuse large B-cell lymphoma [8] and multiple myeloma [9] patients with HBV infection have poor survival outcomes compared to non-infected patients. Together, these results implied that NSCLC patients with HBV infection should be distinguished from uninfected patients because they have different clinical characteristics, outcomes and prognostic factors. This may aid in the development of a distinct prognostic predictive model for NSCLC patients with HBV infection
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