A multi-parametric prognostic model based on clinical features and serological markers predicts overall survival in non-
- PDF / 3,071,021 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 52 Downloads / 151 Views
PRIMARY RESEARCH
Cancer Cell International Open Access
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 Shulin Chen1†, Hanqing Huang2†, Yijun Liu1†, Changchun Lai3, Songguo Peng1, Lei Zhou4, Hao Chen1†, Yiwei Xu5† and Xia He1*†
Abstract Background: 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. Methods: The 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. Results: The 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.
*Correspondence: [email protected] † Shulin Chen, Hanqing Huang and Yijun Liu contributed equally to this work † Hao Chen, Yiwei Xu and Xia He contributed equally to this work 1 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, People’s Republic of China Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Comm
Data Loading...
