Bingjie Zeng1, Ping Ji1, Chen Chen1, Junjun Sun1, Chenzheng Gu1, Anquan Shang1, Junlu Wu1, Zujun Sun2, Dong Li3. 1. Department of Clinical Laboratory, Shanghai Tongji Hospital, Tongji University School of Medicine, No. 389, Xincun Road, Shanghai, 200065, China. 2. Department of Clinical Laboratory, Shanghai Tongji Hospital, Tongji University School of Medicine, No. 389, Xincun Road, Shanghai, 200065, China. Electronic address: sunzujun@tongji.edu.cn. 3. Department of Clinical Laboratory, Shanghai Tongji Hospital, Tongji University School of Medicine, No. 389, Xincun Road, Shanghai, 200065, China. Electronic address: lidong@tongji.edu.cn.
Abstract
OBJECTIVE: The purpose of this study was to establish and validate a nomogram to predict the prognosis in patients with non-small cell lung cancer (NSCLC) from multiple perspectives. RESULTS: A total of 98,640 eligible patients were randomly divided into a training set (n = 69,048) and a validation set (n = 29,592). The baseline characteristics of the two sets were similar. We used clinical data from patients in the training set for univariate and multivariate Cox regression analyses. Twelve independent risk factors were incorporated for constructed a prognostic nomogram. And the nomogram with a concordance index of 0.777 (95 % CI, 0.775 to 0.779) for overall survival. The calibration curve results showed that the actual survival rate was consistent with the predicted survival rate. The area under curve of the receiver operating characteristic curves demonstrated that the nomogram has a high prediction of the overall survival rate in patients with NSCLC. CONCLUSION: We have developed a nomogram with high prediction accuracy and discrimination ability, which can help clinicians making personalized survival predictions for NSCLC patients.
OBJECTIVE: The purpose of this study was to establish and validate a nomogram to predict the prognosis in patients with non-small cell lung cancer (NSCLC) from multiple perspectives. RESULTS: A total of 98,640 eligible patients were randomly divided into a training set (n = 69,048) and a validation set (n = 29,592). The baseline characteristics of the two sets were similar. We used clinical data from patients in the training set for univariate and multivariate Cox regression analyses. Twelve independent risk factors were incorporated for constructed a prognostic nomogram. And the nomogram with a concordance index of 0.777 (95 % CI, 0.775 to 0.779) for overall survival. The calibration curve results showed that the actual survival rate was consistent with the predicted survival rate. The area under curve of the receiver operating characteristic curves demonstrated that the nomogram has a high prediction of the overall survival rate in patients with NSCLC. CONCLUSION: We have developed a nomogram with high prediction accuracy and discrimination ability, which can help clinicians making personalized survival predictions for NSCLCpatients.
Authors: Oliver Higuera Gómez; Amaia Moreno Paul; Ana Laura Ortega Granados; Silverio Ros Martínez; Diego Pérez Parente; Pedro Ruiz Gracia; Lucía Sáenz Cuervo-Arango; Laia Vilà Journal: Cancer Manag Res Date: 2021-06-14 Impact factor: 3.989