Journal
CANCER MANAGEMENT AND RESEARCH
Volume 10, Issue -, Pages 6143-6156Publisher
DOVE MEDICAL PRESS LTD
DOI: 10.2147/CMAR.S183878
Keywords
nomogram; metastatic lung cancer; SEER; prognosis; overall survival; prediction
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Purpose: This study aimed to establish a nomogram to predict the overall survival (OS) of the general non-small-cell lung cancer (NSCLC) patients with distant metastasis. Patients and methods: We investigated Surveillance, Epidemiology, and End Results database for NSCLC patients with distant metastasis diagnosed between 2010 and 2014. Statistically significant prognostic factors were identified using uni- and multivariable Cox regression analyses. A nomogram incorporating these prognostic factors was developed and evaluated by the Harrell's concordance index (C-index), calibration plots, and risk group stratifications. Results: We finally included 18,209 patients for analysis. These patients were divided into two groups, 14,567 cases for the training cohort and 3,642 for the validation cohort. Marital status, sex, race, age, histology, T stage, N stage, histological differentiation, bone metastasis, brain metastasis, liver metastasis, with M la disease, surgery of primary cancer, and chemotherapy were identified as the prognostic factors of the OS and integrated to construct the nomogram. The nomogram had a C-index of 0.704 (95% CI: 0.699-0.709) in the training set and 0.699 (95% CI: 0.689-0.709) in the validation set. The calibration curves for 1- and 2-year OS in the training and validation sets showed acceptable agreement between the predicted and observed survival. Also, the nomogram was capable of stratifying patients into different risk groups within the patients who presented with bone, liver, or brain metastasis, as well as in each . T, N stage, respectively. Conclusion: A nomogram was established and validated to predict individual prognosis for the general patients with distantly metastatic NSCLC. Global prospective data with the latest TNM classification and more comprehensive prognostic factors are needed to improve this model.
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