Development of a nomogram for predicting survival in clinical T1N0M1 lung adenocarcinoma: a population-based study.

Abstract

Objective: This study aimed to establish a prognostic model for clinical T1N0M1 (cT1N0M1) lung adenocarcinoma patients to evaluate the prognosis of patients in terms of overall survival (OS) rate and cancer-specific survival (CSS) rate.

Methods: Data of patients with metastatic lung adenocarcinoma from 2010 to 2016 were collected from the Surveillance, Epidemiology and End Results database. Multivariate Cox regression analysis was conducted to identify relevant prognostic factors and used to develop nomograms. The receiver operating characteristic (ROC) curve and calibration curve are used to evaluate the predictive ability of the nomograms.

Results: A total of 45610 patients were finally included in this study. The OS and CSS nomograms were constructed by same clinical indicators such as age (<60 years or ≥60 years), sex (female or male), race (white, black, or others), surgery, radiation, chemotherapy, and the number of metastatic sites, based on the results of statistical Cox analysis. From the perspective of OS and CSS, surgery contributed the most to the prognosis. The ROC curve analysis showed that the survival nomograms could accurately predict OS and CSS. According to the points obtained from the nomograms, survival was estimated by the Kaplan-Meier method, then cT1N0M1 patients were divided into three groups: low-risk group, intermediate-risk group, and high-risk group, and the OS ( P  < 0.001) and CSS ( P  < 0.001) were significantly different among the three groups.

Conclusion: The nomograms and risk stratification model provide a convenient and reliable tool for individualized evaluation and clinical decision-making of patients with cT1N0M1 lung adenocarcinoma.