A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study

BMC Cancer

Qinge Shan¹  Zhehai Wang¹  Haiyong Wang¹  Xiao Han¹  Jun Guo¹  Jianxiang Shi²  Xiaohui Wang³ 

[1] Department of Internal Medicine-Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, 250117, Jinan, Shandong, China;Henan Academy of Medical and Pharmaceutical Sciences, Precision Medicine Center, Zhengzhou University, Zhenzhou, Henan, China;Research Service Office, Shandong Liaocheng People’s Hospital, Liaocheng, Shandong, China;

关键词: Small cell lung cancer;    Brain metastasis;    Survival;    Prediction;    Nomogram;   

DOI  :  10.1186/s12885-021-08384-5

来源: Springer

【 摘 要 】

BackgroundThe prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis.MethodsThe 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test.ResultsUnivariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group.ConclusionsWe established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.

【 授权许可】

CC BY