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. 2021 May 29;21(1):640.
doi: 10.1186/s12885-021-08384-5.

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

Qinge Shan #  1 , Jianxiang Shi #  2 , Xiaohui Wang  3 , Jun Guo  1 , Xiao Han  1 , Zhehai Wang  4 , Haiyong Wang  5
Affiliations

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

Qinge Shan et al. BMC Cancer. .

Abstract

Background: The 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.

Methods: The 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.

Results: Univariate 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.

Conclusions: We 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.

Keywords: Brain metastasis; Nomogram; Prediction; Small cell lung cancer; Survival.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
A nomogram for prediction of 1-, and 3-year OS rates of SCLC patients diagnosed with brain metastasis
Fig. 2
Fig. 2
Calibration curves and receiver operating characteristic (ROC) Curves. a Calibration curves showing the probability of 1-year OS between the nomogram prediction and the actual observation; b Calibration curves showing the probability of 3-year OS between the nomogram prediction and the actual observation. The prediction probability of the nomogram for OS was plotted on the X-axis, and the actual probability was plotted on the Y-axis. c The ROC curve of nomogram for predicting 1-year survival rate and area under curve (AUC) = 0.606; d The ROC curve of nomogram for predicting 3-year survival rate and AUC = 0.715
Fig. 3
Fig. 3
Survival curves of high-and low-risk groups in each cohort. a Survival curves of high-risk group and low-risk group in the total cohort. b Survival curves of high-risk group and low-risk group in the training cohort. c Survival curves of high-risk group and low-risk group in the validation cohort
See this image and copyright information in PMC

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