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The development and validation of a nomogram for predicting brain metastases in lung squamous cell carcinoma patients: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database

  • Zhang, Jingya1
  • Xu, Jiali1
  • Jin, Shidai1
  • Gao, Wen1
  • Guo, Renhua1
  • Chen, Liang1
  • 1 First Affiliated Hospital of Nanjing Medical University, Nanjing , (China)
Published Article
Journal of Thoracic Disease
AME Publishing Company
Publication Date
Jan 01, 2021
DOI: 10.21037/jtd-20-3494
PMID: 33569207
PMCID: PMC7867817
PubMed Central
  • Original Article


Background The incidence of brain metastasis (BM) in patients suffering from lung squamous cell carcinoma (LUSC) is lower than that in patients suffering from non-squamous cell carcinoma (NSCC) and there are few studies on BM of LUSC. The purpose of this investigation was to ascertain the risk factors of LUSC, as well as to establish a nomogram prognostic model to predict the incidence of BM in patients with LUSC. Methods Patients diagnosed with LUSC between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and the patient data were collated. All patients diagnosed from 2010–2012 were allocated into the training cohort, and the remaining patients diagnosed from 2013–2015 formed the test cohort. Using factors that were screened out through logistic regression analyses, the nomogram in the training cohort was established. It was then evaluated for discrimination and calibration using the test cohort. The performance of the nomogram was assessed by quantifying the area under the receiver operating characteristic (ROC) curve and evaluating the calibration curve. Results A total of 26,154 LUSC patients were included in the study. The training cohort consisted of 16,543 patients and there were 8611 patients in the test cohort. Age, marital status, insurance status, histological grade, tumor location, laterality, stage of the cancer, number of metastatic organs, chemotherapy, surgery, and radiotherapy were highly correlated with the incidence of BM. The area under the ROC curve (AUC) of the nomogram for the training cohort and the test cohort were 0.810 [95% confidence interval (CI): 0.796 to 0.823] and 0.805 (95% CI: 0.784 to 0.825), respectively. The slope of the calibration curve was close to 1. Conclusions The nomogram was able to accurately predict the incidence of BM. This may be beneficial for the early identification of high-risk LUSC patients and the establishment of individualized treatments.

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