Background: To analyze the prognostic factors of combined small cell lung cancer (CSCLC) and construct a nomogram model for CSCLC.
Methods:
A total of 978 patients diagnosed with CSCLC from 2010 to 2015 were collected based on the SEER database. According to the ratio of 7:3, the patients were divided into the modeling group and the testing group. Univariate and multivariate Cox regression analyses were performed on the patients in the modeling group to analyze the independent factors affecting the prognosis of CSCLC patients and construct a nomogram prediction model, which was verified by the C-index and calibration curve in the training cohort and the validation cohort, respectively.
Results:
Univariate and multivariate Cox regression analysis showed that N stage, M stage, surgery, chemotherapy, radiotherapy, age, brain metastases, lung metastases, liver metastases, bone metastases, and tumor size were independent risk factors affecting the prognosis of CSCLC patients (P<0.05). A nomogram prediction model was constructed based on the above 10 risk factors through visual analysis, and the C-index was 0.753 (95%CI: 0.727~0.750). The calibration curves showed good agreement between the 1 -, 2 -, and 3-year predicted and actual survival rates of the prediction model constructed in this study. The AUC of the 1-, 2-, and 3-year prediction models was 0.813, 0.814, and 0.802, respectively. DCA showed that the nomogram model had more clinical application value in predicting survival prognosis than TNM staging. Finally, according to the total score of the nomogram survival prediction model, all the included cases were divided into low-risk, intermediate-risk, and high-risk strata and these three survival curves of each risk stratification showed significant survival differences (p< 0.05).
Conclusions:
The nomogram prediction model constructed in this study has higher accuracy and clinical application value than the traditional TNM staging. It can predict the 1 -, 2 - and 3-year OS of patients individually and provide a new tool for clinicians to evaluate the survival prognosis of CSCLC. The risk stratification system established by this model can identify high-risk patients more quickly, and make follow-up plans and subsequent treatment plans more targeted.
