Development and validation of nomogram prognostic model for early-stage T1-2N0M0 small cell lung cancer: A population-based analysis - Ge, Tao; Zhu, Shuncang; Sun, Liangdong; Yin, Laibo; Dai, Jie; Qian, Jiayi; Chen, Xiangru; Zhang, Peng; Zhu, Jialong; Jiang, Gening (Frontiers in Oncology, 2022)
Background: Survival outcomes of early-stage T1-2N0M0 small cell lung cancer (SCLC) differ widely, and the existent Veterans Administration Lung Study Group or TNM staging system are not efficient in predicting individual prognosis. Methods: Patients diagnosed with T1-2N0M0 SCLC between 2000 and 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. All patients were split into training cohort and validation cohort according to the year of diagnosis. Using multivariable Cox regression, significant prognostic factors were identified and integrated to develop nomograms for 1-year, 3-year, 5-year OS and LCSS prediction. The prognostic performance of our new model was measured by the concordance index (C-index) and calibration curve. We compared our latest model and the 8th AJCC staging system using decision curve analyses (DCA). Kaplan-Meier survival analyses were applied to test the application of the risk stratification system. Results: A total of 1252 patients diagnosed from 2000 to 2011 were assigned to the training cohort, and 513 cases that were diagnosed from 2012 to 2015 constituted the validation cohort. Age, surgery, lymph nodes removal (LNR), and chemotherapy were independent predictors of LCSS. And the variables of sex, age, surgery, LNR, and chemotherapy were identified to be independent predictors of OS. The above-mentioned prognostic factors were entered into the nomogram construction of OS and LCSS. The C-index of this model in the training cohort was 0.68, 0.7, 0.74, and 0.68, 0.7, 0.74 for predicting 1-, 3-, and 5-year OS and LCSS, respectively. And that in the validation cohort were 0.72, 0.7, 0.7 and 0.73, 0.69, 0.7. The calibration curve showed accepted prediction accuracy between nomogram-predicted survival and actual observed survival regardless of the OS or LCSS. In addition, there showed significant distinctions in survival curves of OS and LCSS between different risk groups stratified by prognostic scores. Compared with the 8th AJCC staging system, our new model also improved net benefits. Conclusions: We developed and validated novel nomograms for individual prediction of OS and LCSS integrating the characteristics of patients and tumor. The model showed superior reliability and may help clinicians make treatment strategies and survival predictions for early-stage T1-2N0M0 SCLC.