Risk stratification model for foreseeing overall survival in Chinese patients with initially metastatic small-cell lung cancer

The study was outlined to develop and approve a nomogram and chance stratification demonstrate for foreseeing overall survival of Chinese patients with at first metastatic small-cell lung cancer (SCLC). We collected information from the Surveillance, Epidemiology, and End Results (SEER) database approximately Chinese SCLC patients with at first distant metastases between 2010 and 2015. Patients with incomplete data about the follow-up time or clinicopathological information were excluded. The included patients were randomized into the training and validation set. Univariate and multivariate Cox proportional hazard regression models were performed. By integrating the significant variables screened, a prescient nomogram and risk stratification model were developed. In addition, we collected 198 small-cell lung cancer patients with metastasis at diagnosis from the case database of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine as an external validation cohort. In all, 421 patients were screened from the SEER database. Multivariate examination showed that age (P = .049), sex (P = .001), grade (P = .008), chemotherapy (P = .001), liver metastasis (P = .001), and pleural invasion (P = .012) were independent prognostic factors. The C-indicator of the nomogram to anticipate overall survival was higher than that of the eighth edition of the American Joint Committee on Cancer Tumor Node Metastasis classification system (0.75 vs 0.543; P < .001). A risk stratification model was encouraged to be created to precisely classify patients into 2 prognostic bunches. The survival rates anticipated by the nomogram appeared to have critical qualifications from the Kaplan-Meier curves in the entire SEER cohort. Calibration curves and survival predictions also showed strong accuracy and consistency in the external validation cohort. The nomogram provided a clear prognostic superiority over the traditional Tumor Node Metastasis system. It could help clinicians make individual risk predictions for initially metastatic Chinese SCLC cancer patients and give necessary treatment recommendations.