A Survival Prediction Model of Pulmonary Sarcomatoid Carcinoma Based on SEER Database
10.3971/j.issn.1000-8578.2021.21.0259
- VernacularTitle:基于SEER数据库构建肺肉瘤样癌的生存预测模型
- Author:
Ying LIU
1
;
Bin XIE
;
Meng WANG
;
Yiran LI
;
Wenjin YAN
;
Xingxiang XU
;
Lingfeng MIN
Author Information
1. The First Clinical College of Dalian Medical University, Dalian 116000, China
- Publication Type:Research Article
- Keywords:
Pulmonary sarcomatoid carcinoma;
Prognostic factors;
Predictive model;
SEER database
- From:
Cancer Research on Prevention and Treatment
2021;48(9):853-858
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the factors affecting the prognosis of patients with pulmonary sarcomatoid carcinoma (PSC) and construct a nomogram prediction model for the prognosis of PSC patients. Methods Based on the SEER database, 1671 patients diagnosed as PSC from 1988 to 2015 were collected and divided into modeling group and validation group according to the ratio of 7:3. Univariate and multivariate Cox regression analysis were performed in the modeling group to explore independent risk factors affecting the prognosis and construct a nomogram survival prediction model. The consistency index and calibration curve were used for verification in the modeling group and the test module respectively. Results Age, gender, histological type, TNM stage, tumor diameter > 50mm, surgery, radiotherapy and chemotherapy were independent factors that affected the prognosis of PSC patients. The nomogram prediction model was constructed and verified based on independent factors. The C indexes of the modeling group and the test model were 0.790 (95%CI: 0.776-0.804) and 0.781 (95%CI: 0.759-0.803), respectively. The calibration curves of the modeling group and the test model indicated that the predicted survival rate was basically the same as the actual survival rate. Conclusion The nomogram prediction model constructed based on the results of multivariate analysis can predict the prognosis of PSC patients, and has high accuracy and consistency.
