Prognostic factors in patients with stage Ⅳ small cell lung cancer: A nomogram prediction model based on different doses of thoracic radiotherapy
10.3760/cma.j.issn.1004-4221.2019.04.005
- VernacularTitle:Ⅳ期小细胞肺癌预后因素分析——基于不同胸部放疗剂量的Nomogram预测模型
- Author:
LiMing XU
1
;
Yajing YUAN
;
Jing LUO
;
Qingsong PANG
;
Jun WANG
;
Zhiyong YUAN
;
Lujun ZHAO
;
Ping WANG
Author Information
1. 天津医科大学肿瘤医院放疗科
- Keywords:
Lung neoplasms/chemotherapy;
Lung neoplasms/radiotherapy;
Prognosis
- From:
Chinese Journal of Radiation Oncology
2019;28(4):268-273
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the effect of different doses of thoracic radiotherapy (TRT) upon the clinical prognosis of patients with extensive-stage (stage Ⅳ) small cell lung cancer (ES-SCLC) and establish a Nomogram prediction model.Methods Clinical data of 144 patients pathologically diagnosed with ES-SCLC undergoing TRT in Tianjin Medical University Cancer Hospital from month,2010 to month,2016 were retrospectively analyzed.Clinical characteristics,treatment data and responses were evaluated.A Nomogram was established by using Cox's proportional hazard regression model to predict the overall survival (OS).The prediction capability and accuracy were assessed by the concordance index (C-index) and a calibration curve between the model and verification groups.Results The median follow-up time was 31.9 months.The 2-year OS rate was 20.3％.The Nomogram model demonstrated that TRT dose,liver metastases,oligometastases/polymetastases,number of chemotherapy cycle and response to chemotherapy were significantly correlated with clinical prognosis.The calibration curve revealed that the predicted and actual OS were highly consistent.The C-index was calculated as 0.701.In the subgroup analyses,patients with high-dose TRT obtained significantly better OS than their counterparts with low-dose TRT.Conclusion The Nomogram prediction model based on different TRT doses can accurately predict the OS rate of ES-SCLC patients,which is an individualized model for predicting the survival probability.
