Open AccessNomogram to predict cause‐specific mortality in extensive‐stage small cell lung cancer: A competing risk analysis
Author(s) -
Zhong Jia,
Zheng Qiwen,
An Tongtong,
Zhao Jun,
Wu Meina,
Wang Yuyan,
Zhuo Minglei,
Li Jianjie,
Zhao Xinghui,
Yang Xue,
Jia Bo,
Chen Hanxiao,
Dong Zhi,
Wang Jingjing,
Chi Yujia,
Zhai Xiaoyu,
Wang Ziping
Publication year - 2019
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.13148
Subject(s) - nomogram , medicine , oncology , stage (stratigraphy) , lung cancer , multivariate analysis , radiation therapy , epidemiology , cancer , metastasis , paleontology , biology
Background Small‐cell lung cancer (SCLC) is one of the most aggressive types of lung cancer. The prognosis for SCLC patients depends on many factors. The intent of this study was to construct a nomogram model to predict mortality for extensive‐stage SCLC. Methods Original data was collected from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict death probability for extensive‐stage SCLC. Results A total of 16 554 extensive‐stage SCLC patients from 2004 to 2014 in the SEER database were included in this study. Gender, race, age, TNM staging (including tumor extent, nodal status, and metastasis), and treatment (surgery, chemotherapy, and radiotherapy) were identified as independent predictors for lung cancer‐specific death for extensive‐stage SCLC patients. A nomogram model was constructed based on multivariate models for lung cancer related death and other cause related death. Performance of the two models was validated by calibration and discrimination, with C‐index values of 0.714 and 0.638, respectively. Conclusion A prognostic nomogram model was established to predict death probability for extensive‐stage SCLC. This validated prognostic model may be beneficial for treatment strategy choice and survival prediction.