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Prognostic model for brain metastases from lung adenocarcinoma identified with epidermal growth factor receptor mutation status

Authors :
Zhengran Li
Songye Han
Weihua Yang
Haixia Jia
Wei Zhou
Shenming Lian
Xing Song
Weili Wang
Sufang Jia
Jianzhong Cao
Xing Cao
Xiaqin Zhang
Yanfen Xi
Hongwei Li
Jianhong Lian
Source :
Thoracic Cancer. 8:436-442
Publication Year :
2017
Publisher :
Wiley, 2017.

Abstract

Background Several indices have been developed to predict survival of brain metastases (BM) based on prognostic factors. However, such models were designed for general brain metastases from different kinds of cancers, and prognostic factors vary between cancers and histological subtypes. Recently, studies have indicated that epidermal growth factor receptor (EGFR) mutation status may be a potential prognostic biological factor in BM from lung adenocarcinoma. Thus, we sought to define the role of EGFR mutation in prognoses and introduce a prognostic model specific for BM from lung adenocarcinoma. Methods Data of 256 patients with BM from lung adenocarcinoma identified with EGFR mutations were collected. Independent prognostic factors were confirmed using a Cox regression model. The new prognostic model was developed based on the results of multivariable analyses. The score of each factor was calculated by six-month survival. Prognostic groups were divided into low, medium, and high risk based on the total scores. The prediction ability of the new model was compared to the three existing models. Results EGFR mutation and Karnofsky performance status were independent prognostic factors and were thus integrated into the new prognostic model. The new model was superior to the three other scoring systems regarding the prediction of three, six, and 12-month survival by pairwise comparison of the area under the curve. Conclusion Our proposed prognostic model specific for BM from lung adenocarcinoma incorporating EGFR mutation status was valid in predicting patient survival. Further verification is warranted, with prospective testing using large sample sizes.

Details

ISSN :
17597706
Volume :
8
Database :
OpenAIRE
Journal :
Thoracic Cancer
Accession number :
edsair.doi...........d35482550d2f99a66760fdb184456363
Full Text :
https://doi.org/10.1111/1759-7714.12460