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

医学 表皮生长因子受体 腺癌 肿瘤科 内科学 比例危险模型 肺癌 生存分析 预测模型 脑转移 突变 总体生存率 病理 癌症 转移 基因 生物 生物化学
作者
Hongwei Li,Weili Wang,Haixia Jia,Jianhong Lian,Jianzhong Cao,Xiaqin Zhang,Xing Song,Sufang Jia,Zhengran Li,Xing Cao,Wei Zhou,Songye Han,Weihua Yang,Yanfen Xi,Shenming Lian
出处
期刊:Thoracic Cancer [Wiley]
卷期号:8 (5): 436-442 被引量:1
标识
DOI:10.1111/1759-7714.12460
摘要

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.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.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.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.
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