亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
太阳当空照完成签到 ,获得积分10
1秒前
熊猫完成签到,获得积分0
12秒前
13秒前
JSFeng完成签到,获得积分20
16秒前
ding应助JSFeng采纳,获得10
20秒前
58秒前
Akim应助SY采纳,获得10
58秒前
风之子发布了新的文献求助10
1分钟前
小萌兽完成签到 ,获得积分10
1分钟前
充电宝应助缘神糕手采纳,获得10
1分钟前
1分钟前
1分钟前
葱葱花卷完成签到 ,获得积分10
1分钟前
ataybabdallah完成签到,获得积分10
1分钟前
TYY完成签到,获得积分10
2分钟前
qianyixingchen完成签到 ,获得积分10
2分钟前
2分钟前
2分钟前
无极微光应助TYY采纳,获得20
2分钟前
科研通AI6.2应助Su采纳,获得10
2分钟前
attention完成签到,获得积分10
2分钟前
背后的山柏完成签到,获得积分10
2分钟前
2分钟前
2分钟前
xi发布了新的文献求助10
2分钟前
OsamaKareem应助Crw__采纳,获得10
2分钟前
罗洁发布了新的文献求助10
2分钟前
Lin完成签到 ,获得积分10
2分钟前
开朗大雁发布了新的文献求助20
3分钟前
zqq完成签到,获得积分0
3分钟前
靓丽的山蝶完成签到 ,获得积分10
3分钟前
香蕉觅云应助hanj采纳,获得10
3分钟前
3分钟前
wanci应助科研通管家采纳,获得10
3分钟前
3分钟前
3分钟前
y2k完成签到,获得积分10
3分钟前
临子完成签到,获得积分10
3分钟前
3分钟前
两只棚猫发布了新的文献求助10
3分钟前
高分求助中
Overcoming Stigma and Bias in Obesity Management 800
Malcolm Fraser : a biography 700
Signals, Systems, and Signal Processing 610
Bounds for Statistical Estimation in Semiparametric Models 500
Climate change and sports: Statistics report on climate change and sports 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Ideology and Meaning-Making under the Putin Regime 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6471930
求助须知:如何正确求助?哪些是违规求助? 8275933
关于积分的说明 17646185
捐赠科研通 5550704
什么是DOI,文献DOI怎么找? 2909374
邀请新用户注册赠送积分活动 1886159
关于科研通互助平台的介绍 1737057