Effect of radiomics based on conventional MRI in predicting IDH mutation in glioma

YANG Jing; LI Ke; JING Yang; WANG Fang; CHEN Wei

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Effect of radiomics based on conventional MRI in predicting IDH mutation in glioma

更新时间：2022-06-24

- Effect of radiomics based on conventional MRI in predicting IDH mutation in glioma
- null Issue 2, Pages: 149(2022)
- 作者机构：
  
  1. 陆军军医大学第一附属医院放射科,重庆,400038
  2. 慧影医疗科技（北京）股份有限公司科研组,北京,100192
- 作者简介：
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YANG Jing, LI Ke, JING Yang, et al. Effect of radiomics based on conventional MRI in predicting IDH mutation in glioma. [J]. null (2):149(2022)
DOI：

YANG Jing, LI Ke, JING Yang, et al. Effect of radiomics based on conventional MRI in predicting IDH mutation in glioma. [J]. null (2):149(2022) DOI：

摘要

目的探讨基于常规MRI的影像组学分析预测脑胶质瘤IDH突变的效果。方法回顾性分析经术后病理证实的172例脑胶质瘤患者(82例突变型和90例野生型)的临床资料。所有患者术前均行增强MRI检查，术后病理对IDH进行分子检测。由2名神经影像医师在FLAIR和T1加权对比增强（CE-T1WI）序列上逐层勾画肿瘤实体及周围水肿病灶，采用组内相关系数（ICC）评价2名医师分别勾画的感兴趣区域组学特征一致性，然后提取影像特征。分别使用方差阈值法、单变量特征选择法、最小绝对收缩和选择算子（LASSO）对组学特征进行筛选和降维。采用χ2检验或Fisher精确检验评价2组胶质瘤影像学特征的统计学差异。采用五折交叉验证法的分类方式，通过Logistic回归分析建立放射组学模型，采用诺莫图展示模型结果，采用校准曲线验证模型的可靠性。结果在CE-T1WI序列筛选出13个组学特征，在FLAIR序列筛选出7个组学特征。癫痫、WHO分级、影像所见强化和水肿对于IDH突变状态预测具有统计学意义（P＜0.05）。影像组学模型在训练集中的AUC为0.833（95%CI：0.778～0.889），在测试集中的AUC为0.753（95%CI：0.628～0.895）。结论影像组学在预测脑胶质瘤IDH突变方面具有较好的性能。

Abstract

关键词

脑胶质瘤IDH突变影像组学MRI

Keywords

gliomaIDH mutationradiomicsMRI

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