流体衰减反转恢复
胶质母细胞瘤
无线电技术
邦费罗尼校正
医学
磁共振成像
单变量分析
生存分析
随机森林
人工智能
单变量
核医学
相关性
比例危险模型
模式识别(心理学)
数学
放射科
计算机科学
统计
内科学
多元分析
多元统计
癌症研究
几何学
作者
Ahmad Chaddad,Paul Daniel,Christian Desrosiers,Matthew Toews,Bassam Abdulkarim
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2019-03-01
卷期号:23 (2): 795-804
被引量:69
标识
DOI:10.1109/jbhi.2018.2825027
摘要
This paper presents a novel set of image texture features generalizing standard grey-level cooccurrence matrices (GLCM) to multimodal image data through joint intensity matrices (JIMs). These are used to predict the survival of glioblastoma multiforme (GBM) patients from multimodal MRI data. The scans of 73 GBM patients from the Cancer Imaging Archive are used in our study. Necrosis, active tumor, and edema/invasion subregions of GBM phenotypes are segmented using the coregistration of contrast-enhanced T1-weighted (CE-T1) images and its corresponding fluid-attenuated inversion recovery (FLAIR) images. Texture features are then computed from the JIM of these GBM subregions and a random forest model is employed to classify patients into short or long survival groups. Our survival analysis identified JIM features in necrotic (e.g., entropy and inverse-variance) and edema (e.g., entropy and contrast) subregions that are moderately correlated with survival time (i.e., Spearman rank correlation of 0.35). Moreover, nine features were found to be associated with GBM survival with a Hazard-ratio range of 0.38-2.1 and a significance level of p <; 0.05 following Holm-Bonferroni correction. These features also led to the highest accuracy in a univariate analysis for predicting the survival group of patients, with AUC values in the range of 68-70%. Considering multiple features for this task, JIM features led to significantly higher AUC values than those based on standard GLCMs and gene expression. Furthermore, an AUC of 77.56% with p = 0.003 was achieved when combining JIM, GLCM, and gene expression features into a single radiogenomic signature. In summary, our study demonstrated the usefulness of modeling the joint intensity characteristics of CE-T1 and FLAIR images for predicting the prognosis of patients with GBM.
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