Predicting the Gene Status and Survival Outcome of Lower Grade Glioma Patients With Multimodal MRI Features
Predicting the Gene Status and Survival Outcome of Lower Grade Glioma Patients With Multimodal MRI Features
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DOI：
10.1109/access.2019.2920396
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Niazi, Tamim
中科院分区：
文献类型：
--
作者：
Chaddad, Ahmad;Desrosiers, Christian;Niazi, Tamim
我们提出了一类新的基于联合强度矩阵(JIM)的多模式图像特征来模拟低级别胶质瘤(LGG)肿瘤的放射学分析中的细粒度纹理特征。实验使用扩展的Jim特征来预测LGG患者的遗传状态和生存结果，这些患者在术前进行T1加权、T1加权后对比、液体衰减反转恢复(FLAIR)和来自癌症成像档案的T2加权MR图像(n=107)。从放射肿瘤学家标记的感兴趣区域中提取纹理特征，并用19个参数进行总结。这些参数然后被用来通过Wilcoxon检验比较突变和野生基因类型组(即IDH1、ATRX、TP53和1p/19q共缺失)，并用Kaplan-Meier估计器比较短生存期和长生存期患者组。随机森林(RF)分类被用来预测基因状态(即突变或野生)和生存结果(即短或长存活)，以及识别高度群体信息的特征。Jim特征的子集与LGG基因状态(即在IDH1和ATRX中，正确的p<；0.05)和生存结果(p=0.0001，HR=0.09，CI=0.03-0.3)显示出统计学意义上的相关性。结合Jim和GLCM特征预测IDH1状态的最大分类AUC为78.59%。结合所有特征(即体积、Jim和GLCM)的分类在预测LGG患者短期和长期生存结果方面的AUC值为86.79%(校正p=0.04)，其中Jim特征通常是最具信息量的预测因子。
We propose a novel class of multimodal image features based on the joint intensity matrix (JIM) to model fine-grained texture signatures in the radiomic analysis of lower grade glioma (LGG) tumors. Experiments use expanded JIM features to predict the genetic status and the survival outcome of LGG patients with preoperative T1-weighted, T1-weighted post-contrast, fluid attenuation inversion recovery (FLAIR), and T2-weighted MR images from The Cancer Imaging Archive (n = 107). Texture features were extracted from regions of interest labeled by a radiation oncologist and summarized by 19 parameters. These parameters are then used to contrast mutant and wild gene type groups (i.e., IDH1, ATRX, TP53, and 1p/19q codeletion) via the Wilcoxon test, and to compare short and long survival patient groups with the Kaplan-Meier estimator. Random forest (RF) classification is employed to predict gene status (i.e., mutation or wild) and survival outcome (i.e., short or long survival), as well as to identify highly group-informative features. A subset of JIM features show statistically significant relationships with LGG gene status (i.e., in IDH1 and ATRX, with corrected p < 0.05) and survival outcome (p = 0.0001, HR = 0.09, CI = 0.03-0.3). A maximum classification AUC of 78.59% was obtained for predicting IDH1 status from combined JIM and GLCM features. Classification combining all features (i.e., volume, JIM, and GLCM) results in an AUC value of 86.79% (corrected p = 0.04) in predicting short and long LGG patient survival outcomes, where JIM features are generally the most informative predictors.