Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features.
Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features.
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DOI：
10.3389/fnins.2016.00615
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发表时间：
2016
影响因子：
4.3
通讯作者：
Van Huffel S
中科院分区：
文献类型：
--
作者：
Ion-Mărgineanu A;Van Cauter S;Sima DM;Maes F;Sunaert S;Himmelreich U;Van Huffel S
目的：本文的目的是根据从多形性胶质母细胞瘤（GBM）患者中检索到的随访多参数磁共振成像（MRI）数据区分肿瘤进展和治疗反应。材料和方法：多参数MRI数据包括常规MRI（cMRI）和高级MRI [即，灌注加权MRI（PWI）和弥散峰度MRI（DKI）]从29例手术后接受辅助治疗的GBM患者获得。我们提出了一个自动流水线处理先进的MRI数据和提取基于强度的直方图特征和3-D纹理特征，使用手动和半手动划定的感兴趣区域（ROI）。分类器的训练使用留一个病人的交叉验证方案在完整的MRI数据。平衡准确率（BAR）值的计算和比较不同的ROI，MR模态和分类器，使用非参数多重比较测试。结果如下：对于cMRI、PWI、DKI和所有三种MRI模态组合，使用手动描绘的最大BAR值分别为0.956、0.85、0.879和0.932。对于cMRI、PWI、DKI和所有三种MR模态组合，使用半手动描绘的最大BAR值分别为0.932、0.894、0.885和0.947。在使用Kruskal-Wallis和事后Dunn-Šidák分析进行统计测试后，我们得出结论，在使用cMRI或所有MRI模态组合的半手动描绘提取的特征上训练RUSBoost分类器表现最好。结论：我们得出了两个主要结论：（1）使用从手动总轮廓中提取的T1后对比度（T1 pc）特征，AdaBoost获得了最高的BAR值，为0.956;（2）使用从半手动描绘的对比增强感兴趣区域中提取的T1 pc平均值、T1 pc-第90百分位数和脑血容量（CBV）第90百分位数、SVM-rbf，和RUSBoost分别实现了0.947和0.932的BAR值。我们的研究结果表明，在T1 pc和CBV特征上训练的AdaBoost，SVM-rbf和RUSBoost可以非常高的准确性区分进行性和反应性GBM患者。
Purpose: The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients. Materials and Methods: Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI [i.e., perfusion weighted MRI (PWI) and diffusion kurtosis MRI (DKI)] were acquired from 29 GBM patients treated with adjuvant therapy after surgery. We propose an automatic pipeline for processing advanced MRI data and extracting intensity-based histogram features and 3-D texture features using manually and semi-manually delineated regions of interest (ROIs). Classifiers are trained using a leave-one-patient-out cross validation scheme on complete MRI data. Balanced accuracy rate (BAR)–values are computed and compared between different ROIs, MR modalities, and classifiers, using non-parametric multiple comparison tests. Results: Maximum BAR–values using manual delineations are 0.956, 0.85, 0.879, and 0.932, for cMRI, PWI, DKI, and all three MRI modalities combined, respectively. Maximum BAR–values using semi-manual delineations are 0.932, 0.894, 0.885, and 0.947, for cMRI, PWI, DKI, and all three MR modalities combined, respectively. After statistical testing using Kruskal-Wallis and post-hoc Dunn-Šidák analysis we conclude that training a RUSBoost classifier on features extracted using semi-manual delineations on cMRI or on all MRI modalities combined performs best. Conclusions: We present two main conclusions: (1) using T1 post-contrast (T1pc) features extracted from manual total delineations, AdaBoost achieves the highest BAR–value, 0.956; (2) using T1pc-average, T1pc-90th percentile, and Cerebral Blood Volume (CBV) 90th percentile extracted from semi-manually delineated contrast enhancing ROIs, SVM-rbf, and RUSBoost achieve BAR–values of 0.947 and 0.932, respectively. Our findings show that AdaBoost, SVM-rbf, and RUSBoost trained on T1pc and CBV features can differentiate progressive from responsive GBM patients with very high accuracy.