Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.
Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI.
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DOI：
10.1002/jmri.25963
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发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Saligheh Rad H
中科院分区：
文献类型：
--
作者：
Fathi Kazerooni A;Nabil M;Zeinali Zadeh M;Firouznia K;Azmoudeh-Ardalan F;Frangi AF;Davatzikos C;Saligheh Rad H
弥漫性胶质瘤的靶向局部活检和治疗依赖于准确识别组织亚区，而目前的MRI技术缺乏特异性。探讨各种常规和定量MRI方法在区分脑胶质瘤亚区中的互补和竞争作用。有前途。从10例新诊断的胶质瘤患者中收集了51例组织标本，采用图像引导定位活检手术。术前在3 T下采集常规和定量MR图像，包括增强前后T1 w、T2 w、T2-FLAIR、T2-弛豫测量、DWI、DTI、IVIM和DSC-MRI。活检标本的组织病理学归因于胶质瘤组织亚区类别的活动性肿瘤（AT），浸润性水肿（IE），和正常组织（NT）亚区。对于每个组织样本，从术前图像中导出包括15个基于MRI的参数的特征向量，并通过机器学习算法进行评估，以确定用于表征组织子区域的最佳多参数特征组合。AT、IE和NT亚区的区分采用单因素方差分析（ANOVA）检验，成对组织亚区的区分采用Tukey honest显著性差异和Games-Howell检验（P < 0.05）。交叉验证的特征选择和分类方法，用于识别准确的多参数MRI参数组合。排除17份组织样本后，考虑分析34份样本（AT = 6，IE = 20和NT = 8）。对于基于扩散的参数（AUC>90%），观察到区分IE与NT和AT与NT的最高准确度和统计学显著性差异，并且灌注衍生参数是区分IE与AT的最准确特征。“CBV、MD、T2_ISO、FLAIR”参数的组合显示出用于识别三个亚区的高诊断性能（AUC ~90%）。整合几个定量沿着与传统的MRI参数可能提供一个潜在的多参数成像生物标志物，用于预测组织病理学证实的胶质瘤组织亚区。2阶段3
Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity. To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas. Prospective. Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas. Conventional and quantitative MR images consisting of pre- and postcontrast T1w, T2w, T2-FLAIR, T2-relaxometry, DWI, DTI, IVIM, and DSC-MRI were acquired preoperatively at 3T. Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions. For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination. After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of “CBV, MD, T2_ISO, FLAIR” parameters showed high diagnostic performance for identification of the three subregions (AUC ~90%). Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions. 2 Stage 3
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影响因子：
2.9
作者：
Lope-Piedrafita, Silvia;Garcia-Martin, Maria L.;Galons, Jean-Philippe;Gillies, Robert J.;Trouard, Theodore P.
通讯作者：
Trouard, Theodore P.
影响因子：
12.7
作者：
Claes A;Idema AJ;Wesseling P
通讯作者：
Wesseling P
影响因子：
19.7
作者：
LEBIHAN, D;BRETON, E;LAVALJEANTET, M
通讯作者：
LAVALJEANTET, M
影响因子：
4.3
作者：
Ion-Mărgineanu A;Van Cauter S;Sima DM;Maes F;Sunaert S;Himmelreich U;Van Huffel S
通讯作者：
Van Huffel S
影响因子：
19.7
作者：
Boonzaier, Natalie R.;Larkin, Timothy J.;Price, Stephen J.
通讯作者：
Price, Stephen J.