Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging.
Differentiation between glioblastomas and solitary brain metastases using diffusion tensor imaging.
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DOI：
10.1016/j.neuroimage.2008.09.027
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发表时间：
2009-02-01
期刊：
影响因子：
5.7
通讯作者：
Poptani H
中科院分区：
文献类型：
--
作者：
Wang S;Kim S;Chawla S;Wolf RL;Zhang WG;O'Rourke DM;Judy KD;Melhem ER;Poptani H
本研究的目的是确定扩散张量成像（DTI）指标，包括张量形状指标，如线性和平面各向异性系数（CL和CP），是否有助于区分胶质母细胞瘤和孤立性脑转移瘤。本研究纳入63例组织病理学诊断为胶质母细胞瘤（男性22例，女性16例，平均年龄58.4岁）和脑转移（男性13例，女性12例，平均年龄56.3岁）的患者。对比增强t1加权、流体衰减反转恢复（FLAIR）图像、分数各向异性（FA）、表观扩散系数（ADC）、CL和CP图被共同注册，每个病变被半自动细分为四个区域：中心、增强、近端肿瘤周围和远端肿瘤周围。从每个区域测量DTI指标以及对比度增强的t1加权图像的归一化信号强度。采用单因素和多因素logistic回归分析确定最佳分类模型。结果显示，胶质母细胞瘤的FA、CL和CP均显著高于脑转移瘤的各节段区（p < 0.05），其中增强区差异最显著（p < 0.001）。单独使用增强区的FA和CL预测精度最高，曲线下面积为0.90。最佳logistic回归模型包括增强部分的ADC、FA和CP 3个参数，灵敏度为92%，特异度为100%，曲线下面积为0.98。我们的结论是，单独使用或联合使用DTI指标，具有作为区分胶质母细胞瘤和转移瘤的非侵入性措施的潜力。
The purpose of this study is to determine whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as linear and planar anisotropy coefficients (CL and CP) can help differentiate glioblastomas from solitary brain metastases. Sixty-three patients with histopathologic diagnosis of glioblastomas (22 men, 16 women, mean age 58.4 years) and brain metastases (13 men, 12 women, mean age 56.3 years) were included in this study. Contrast-enhanced T1-weighted, fluid attenuated inversion recovery (FLAIR) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CL and CP maps were co-registered and each lesion was semi-automatically subdivided into four regions: central, enhancing, immediate peritumoral and distant peritumoral. DTI metrics as well as the normalized signal intensity from the contrast-enhanced T1-weighted images were measured from each region. Univariate and multivariate logistic regression analyses were employed to determine the best model for classification. The results demonstrated that FA, CL and CP from glioblastomas were significantly higher than those of brain metastases from all segmented regions (p < 0.05), and the differences from the enhancing regions were most significant (p < 0.001). FA and CL from the enhancing region had the highest prediction accuracy when used alone with an area under the curve of 0.90. The best logistic regression model included three parameters (ADC, FA and CP) from the enhancing part, resulting in 92% sensitivity, 100% specificity and area under the curve of 0.98. We conclude that DTI metrics, used individually or combined, have a potential as a noninvasive measure to differentiate glioblastomas from metastases.
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