The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).
The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).
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DOI：
10.1016/j.neuroimage.2016.07.038
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发表时间：
2016-11-15
期刊：
影响因子：
5.7
通讯作者：
Nilsson M
中科院分区：
文献类型：
--
作者：
Szczepankiewicz F;van Westen D;Englund E;Westin CF;Ståhlberg F;Lätt J;Sundgren PC;Nilsson M
扩散MRI（dMRI）可以根据体素内表观扩散率的变化来探测肿瘤组织的结构异质性。然而，扩散方差和组织异质性之间的联系并没有得到很好的确立。为了研究这一联系，我们测试的假设，扩散方差，微观各向异性和各向同性异质性引起的，与可变的细胞偏心率和细胞密度在脑肿瘤。我们在手术前对7例脑膜瘤和8例胶质瘤使用一种新的弥散方差分解（DIVIDE）编码方案进行了dMRI。根据总平均峰度（MKT）从dMRI量化扩散方差，并使用DIVIDE将MKT分解为由微观各向异性（MKA）和各向同性异质性（MKI）引起的组分。根据分数各向异性（FA）和微观分数各向异性（μFA）评价扩散各向异性。对切除的肿瘤组织进行定量显微镜检查，其中分别通过结构张量分析和细胞核分割来量化结构各向异性和细胞密度。为了验证DIVIDE参数，将其与来自显微镜的相应参数相关联。我们发现DIVIDE参数和相应的显微镜参数之间非常一致; MKA与细胞偏心率相关（r = 0.95，p < 10−7），MKI与细胞密度方差相关（r = 0.83，p < 10−3）。扩散各向异性与体素尺度（FA，r = 0.80，p < 10−3）和微观尺度（μFA，r = 0.93，p < 10−6）上的结构张量各向异性相关。多元回归分析表明，传统的MKT参数反映了可变的细胞偏心率和细胞密度，因此缺乏特异性的微观结构特征。然而，特异性通过分解两个贡献获得; MKA仅与细胞偏心率相关，MKI仅与细胞密度方差相关。脑膜瘤的差异主要是由显微镜各向异性（平均值± s.d.）MKA = 1.11 ± 0.33 vs MKI = 0.44 ± 0.20（p < 10−3），而在胶质瘤中，它主要是由各向同性异质性引起的MKI = 0.57 ± 0.30 vs MKA = 0.26 ± 0.11（p < 0.05）。总之，DIVIDE允许非侵入性映射参数，反映可变细胞偏心率和密度。这些结果构成了令人信服的证据，组织异质性的特定方面和dMRI参数之间存在联系。微观各向异性和各向同性异质性的分解效应有利于改善对肿瘤异质性以及微观和宏观尺度上的扩散各向异性的解释。
The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MKA correlated with cell eccentricity (r = 0.95, p < 10−7) and MKI with the cell density variance (r = 0.83, p < 10−3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r = 0.80, p < 10−3) and microscopic scale (μFA, r = 0.93, p < 10−6). A multiple regression analysis showed that the conventional MKT parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MKA was associated only to cell eccentricity, and MKI only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean ± s.d.) MKA = 1.11 ± 0.33 vs MKI = 0.44 ± 0.20 (p < 10−3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MKI = 0.57 ± 0.30 vs MKA = 0.26 ± 0.11 (p < 0.05). In conclusion, DIVIDE allows noninvasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.
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影响因子：
0.6
作者：
Edén, M
通讯作者：
Edén, M
DOI：
10.1016/j.jmr.2011.09.022
发表时间：
1996-06-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子：
--
作者：
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通讯作者：
Pierpaoli, C
影响因子：
4.4
作者：
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通讯作者：
Topgaard, Daniel
影响因子：
3.5
作者：
Budde MD;Annese J
通讯作者：
Annese J
影响因子：
2
作者：
Callaghan, PT;Komlosh, ME
通讯作者：
Komlosh, ME