Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging

Kashimura, Hiroshi; Inoue, Takashi; Ogasawara, Kuniaki; Arai, Hiroshi; Otawara, Yasunari; Kanbara, Yoshiyuki; Ogawa, Akira

doi:10.3171/jns-07/10/0784

Cited by 64 publications

(48 citation statements)

References 18 publications

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“…We expect that μFA may not only contribute to the investigation of complex WM geometries, but also in detecting microscopic anisotropy in tissues that are approximately isotropic on the voxel scale, for example, in GM (McNab et al, 2013; Truong et al, 2014). Further, the μFA and OP may provide complementing information to the FA and tensor shape analysis previously used in the differentiation of classic and atypical meningioma (Toh et al, 2008), detection of fibroblastic meningioma (Tropine et al, 2007), and in the preoperative estimation of tumor consistency (Kashimura et al, 2007), by removing the confounding effects of orientation dispersion which are otherwise ignored.…”

Section: Discussionmentioning

confidence: 99%

Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors

et al. 2015

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The anisotropy of water diffusion in brain tissue is affected by both disease and development. This change can be detected using diffusion MRI and is often quantified by the fractional anisotropy (FA) derived from diffusion tensor imaging (DTI). Although FA is sensitive to anisotropic cell structures, such as axons, it is also sensitive to their orientation dispersion. This is a major limitation to the use of FA as a biomarker for “tissue integrity”, especially in regions of complex microarchitecture. In this work, we seek to circumvent this limitation by disentangling the effects of microscopic diffusion anisotropy from the orientation dispersion. The microscopic fractional anisotropy (μFA) and the order parameter (OP) were calculated from the contrast between signal prepared with directional and isotropic diffusion encoding, where the latter was achieved by magic angle spinning of the q-vector (qMAS). These parameters were quantified in healthy volunteers and in two patients; one patient with meningioma and one with glioblastoma. Finally, we used simulations to elucidate the relation between FA and μFA in various micro-architectures. Generally, μFA was high in the white matter and low in the gray matter. In the white matter, the largest differences between μFA and FA were found in crossing white matter and in interfaces between large white matter tracts, where μFA was high while FA was low. Both tumor types exhibited a low FA, in contrast to the μFA which was high in the meningioma and low in the glioblastoma, indicating that the meningioma contained disordered anisotropic structures, while the glioblastoma did not. This interpretation was confirmed by histological examination. We conclude that FA from DTI reflects both the amount of diffusion anisotropy and orientation dispersion. We suggest that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence.

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Section: Discussionmentioning

confidence: 99%

Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors

et al. 2015

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“…The method presented here is applicable to a wide variety of inquiries as it requires few assumptions about the investigated tissue and is implemented as a straight-forward modification to the conventional diffusion encoding sequence (Lasič et al, 2014; Szczepankiewicz et al, 2015). This is especially true if diffusion anisotropy parameters are intended to differentiate tumor subtypes (Jolapara et al, 2010; Sanverdi et al, 2012; Wang et al, 2012), preoperative estimation of tumor consistency (Kashimura et al, 2007; Tropine et al, 2007), delineation for biopsies (Kinoshita et al, 2008), and tumor proliferation (Beppu et al, 2005). Such cases warrant the use of MK A or μFA, since conventional measures of anisotropy ignore the interaction between orientation coherence and voxel size, and may therefore be strongly biased (Figure 7).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the μFA stratified the fibroblastic meningiomas from the other subtypes, i.e., the fibroblastic tumors had the four highest μFA values, whereas the same stratification was not observed for FA. It may therefore be possible to use μFA for pre-surgical toughness estimation in meningiomas (Kashimura et al, 2007; Tropine et al, 2007). We emphasize that the tissue with the highest μFA is not necessarily mapped to the highest FA due to variable orientation coherence (black arrows point to the same tumor sample).…”

Section: Figurementioning

confidence: 99%

The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)

et al. 2016

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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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“…However, the authors did feel that soft tumors tended to show hyperintensity on T2WI. Similarly, Kashimura et al found that there was no association between T2WI and consistency 25 . A more recent larger series from Romani et al also found no association using T1- or T2WI, as well as FLAIR or proton density weighted imaging (PDWI) 26 .…”

Section: Neuroimaging Studies Of Meningioma Consistencymentioning

confidence: 93%

“…However, these results were not compared with an actual assessment of meningioma consistency. The same year, Kashimura et al showed that FA values for hard meningiomas were significantly higher compared to soft ones 25 . Using an FA cut-off value of 0.3, they demonstrated sensitivity of 91% and specificity of 67%.…”

Section: Neuroimaging Studies Of Meningioma Consistencymentioning

confidence: 94%

Predicting Meningioma Consistency on Preoperative Neuroimaging Studies

Shiroishi

Cen

Tamrazi

et al. 2016

Neurosurgery Clinics of North America

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Synopsis This article provides an overview of the neuroimaging literature focused on pre-operative prediction of meningioma consistency. A validated, non-invasive neuroimaging method to predict tumor consistency can provide valuable information regarding neurosurgical planning and patient counseling. Most of the neuroimaging literature indicates conventional MRI using T2-weighted imaging (T2WI) may be helpful to predict meningioma consistency, however, further rigorous validation is necessary. Much less is known about advanced MRI techniques such as diffusion MRI, MR elastography (MRE) and MR spectroscopy (MRS). Of these methods, MRE and DTI appear particularly promising.

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Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging

Abstract: The FA value calculated from preoperative MR DT imaging predicts meningioma consistency.

Cited by 64 publications

References 18 publications

Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors

Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: Applications in healthy volunteers and in brain tumors

The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)

Predicting Meningioma Consistency on Preoperative Neuroimaging Studies

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