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Table of Contents    
Year : 2016  |  Volume : 64  |  Issue : 2  |  Page : 273-274

Diffusion kurtosis imaging for cerebral astrocytomas

1 Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
2 Department of Neurological Sciences, Christian Medical College, Vellore, Tamil Nadu, India

Date of Web Publication3-Mar-2016

Correspondence Address:
Vedantam Rajshekhar
Department of Neurological Sciences, Christian Medical College, Vellore, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0028-3886.177601

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How to cite this article:
Vedantam A, Rajshekhar V. Diffusion kurtosis imaging for cerebral astrocytomas. Neurol India 2016;64:273-4

How to cite this URL:
Vedantam A, Rajshekhar V. Diffusion kurtosis imaging for cerebral astrocytomas. Neurol India [serial online] 2016 [cited 2021 Jun 20];64:273-4. Available from:

The authors describe the use of diffusion kurtosis imaging in a series of 60 patients with astrocytomas (grade I-IV).[1] Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) were performed on all patients and diffusion indices were measured on axial images to estimate microstructure in the tumor. DTI, which is used to map white matter tracts and assist in intraoperative navigation, measures the diffusion of water molecules with the assumption that diffusion occurs in a free and unrestricted environment, and has a normal or Gaussian distribution. This assumption may not be valid for biological tissues due to cell membranes and organelles. DKI characterizes non-Gaussian diffusion accounting for tissue complexity, thus providing a more comprehensive approach to characterize neural tissues as compared to DTI.[2] The authors found that solid parts of the tumor had a higher mean kurtosis, axial kurtosis and radial kurtosis for high grade astrocytomas (grade III and IV) as compared to low grade astrocytomas (grade I and II). Mean kurtosis of the peritumoral hyperintensity was significantly higher in high grade tumors. Surgical pathological samples were analyzed for aquaporin-4. Aquaporin-4 expression was significantly higher in the solid parts of the tumor for high grade tumors and this correlated positively with the mean kurtosis, axial and radial kurtosis. In this study, DKI as compared to DTI was more accurate in distinguishing high grade and low grade tumors.[1]

The use of advanced imaging to study primary brain tumors has gained increasing significance with the introduction of advanced magnetic resonance imaging. This has given rise to a new field called “radiogenomics” or “imaging genomics”, which aims to define the relationship between radiological features and genomic and/or molecular tumor characteristics.[3] The ability to use a non-invasive imaging metric to define a tumor's molecular profile is a useful tool for surgeons and oncologists. This approach could yield imaging biomarkers for tumors that may help in diagnosis, prognosis and for planning treatment regimens. Quantitative imaging such as DTI and DKI can considerably reduce the subjectivity associated with describing imaging features, and the authors of this paper should be commended for demonstrating that DKI can differentiate low and high-grade tumors.

The determination of tumor grade is of critical importance in deciding on surgical approach and adjuvant therapy. Microscopic tissue analysis remains the gold standard for grading tumors. As shown in this study, higher kurtosis values in high grade tumors may reflect a higher degree of tissue complexity – increased cellularity, hemorrhage, and necrosis. In high grade tumors, increased mean kurtosis in peritumoral tissue may reflect higher cellularity as a result of invasive growth of tumor cells into the surrounding tissue.[4] DKI, therefore, appears to reflect known pathological features of high grade astrocytomas.

The methodology of measuring DKI indices, however, remains non-standardized. Regions of interests are drawn manually in areas of solid tumor or peritumoral edema to measure DKI indices. In this study, two independent radiologists measured the region of interest to account for the variability in determining regions of interest. Although not mentioned in this study, it would have been useful to show the inter-observer differences in DKI indices between the two radiologists. Some studies now utilize a semi-automated method to draw regions of interest. Multiple software platforms are available to process the images and few studies have determined if there is a difference in the accuracy of metrics measured between different software platforms. As quantitative imaging moves from a research initiative to a clinical tool, a more standardized and automated approach to determining diffusion metrics needs to be established.

The correlations between aquaporin-4 expression and DKI indices appear to be promising in explaining the increased mean kurtosis in high grade tumors. High grade astrocytomas have a pronounced redistribution of aquaporin-4 as compared to low grade astrocytomas.[5] However, this correlation does not necessarily indicate a causative relationship. The authors did not specify if the location of the regions of interest selected on imaging were the same as the location from which tissue was analyzed for aquaporin expression. The heterogeneity of a high-grade glioma can make this correlation coincidental. It is possible that higher mean kurtosis is a reflection of high cellularity and not necessarily increased aquaporin-4 expression. Diffusion of water molecules is affected by a variety of microstructural characteristics in a complex tumor environment, and it is unclear to what extent aquaporin-4 expression contributes to this. Therefore, the conclusion that DKI values can reflect the level of aquaporin-4 expression needs to be investigated further.

With the present study, the authors have shown that DKI can differentiate low and high-grade tumors.[1] The evaluation of DKI as a tool to prognosticate survival in high-grade tumors may help differentiate tumor subtypes and assist in determining treatment options. One approach would be to determine associations between DKI indices and prognostically important genomic features such as isocitrate dehydrogenase 1 (IDH-1) mutation. Recent studies have attempted to use diffusion imaging as a non-invasive marker for the IDH-1 mutation.[6] Although this study shows that DKI is a potential non-invasive biomarker for astrocytomas, more work is needed to determine if it can be used as a clinical tool to make treatment decisions or for prognostication.

  References Top

Tan Y, Zhang H, Zhao RF, Wang XC, Qin JB, Wu XF. Comparison of the values of MRI diffusion kurtosis imaging and diffusion tensor imaging in cerebral astrocytoma grading and their association with aquaporin-4. Neurol India 2016;64:265-72.  Back to cited text no. 1
  Medknow Journal  
Wu EX, Cheung MM. MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed 2010;23:836-48.  Back to cited text no. 2
Zinn PO, Mahmood Z, Elbanan MG, Colen RR. Imaging genomics in gliomas. Cancer J 2015;21:225-34.  Back to cited text no. 3
Giese A, Bjerkvig R, Berens ME, Westphal M. Cost of migration: Invasion of malignant gliomas and implications for treatment. J Clin Oncol 2003;21:1624-36.  Back to cited text no. 4
Warth A, Mittelbronn M, Wolburg H. Redistribution of the water channel protein aquaporin-4 and the K+ channel protein Kir4.1 differs in low- and high-grade human brain tumors. Acta Neuropathol 2005;109:418-26.  Back to cited text no. 5
Lee S, Choi SH, Ryoo I, Yoon TJ, Kim TM, Lee SH, et al. Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging. J Neurooncol 2015;121:141-50.  Back to cited text no. 6


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