Abstract
Two Diffusion Kurtosis Imaging Post-Processing Methods for Differentiating Glioma Grades, IDH Mutation Statuses, and Heterogeneity
Highlights
To compare the application of two diffusion kurtosis imaging (DKI) post-processing methods that Diffusional Kurtosis Estimator (DKE) software and DKI histogram analysis in glioma grading, isocitrate dehydrogenase (IDH) mutation typing, and evaluation of tumor heterogeneity
When DKI parameters were compared pairwise based on glioma grade (WHO II, III, and IV), there were significant differences in mean kurtosis (MK) values among the different grades (P < 0.001) (Table 2)
The results of our study demonstrated that MK could be used to differentiate World Health Organization (WHO) II, III, and IV gliomas, which is consistent with the results of other studies [8, 30,31,32,33]
Summary
To compare the application of two DKI post-processing methods that DKE software and DKI histogram analysis in glioma grading, IDH mutation typing, and evaluation of tumor heterogeneity. Histogram parameters were calculated, including minimum, maximum, mean, standard deviation, percentile values (25th, 50th, 75th, 95th), kurtosis, and skewness of Kapp and Dapp. Conclusions: Several quantitative DKI parameters obtained from the DKE software and histogram analysis could be used for glioma grading and predicting IDH mutations. Histogram analysis which based on grey level frequency distribution could provide more information regarding quantitative parameters image, include percentile, mean, minimum and maximum intensity, standard deviation, skewness, and kurtosis. The aim of this study was to compare the application of two DKI post-processing methods that DKE software and DKI histogram analysis in glioma grading, IDH mutation typing, and evaluation of tumor heterogeneity
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