Utility of a diffusion kurtosis model in the differential diagnosis of orofacial tumours

W. Panyarak, T. Chikui, K. Tokumori, Y. Yamashita, T. Kamitani, O. Togao, K. Yoshiura

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4 Citations (Scopus)


AIM: To compare the goodness of fit and correlations between diffusion kurtosis imaging (DKI) and a mono-exponential (ME) model, to compare the corrected apparent diffusion coefficient (Dapp) and apparent kurtosis (Kapp) of the DKI model, and the apparent diffusion coefficient (ADC) of the ME model among the various orofacial lesions, and to evaluate the diagnostic performances between the two models. MATERIALS AND METHODS: A total of 100 orofacial lesions underwent echo-planar diffusion magnetic resonance imaging (MRI) with four b-values. The goodness of fit was evaluated using Akaike information criterion. The correlations of the diffusion-derived parameters were evaluated. The diagnostic performance was analysed by receiver operating characteristics (ROC). RESULTS: The DKI model showed a significantly better goodness of fit than the ME model (p<0.0001). The Kapp had a strongly negative correlation with the Dapp (ρ=–0.749) and ADC (ρ=–0.938). A strongly positive correlation existed between the Dapp and ADC (ρ=0.906). All parameters differed significantly between benign tumours and malignant tumours (p<0.05). In differentiating benign tumours from the malignant tumours, the AUC of Dapp (0.871) was larger than that of ADC (0.805); however, a significant difference was not found (p=0.102). CONCLUSION: The DKI model had better goodness of fit than the ME model. Furthermore, the Dapp and Kapp were also characteristic for each pathological category; however, the DKI model did not yield a significantly higher diagnostic performance than the ME model, which might be related to the high correlation among the diffusion-derived parameters and wide variation among categories.

Original languageEnglish
Pages (from-to)507-519
Number of pages13
JournalClinical Radiology
Issue number7
Publication statusPublished - Jul 2020

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging


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