4.5 Article

Diffusion kurtosis imaging combined with dynamic susceptibility contrast-enhanced MRI in differentiating high-grade glioma recurrence from pseudoprogression

Journal

EUROPEAN JOURNAL OF RADIOLOGY
Volume 144, Issue -, Pages -

Publisher

ELSEVIER IRELAND LTD
DOI: 10.1016/j.ejrad.2021.109941

Keywords

Glioma; Recurrence; Pseudoprogression; Diffusion kurtosis imaging (DKI); Dynamic susceptibility contrast-enhanced (DSC) MRI

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The study compared the additional value of diffusion kurtosis imaging (DKI) and dynamic susceptibility contrast-enhanced (DSC) MRI in differentiating glioma recurrence from pseudoprogression. The combination of DKI and DSC MRI showed improved diagnostic performance, with a diagnostic accuracy of 88.24%.
Objectives: To compare the added value of diffusion kurtosis imaging (DKI) with the combination of dynamic susceptibility contrast-enhanced (DSC) MRI in differentiating glioma recurrence from pseudoprogression. Methods: Thirty-four patients with high-grade gliomas developing new and/or increasing enhanced lesions within six months after surgery and chemoradiotherapy were retrospectively analyzed. All patients were path-ologically confirmed to have recurrent glioma (n = 22) or pseudoprogression (n = 12). The DKI and DSC MRI parameters were calculated based on the enhanced lesions on contrast-enhanced T1WI. ROC analysis was performed on significant variables to determine their diagnostic performance. Multivariate logistic regression was used to determine the best prediction model for discrimination. Results: The relative mean kurtosis (rMK), relative axial kurtosis (rKa), relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) of glioma recurrence were higher than those of pseudoprogression (all, P < 0.05). The AUCs and diagnostic accuracy were 0.879 and 82.35% for rMK, 0.723 and 70.59% for rKa, 0.890 and 82.35% for rCBV, 0.765 and 73.53% for rMTT, respectively. A multivariate logistic regression model showed a significant contribution of rMK (P = 0.006) and rCBV (P = 0.009) as independent imaging classifiers for discrimination. The combined use of rMK and rCBV improved the AUC to 0.924 (P < 0.001) and the diagnostic accuracy to 88.24%. Conclusion: DKI may be a valuable non-invasive tool in differentiating glioma recurrence from pseudoprog-ression, and its use in combination with DSC MRI can improve diagnostic performance in assessing treatment response compared with either technique alone.

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