Diffusional kurtosis imaging for differentiating between high‐grade glioma and primary central nervous system lymphoma

Background The aim of this study was to assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters for differentiating high‐grade gliomas (HGGs) from primary central nervous system lymphomas (PCNSLs). Methods Diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ // ), radial diffusivity (λ ⊥ ); and kurtosis parameters, including mean kurtosis (MK), axial kurtosis (K // ), and radial kurtosis (K ⊥ ), were normalized to contralateral normal‐appearing white matter (NAWMc) to decrease inter‐individual and inter‐regional changes across the entire brain, and then compared with the solid parts of 20 HGGs and 11 PCNSLs [median 95% confidence interval (CI), P < 0.004; 0.05/14], significance level, Kolmogorov‐Smirnov test, Bonferroni correction]. Results FA, MD, λ // , and λ ⊥ values were higher in HGGs than in PCNSLs, but not significantly [HGGs: 0.209 (95% CI, 0.134–0.338), 1.385 (95% CI, 1.05–1.710), 1.655 (95% CI, 1.30–2.060), 1.228 (95% CI, 0.932–1.480), respectively; PCNSLs: 0.143 (95% CI, 0.110–0.317), 1.070 (95% CI, 0.842–1.470), 1.260 (95% CI, 0.960–1.930), 1.010 (95% CI, 0.782–1.240)], respectively; P = 0.120, 0.010, 0.004, and 0.004, respectively). However, MK and K // were significantly higher in PCNSLs compared with HGGs [PCNSLs: 0.765 (95% CI, 0.697–0.890), 0.787 (95% CI, 0.615–1.030), respectively; HGGs: 0.531 (95% CI, 0.402–0.766), 0.532 (95% CI, 0.432–0.680], respectively; P = 0.001, 0.000, respectively); but not K ⊥ [0.774 (95% CI, 0.681–0.899) for PCNSLs; 0.554 (95% CI, 0.389−0.954) for HGGs; P = 0.024]. Conclusion There were significant differences in kurtosis parameters (MK and K // ) between HGGs and PCNSLs, while differences in diffusion parameters between them did not reach significance; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters. J. Magn. Reson. Imaging 2016;44:30–40.