Haopeng Pang1, Yan Ren1, Zhenwei Yao1, Jingsong Wu1, Chengjun Yao1, Xuefei Dang2, Yong Zhang3, and Xiaoyuan Feng1
1Affiliated Huashan Hospital of Fudan University, Shanghai, China, People's Republic of, 2The 307th Hospital of Chinese People’s liberation Army, Beijing, China, People's Republic of, 3MR Research China, GE Healthcare, Beijing, China, People's Republic of
Synopsis
This study provided a new non-invasive method to better discriminate between high-grade
gliomas and primary nervous system lymphomas. We found the kurtosis parameters
(MK and K//)
showed more obvious differences that can be used for differentiating between
these two types of tumors than diffusion parameters (FA, MD, λ// and λ⊥).
The ROC curve analysis showed MK and K// had the largest area under curve,
which further confirmed that the kurtosis parameters MK and K// could better separate these
tumors than traditional diffusion parameters.Purpose
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 to the solid parts of 20 HGGs and 11
PCNSLs [median (95% confidence interval, 95% 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 to
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].
Discussion and Conclusion
The diffusional parameters FA, λ
// and λ⊥ obtained on DKI did not show significant differences
between HGGs and PCNSLs. Conversely, the kurtosis metrics MK and K
// obtained on DKI in
primary cerebral lymphoma were significantly higher than those of high-grade
glioma. Thus, kurtosis parameters obtained on DKI are better for
differentiating PCNSLs from HGGs than diffusional metrics.
Acknowledgements
No acknowledgement found.References
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Diffusional kurtosis imaging: The quantification of non-gaussian water
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