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Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging

Li, Sirui ; Zheng, Yuan ; Sun, Wenbo ; Lasič, Samo ; Szczepankiewicz, Filip LU orcid ; Wei, Qing ; Han, Shihong ; Zhang, Shuheng ; Zhong, Xiaoli and Wang, Liang , et al. (2021) In European Radiology 31(11). p.8197-8207
Abstract

Objective: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. Materials and methods: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG)... (More)

Objective: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. Materials and methods: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), total mean kurtosis (MKT), MKA/MKT, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. Results: FA, MD, MKA, MKI, MKT, μFA, and MKA/MKT differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p < 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p < 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MKI: p = 0.003; others: p < 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MKT and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MKI had the highest correlation coefficient (rs = 0.618). Conclusion: DIVIDE is a promising technique for glioma characterization and diagnosis. Key Points: • DIVIDE metrics MKIis related to cell density heterogeneity while MKAand μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MKIwas larger than MKAwhich indicates predominant cell density heterogeneity in gliomas. • MKAand MKIincreased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MKAin relation to the cell density heterogeneity metric MKI.

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@article{61429dfa-8112-4e63-aea8-0048299bb886,
  abstract     = {{<p>Objective: To evaluate the potential of diffusional variance decomposition (DIVIDE) for grading, molecular feature classification, and microstructural characterization of gliomas. Materials and methods: Participants with suspected gliomas underwent DIVIDE imaging, yielding parameter maps of fractional anisotropy (FA), mean diffusivity (MD), anisotropic mean kurtosis (MK<sub>A</sub>), isotropic mean kurtosis (MK<sub>I</sub>), total mean kurtosis (MK<sub>T</sub>), MK<sub>A</sub>/MK<sub>T</sub>, and microscopic fractional anisotropy (μFA). Tumor type and grade, isocitrate dehydrogenase (IDH) 1/2 mutant status, and the Ki-67 labeling index (Ki-67 LI) were determined after surgery. Statistical analysis included 33 high-grade gliomas (HGG) and 17 low-grade gliomas (LGG). Tumor diffusion metrics were compared between HGG and LGG, among grades, and between wild and mutated IDH types using appropriate tests according to normality assessment results. Receiver operating characteristic and Spearman correlation analysis were also used for statistical evaluations. Results: FA, MD, MK<sub>A</sub>, MK<sub>I</sub>, MK<sub>T</sub>, μFA, and MK<sub>A</sub>/MK<sub>T</sub> differed between HGG and LGG (FA: p = 0.047; MD: p = 0.037, others p &lt; 0.001), and among glioma grade II, III, and IV (FA: p = 0.048; MD: p = 0.038, others p &lt; 0.001). All diffusion metrics differed between wild-type and mutated IDH tumors (MK<sub>I</sub>: p = 0.003; others: p &lt; 0.001). The metrics that best discriminated between HGG and LGGs and between wild-type and mutated IDH tumors were MK<sub>T</sub> and FA respectively (area under the curve 0.866 and 0.881). All diffusion metrics except FA showed significant correlation with Ki-67 LI, and MK<sub>I</sub> had the highest correlation coefficient (r<sub>s</sub> = 0.618). Conclusion: DIVIDE is a promising technique for glioma characterization and diagnosis. Key Points: • DIVIDE metrics MK<sub>I</sub>is related to cell density heterogeneity while MK<sub>A</sub>and μFA are related to cell eccentricity. • DIVIDE metrics can effectively differentiate LGG from HGG and IDH mutation from wild-type tumor, and showed significant correlation with the Ki-67 labeling index. • MK<sub>I</sub>was larger than MK<sub>A</sub>which indicates predominant cell density heterogeneity in gliomas. • MK<sub>A</sub>and MK<sub>I</sub>increased with grade or degree of malignancy, however with a relatively larger increase in the cell eccentricity metric MK<sub>A</sub>in relation to the cell density heterogeneity metric MK<sub>I</sub>.</p>}},
  author       = {{Li, Sirui and Zheng, Yuan and Sun, Wenbo and Lasič, Samo and Szczepankiewicz, Filip and Wei, Qing and Han, Shihong and Zhang, Shuheng and Zhong, Xiaoli and Wang, Liang and Li, Huan and Cai, Yuxiang and Xu, Dan and Li, Zhiqiang and He, Qiang and van Westen, Danielle and Bryskhe, Karin and Topgaard, Daniel and Xu, Haibo}},
  issn         = {{0938-7994}},
  keywords     = {{Classification; Diffusion magnetic resonance imaging; Glioma; Isocitrate dehydrogenase; Neuroimaging}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{11}},
  pages        = {{8197--8207}},
  publisher    = {{Springer}},
  series       = {{European Radiology}},
  title        = {{Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging}},
  url          = {{http://dx.doi.org/10.1007/s00330-021-07959-x}},
  doi          = {{10.1007/s00330-021-07959-x}},
  volume       = {{31}},
  year         = {{2021}},
}