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Glioma grading, mol...
Glioma grading, molecular feature classification, and microstructural characterization using MR diffusional variance decomposition (DIVIDE) imaging
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- Li, Sirui (författare)
- Zhongnan Hospital of Wuhan University
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- Zheng, Yuan (författare)
- United Imaging, Houston
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- Sun, Wenbo (författare)
- Zhongnan Hospital of Wuhan University
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- Lasič, Samo (författare)
- Random Walk Imaging AB
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- Szczepankiewicz, Filip (författare)
- Lund University,Lunds universitet,Diagnostisk radiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Medicinsk strålningsfysik, Lund,Institutionen för kliniska vetenskaper, Lund,MR Physics,Forskargrupper vid Lunds universitet,Multidimensional microstructure imaging,Diagnostic Radiology, (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Medical Radiation Physics, Lund,Department of Clinical Sciences, Lund,Lund University Research Groups,Random Walk Imaging AB
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- Wei, Qing (författare)
- United Imaging Healthcare, Shanghai
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- Han, Shihong (författare)
- United Imaging Healthcare, Shanghai
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- Zhang, Shuheng (författare)
- United Imaging Healthcare, Shanghai
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- Zhong, Xiaoli (författare)
- Zhongnan Hospital of Wuhan University
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- Wang, Liang (författare)
- Zhongnan Hospital of Wuhan University
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- Li, Huan (författare)
- Zhongnan Hospital of Wuhan University
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- Cai, Yuxiang (författare)
- Zhongnan Hospital of Wuhan University
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- Xu, Dan (författare)
- Zhongnan Hospital of Wuhan University
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- Li, Zhiqiang (författare)
- Zhongnan Hospital of Wuhan University
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- He, Qiang (författare)
- United Imaging Healthcare, Shanghai
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- van Westen, Danielle (författare)
- Lund University,Lunds universitet,Diagnostisk radiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Diagnostic Radiology, (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments
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- Bryskhe, Karin (författare)
- Random Walk Imaging AB
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- Topgaard, Daniel (författare)
- United Imaging Healthcare, Shanghai
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- Xu, Haibo (författare)
- Zhongnan Hospital of Wuhan University
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(creator_code:org_t)
- 2021-04-29
- 2021
- Engelska.
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 31:11, s. 8197-8207
- Relaterad länk:
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http://dx.doi.org/10...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- Classification
- Diffusion magnetic resonance imaging
- Glioma
- Isocitrate dehydrogenase
- Neuroimaging
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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- Av författaren/redakt...
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Li, Sirui
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Zheng, Yuan
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Sun, Wenbo
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Lasič, Samo
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Szczepankiewicz, ...
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Wei, Qing
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Han, Shihong
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Zhang, Shuheng
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Zhong, Xiaoli
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Wang, Liang
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Li, Huan
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Cai, Yuxiang
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Xu, Dan
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Li, Zhiqiang
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He, Qiang
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van Westen, Dani ...
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Bryskhe, Karin
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Topgaard, Daniel
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Xu, Haibo
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European Radiolo ...
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Lunds universitet