Comparing the value of DKI and DTI in detecting isocitrate dehydrogenase genotype of astrocytomas

Tan, Yan; Zhang, H.; Wang, X.; Qin, Jiaqian; Wang, L.; Yang, Guoqiang; Yan, Hao

doi:10.1016/j.crad.2018.12.004

Cited by 25 publications

(11 citation statements)

References 29 publications

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“…In our study, ADC and MD values of IDH1 wild-type group were significantly lower than that of IDH1-mutant group in LrGGs. FA showed ineffective prediction on IDH1 genotype, possibly due to the high heterogeneity in FA values for the solid component of the tumor, which is consistent with the results of Tan et al (2019) .…”

Section: Discussionsupporting

confidence: 80%

“…Many studies have found that DKI has excellent efficacy in the grading diagnosis, differential diagnosis, molecular marker prediction, and prognostic assessment of glioma ( Jiang et al, 2015 ; Pang et al, 2016 ; Hempel et al, 2018 , 2019 ; Tan et al, 2019 ; Zhang et al, 2019 ; Haopeng et al, 2020 ). A DKI parameter MK reflects the complexity of the tissue microenvironment under the assumption of non-Gaussian distribution in the organism.…”

Section: Discussionmentioning

confidence: 99%

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Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Xie

Shen

et al. 2021

Front. Neurosci.

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Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas.Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann–Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated.Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI.Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Xie

Shen

et al. 2021

Front. Neurosci.

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“…In our study, ADC and MD values of IDH1 wild-type group were signi cantly lower than that of IDH1-mutant group in LrGGs. FA showed ineffective prediction on IDH1 genotype, possibly due to the high heterogeneity in FA values for the solid component of the tumor, which is consistent with the results of Tan et al [17].…”

Section: Discussionsupporting

confidence: 90%

Assessment of IDH1 Genotype and Cell Proliferation in Gliomas Using Diffusion Magnetic Resonance Imaging

Xie

Shen

et al. 2021

Preprint

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Purpose To compare the efficacy of parameters from mono-, bi- and stretch-exponential diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) for prediction of IDH1 genotype and assessment of cell proliferation in gliomas. Methods 91 patients with pathologically confirmed gliomas underwent DWI, multi-b-value DWI and DKI/NODDI on 3.0T MRI. The ROIs were manually placed to obtain measurements including apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), orientation dispersion index (ODI) and intracellular volume fraction (ICVF). Results In LrGGs, IDH1 wild-type group showed significantly lower ADC, D, f, DDC, α, MD and higher D*, MK, ODI and ICVF values than IDH1-mutant group (P < 0.05). Among them, α has the highest AUC value (0.846). In GBMs, no difference between IDH1-mutant and IDH1 wild-type group was found. For IDH1-mutant group, all parameters, except for FA and D*, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC and DDC statistically discriminated LrGGs from GBMs (P = 0.039 and 0.046, respectively). In addition, MK has maximal correlation coefficient (r = 0.612, P < 0.001) and D* has the minimal correlation coefficient (r = 0.146, P = 0.176) with Ki-67 LI. Conclusion The α may be the most effective diffusion MRI marker for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.

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“…Unfortunately, all 4 studies were from the same institute and based on an overlapping dataset preventing a subgroup meta-analysis [9,20,34,35]. Recently, however, after the date this systematic review search was performed, two studies have been published comparing the diagnostic performance of DKI and DTI in predicting the IDH mutation status in gliomas [36,37]. Both groups considered MK and mean diffusivity (MD) as the main DKI and DTI parameters respectively and concluded that MK can identify IDH mutation status with higher diagnostic value than MD [36,37].…”

Section: Discussionmentioning

confidence: 99%

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis

Abdalla¹,

Dixon

Sanverdi

et al. 2020

Neuroradiology

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Purpose We aim to illustrate the diagnostic performance of diffusional kurtosis imaging (DKI) in the diagnosis of gliomas. Methods A review protocol was developed according to the (PRISMA-P) checklist, registered in the international prospective register of systematic reviews (PROSPERO) and published. A literature search in 4 databases was performed using the keywords 'glioma' and 'diffusional kurtosis'. After applying a robust inclusion/exclusion criteria, included articles were independently evaluated according to the QUADAS-2 tool and data extraction was done. Reported sensitivities and specificities were used to construct 2 × 2 tables and paired forest plots using the Review Manager (RevMan®) software. A random-effect model was pursued using the hierarchical summary receiver operator characteristics. Results A total of 216 hits were retrieved. Considering duplicates and inclusion criteria, 23 articles were eligible for full-text reading. Ultimately, 19 studies were eligible for final inclusion. The quality assessment revealed 9 studies with low risk of bias in the 4 domains. Using a bivariate random-effect model for data synthesis, summary ROC curve showed a pooled area under the curve (AUC) of 0.92 and estimated sensitivity of 0.87 (95% CI 0.78-0.92) in high-/low-grade gliomas' differentiation. A mean difference in mean kurtosis (MK) value between HGG and LGG of 0.22 (95% CI 0.25-0.19) was illustrated (p value = 0.0014) with moderate heterogeneity (I 2 = 73.8%). Conclusion DKI shows good diagnostic accuracy in the differentiation of high-and low-grade gliomas further supporting its potential role in clinical practice. Further exploration of DKI in differentiating IDH status and in characterising non-glioma CNS tumours is however needed. Keywords Magnetic resonance imaging. Diffusion-weighted imaging. Gliomas. Diagnosis Abbreviations ADC Apparent diffusion coefficient AUC Area under the curve CI Confidence interval CNS Central nervous system DKI Diffusional kurtosis imaging DTA Diagnostic test accuracy DTI Diffusion tensor imaging DWI Diffusion-weighted imaging HGAs High-grade astrocytomas HGG High-grade glioma IDH Isocitrate dehydrogenase LGG Low-grade glioma Keypoints 1-Evidence that DKI is a promising diagnostic tool for the assessment of CNS gliomas has been increasing. 2-Evidence that DKI is potentially generalisable across different institutions and scanning techniques. 3-This meta-analysis raises the need for establishing a standardized, widely accepted, evidence-based technique for DKI to be integrated into the routine imaging protocols of CNS gliomas.

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Comparing the value of DKI and DTI in detecting isocitrate dehydrogenase genotype of astrocytomas

Cited by 25 publications

References 29 publications

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Assessment of IDH1 Genotype and Cell Proliferation in Gliomas Using Diffusion Magnetic Resonance Imaging

The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis

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