ArticleEuropean radiology2018
Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis.Radiation oncology (London, England) · 2020Pooled it
- Pooled it
- Preoperative prediction of brain invasion in meningiomas: a comparison of diffusion kurtosis imaging and diffusion tensor imaging.BMC neurology · 2025Article
- Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion for adult diffuse glioma genotyping.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Histopathological grades as prognostic factors of H3 K27-altered diffuse midline glioma and their prediction using multiparametric MR imaging.Neuroradiology · 2025Article
- Diffusion along the perivascular space as a potential biomarker for glioma grading and isocitrate dehydrogenase 1 mutation status prediction.Quantitative imaging in medicine and surgery · 2023Article
- Evaluation of Whole-Tumor Texture Analysis Based on MRI Diffusion Kurtosis and Biparametric VI-RADS Model for Staging and Grading Bladder Cancer.Bioengineering (Basel, Switzerland) · 2023Article
- Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading.Neuroradiology · 2023Article
- Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation.Diagnostics (Basel, Switzerland) · 2023Article
- An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.Frontiers in oncology · 2023Article
- Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.Journal of clinical medicine · 2022Article
- Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas.Neuroradiology · 2022Article
- Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging.European radiology · 2021Article
- Feasibility of generalised diffusion kurtosis imaging approach for brain glioma grading.Neuroradiology · 2021Article
- Radiomics and radiogenomics in gliomas: a contemporary update.British journal of cancer · 2021Review
- Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study.European radiology · 2021Article
- Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma.Neuroinformatics · 2021Article
- Untangling the diffusion signal using the phasor transform.NMR in biomedicine · 2020Article
- Based on Histogram Analysis: ADCScientific reports · 2020Article
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7 authors.
Funding
Abstract
objectiveTo investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading.
methodsA total of 39 glioma patients who underwent preoperative magnetic resonance imaging (MRI) were classified into low-grade (13 cases) and high-grade (26 cases) glioma groups. Parametric DKI maps were derived, and histogram metrics between low- and high-grade gliomas were analysed. The optimum diagnostic thresholds of the parameters, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were achieved using a receiver operating characteristic (ROC).
resultSignificant differences were observed not only in 12 metrics of histogram DKI parameters (P<0.05), but also in mean diffusivity (MD) and mean kurtosis (MK) values, including age as a covariate (F=19.127, P<0.001 and F=20.894, P<0.001, respectively), between low- and high-grade gliomas. Mean MK was the best independent predictor of differentiating glioma grades (B=18.934, 22.237 adjusted for age, P<0.05). The partial correlation coefficient between fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) was 0.675 (P<0.001). The AUC of the mean MK, sensitivity, and specificity were 0.925, 88.5% and 84.6%, respectively.
conclusionsDKI parameters can effectively distinguish between low- and high-grade gliomas. Mean MK is the best independent predictor of differentiating glioma grades. KEY POINTS: • DKI is a new and important method. • DKI can provide additional information on microstructural architecture. • Histogram analysis of DKI may be more effective in glioma grading.
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