ArticleNeuro-oncology2014
Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas.
Article in Neuro-oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 5 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
37 citing papers in PubMed, 5 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Hydrogen Proton Magnetic Resonance Spectroscopy (MRS) in Differential Diagnosis of Intracranial Tumors: A Systematic Review.Contrast media & molecular imaging · 2022Pooled 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
- Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review.The British journal of radiology · 2018Pooled it
- Discrimination between Glioma Grades II and III Using Dynamic Susceptibility Perfusion MRI: A Meta-Analysis.AJNR. American journal of neuroradiology · 2017Pooled it
- PPAR-γ agonists reactivate the ALDOC-NR2F1 axis to enhance sensitivity to temozolomide and suppress glioblastoma progression.Cell communication and signaling : CCS · 2024Article
- Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.Journal of magnetic resonance imaging : JMRI · 2023Review
- Article
- Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis.Current oncology (Toronto, Ont.) · 2022Review
- Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.Journal of clinical medicine · 2022Article
- Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.Cancers · 2022Review
- Article
- Application of diffusion kurtosis imaging andFrontiers in oncology · 2022Article
- ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging.Journal of clinical medicine · 2021Article
- Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery.Brain sciences · 2021Article
- Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer.Frontiers in oncology · 2021Article
- Clinical Imaging for Diagnostic Challenges in the Management of Gliomas: A Review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2020Review
- CT-2A neurospheres-derived high-grade glioma in mice: a new model to address tumor stem cells and immunosuppression.Biology open · 2019Article
- Survival prediction of high-grade glioma patients with diffusion kurtosis imaging.American journal of translational research · 2019Article
- Contribution of susceptibility- and diffusion-weighted magnetic resonance imaging for grading gliomas.Experimental and therapeutic medicine · 2018Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe assessed the diagnostic accuracy of diffusion kurtosis imaging (DKI), dynamic susceptibility-weighted contrast-enhanced (DSC) MRI, and short echo time chemical shift imaging (CSI) for grading gliomas.
methodsIn this prospective study, 35 patients with cerebral gliomas underwent DKI, DSC, and CSI on a 3 T MR scanner. Diffusion parameters were mean diffusivity (MD), fractional anisotropy, and mean kurtosis (MK). Perfusion parameters were mean relative regional cerebral blood volume (rrCBV), mean relative regional cerebral blood flow (rrCBF), mean transit time, and relative decrease ratio (rDR). The diffusion and perfusion parameters along with 12 CSI metabolite ratios were compared among 22 high-grade gliomas and 14 low-grade gliomas (Mann-Whitney U-test, P < .05). Classification accuracy was determined with a linear discriminant analysis for each MR modality independently. Furthermore, the performance of a multimodal analysis is reported, using a decision-tree rule combining the statistically significant DKI, DSC-MRI, and CSI parameters with the lowest P-value. The proposed classifiers were validated on a set of subsequently acquired data from 19 clinical patients.
resultsStatistically significant differences among tumor grades were shown for MK, MD, mean rrCBV, mean rrCBF, rDR, lipids over total choline, lipids over creatine, sum of myo-inositol, and sum of creatine. DSC-MRI proved to be the modality with the best performance when comparing modalities individually, while the multimodal decision tree proved to be most accurate in predicting tumor grade, with a performance of 86%.
conclusionsCombining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.
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