ArticleMagnetic resonance imaging2016
Imaging signatures of meningioma and low-grade glioma: a diffusion tensor, magnetization transfer and quantitative longitudinal relaxation time MRI study.
Article in Magnetic resonance imaging, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of TFrontiers in oncology · 2022Pooled it
- Assessing the Validity of Diffusion Weighted Imaging Models: A Study in Patients with Post-Surgical Lower-Grade Glioma.Journal of clinical medicine · 2025Article
- An Exploratory Study: Can Native T1 Mapping Differentiate Sarcoma from Benign Soft Tissue Tumors at 1.5 T and 3 T?Cancers · 2024Article
- Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review.The neuroradiology journal · 2024Review
- Preliminary Application of Magnetization Transfer Imaging in the Study of Normal Uterus and Uterine Lesions.Frontiers in oncology · 2022Article
- Whole-brain 3D MR fingerprinting brain imaging: clinical validation and feasibility to patients with meningioma.Magma (New York, N.Y.) · 2021Article
- The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma.The British journal of radiology · 2018Article
- Transverse relaxation of cerebrospinal fluid depends on glucose concentration.Magnetic resonance imaging · 2017Article
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6 authors.
Funding
Abstract
Differentiation of cerebral tumor pathology currently relies on interpretation of conventional structural MRI and in some cases histology. However, more advanced MRI methods may provide further insight into the organization of cerebral tumors and have the potential to aid diagnosis. The objective of this study was to use multimodal quantitative MRI to measure the imaging signatures of meningioma and low-grade glioma (LGG). Nine adults with meningioma and 11 with LGG were identified, and underwent standard structural, quantitative longitudinal relaxation time (T1) mapping, magnetization transfer and diffusion tensor MRI. Maps of mean (〈D〉), axial (λAX) and radial (λRAD) diffusivity, fractional anisotropy (FA), magnetization transfer ratio (MTR) and T1 were generated on a voxel-by-voxel basis. Using structural and echo-planar T2-weighted MRI, manual region-of-interest segmentation of brain tumor, edema, ipsilateral and contralateral normal-appearing white matter (NAWM) was performed. Differences in imaging signatures between the different tissue types, both absolute mean values and ratios relative to contralateral NAWM, were assessed using t-tests with statistical significance set at p<0.05. For both absolute mean values and ratios relative to contralateral NAWM, there were significant differences in 〈D〉, λAX, λRAD, FA, MTR and T1 between meningioma and LGG tumor tissue, respectively. Only T1 and FA differed significantly between edematous tissue associated with the two tumor types. These results suggest that multimodal MRI biomarkers are significantly different, particularly in tumor tissue, between meningioma and LGG. By using quantitative multimodal MRI it may be possible to identify tumor pathology non-invasively.
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