ArticleAJNR. American journal of neuroradiology2014
Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases.
Article in AJNR. American journal of neuroradiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis.AJNR. American journal of neuroradiology · 2018Pooled it
- Pooled it
- Utilizing Diffusion Tensor Imaging to Differentiate High-Grade Gliomas and Solitary Brain Metastases.Asian journal of neurosurgery · 2025Article
- Microbubble-Enhanced Focused Ultrasound for Infiltrating Gliomas.Biomedicines · 2024Review
- Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.Frontiers in radiology · 2024Review
- Mesoscopic Assessment of Microstructure in Glioblastomas and Metastases by Merging Advanced Diffusion Imaging with Immunohistopathology.AJNR. American journal of neuroradiology · 2023Article
- Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives.Biomedicines · 2023Review
- The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases.Journal of personalized medicine · 2022Article
- Diffusion Tensor Imaging Can Discriminate the Primary Cell Type of Intracranial Metastases for Patients with Lung Cancer.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.Scientific reports · 2022Article
- Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis.Journal of oncology · 2021Article
- From Neurosurgical Planning to Histopathological Brain Tumor Characterization: Potentialities of Arcuate Fasciculus Along-Tract Diffusion Tensor Imaging Tractography Measures.Frontiers in neurology · 2021Article
- Role of Diffusion Tensor Imaging Parameters in the Characterization and Differentiation of Infiltrating and Non-Infiltrating Spinal Cord Tumors : Preliminary Study.Clinical neuroradiology · 2020Article
- Differentiation between high-grade gliomas and solitary brain metastases: a comparison of five diffusion-weighted MRI models.BMC medical imaging · 2020Article
- Quantitative evaluation of diffusion tensor imaging for clinical management of glioma.Neurosurgical review · 2020Review
- Article
- On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data.PloS one · 2020Article
- Current Applications of Diffusion Tensor Imaging and Tractography in Intracranial Tumor Resection.Frontiers in oncology · 2019Review
- Imaging in neuro-oncology.Therapeutic advances in neurological disorders · 2018Review
- Correlation of diffusion tensor imaging parameters and Gleason scores of prostate cancer.Experimental and therapeutic medicine · 2018Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeDifferentiation of glioblastomas and solitary brain metastases is an important clinical problem because the treatment strategy can differ significantly. The purpose of this study was to investigate the potential added value of DTI metrics in differentiating glioblastomas from brain metastases. MATERIALS AND
methodsOne hundred twenty-eight patients with glioblastomas and 93 with brain metastases were retrospectively identified. Fractional anisotropy and mean diffusivity values were measured from the enhancing and peritumoral regions of the tumor. Two experienced neuroradiologists independently rated all cases by using conventional MR imaging and DTI. The diagnostic performances of the 2 raters and a DTI-based model were assessed individually and combined.
resultsThe fractional anisotropy values from the enhancing region of glioblastomas were significantly higher than those of brain metastases (P < .01). There was no difference in mean diffusivity between the 2 tumor types. A classification model based on fractional anisotropy and mean diffusivity from the enhancing regions differentiated glioblastomas from brain metastases with an area under the receiver operating characteristic curve of 0.86, close to those obtained by 2 neuroradiologists using routine clinical images and DTI parameter maps (area under the curve = 0.90 and 0.85). The areas under the curve of the 2 radiologists were further improved to 0.96 and 0.93 by the addition of the DTI classification model.
conclusionsClassification models based on fractional anisotropy and mean diffusivity from the enhancing regions of the tumor can improve diagnostic performance in differentiating glioblastomas from brain metastases.
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