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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
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Anatomo-radiological correlation between diffusion tensor imaging and histologic analyses of glial tumors: a
Henri-Arthur Leroy1,2, M Lacoste3, C-A Maurage4
1Department of Neurosurgery, CHU Lille, Univ. Lille, F-59000, Lille, France. henriarthurleroy@gmail.com.
Acta Neurochirurgica
|April 16, 2020
Summary
Diffusion tensor imaging (DTI) accurately predicts white matter tract disruption in gliomas by correlating with histology. This method aids in surgical planning and predicting tumor grade and outcomes.
Area of Science:
- Neurosurgery
- Neuroimaging
- Oncology
Background:
- Neurosurgical management of gliomas aims for maximal resection while preserving function.
- Diffusion tensor imaging (DTI) non-invasively identifies white matter tracts and tumor interactions.
- Previous DTI validation relied on intraoperative stimulation, not histopathology.
Purpose of the Study:
- To evaluate the correlation between preoperative DTI tractography and glioma histology.
- To assess DTI's accuracy in identifying fiber direction and tumor-related disruption.
- To validate DTI against histopathological analysis for subcortical fiber integrity.
Main Methods:
- Eleven patients with glial tumors underwent preoperative DTI.
- Correlations were made between FA maps and post-resection tumor histology.
- Thirty-one tumor areas were classified by infiltration, myelin destruction, and fiber organization.
Main Results:
- DTI demonstrated 89% sensitivity and 90% specificity for predicting disrupted tracts.
- DTI aligned with histologic fiber orientation in 90% of patients.
- Glioblastoma (WHO grade IV) showed higher white matter destruction than lower grades.
Conclusions:
- High concordance exists between DTI FA maps and histology for predicting subcortical fiber disruption.
- DTI data can be routinely used in glioma surgery for predicting tumor grade and outcomes.
- This study provides consistency for DTI's role in neurosurgical oncology.
