Evaluating potential of multi-parametric MRI using co-registered histology: Application to a mouse model of glioblastoma

Al-mubarak, H., Vallatos, A. , Gallagher, L., Birch, J. , Chalmers, A.J. and Holmes, W.M. (2021) Evaluating potential of multi-parametric MRI using co-registered histology: Application to a mouse model of glioblastoma. Magnetic resonance imaging, 85, pp. 121-127. (doi: 10.1016/j.mri.2021.10.030) (PMID:34687852)

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Background: Conventional MRI fails to detect regions of glioblastoma cell infiltration beyond the contrast-enhanced T1 solid tumor region, with infiltrating tumor cells often migrating along host blood vessels. Purpose: MRI is capable of generating a range of image contrasts which are commonly assessed individually by qualitative visual inspection. It has long been hypothesized that better diagnoses could be achieved by combining these multiple images, so called multi-parametric or multi-spectral MRI. However, the lack of clinical histology and the difficulties of co-registration, has meant this hypothesis has never been rigorously tested. Here we test this hypothesis, using a previously published multi-dimensional dataset consisting of registered MR images and histology. Study type: Animal Model. Subjects: Mice bearing orthotopic glioblastoma xenografts generated from a patient-derived glioblastoma cell line. Field strength/sequences: 7 Tesla, T1/T2 weighted, T2 mapping, contrast enhance T1, diffusion-weighted, diffusion tensor imaging. Assessment: Immunohistochemistry sections were stained for Human Leukocyte Antigen (probing human-derived tumor cells). To achieve quantitative MRI-tissue comparison, multiple histological slices cut in the MRI plane were stacked to produce tumor cell density maps acting as ‘ground truth’. Statistical tests: Sensitivity, specificity, accuracy and Dice similarity indices were calculated. ANOVA, t-test, Bonferroni correction and Pearson coefficients were used for statistical analysis. Results: Correlation coefficient analysis with co-registered 'ground truth' histology showed interactive regression maps had higher correlation coefficients and sensitivity values than T2W, ADC, FA, and T2map. Further, the interaction regression maps showed statistical improved detection of tumor volume. Data conclusion: Voxel-by-voxel analysis provided quantitative evidence confirming the hypothesis that mpMRI can, potentially, better distinguish between the tumor region and normal tissue.

Item Type:Articles
Additional Information:H. Al-Mubarak would like to thank the Ministry of Higher Education and Scientific Research of Iraq for financial support. Contract grant sponsor: The Brain Tumour Charity; Contract grant number: 26/160.nt.
Glasgow Author(s) Enlighten ID:Gallagher, Mrs Lindsay and Holmes, Dr William and Birch, Dr Joanna and Vallatos, Dr Antoine and Chalmers, Professor Anthony and Al-mubarak, Haitham Farooq Ibrahim
Authors: Al-mubarak, H., Vallatos, A., Gallagher, L., Birch, J., Chalmers, A.J., and Holmes, W.M.
College/School:College of Medical Veterinary and Life Sciences
College of Medical Veterinary and Life Sciences > School of Cancer Sciences
College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Magnetic resonance imaging
ISSN (Online):1873-5894
Published Online:21 October 2021
Copyright Holders:Copyright © 2021 Elsevier
First Published:First published in Magnetic Resonance Imaging 85:121-127
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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