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A novel radiological classification system for cerebral gliomas: The Brain-Grid
PURPOSE: Standard radiological/topographical classifications of gliomas often do not reflect the real extension of the tumor within the lobar-cortical anatomy. Furthermore, these systems do not provide information on the relationship between tumor growth and the subcortical white matter architecture...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345500/ https://www.ncbi.nlm.nih.gov/pubmed/30677090 http://dx.doi.org/10.1371/journal.pone.0211243 |
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author | Latini, Francesco Fahlström, Markus Berntsson, Shala G. Larsson, Elna-Marie Smits, Anja Ryttlefors, Mats |
author_facet | Latini, Francesco Fahlström, Markus Berntsson, Shala G. Larsson, Elna-Marie Smits, Anja Ryttlefors, Mats |
author_sort | Latini, Francesco |
collection | PubMed |
description | PURPOSE: Standard radiological/topographical classifications of gliomas often do not reflect the real extension of the tumor within the lobar-cortical anatomy. Furthermore, these systems do not provide information on the relationship between tumor growth and the subcortical white matter architecture. We propose the use of an anatomically standardized grid system (the Brain-Grid) to merge serial morphological magnetic resonance imaging (MRI) scans with a representative tractographic atlas. Two illustrative cases are presented to show the potential advantages of this classification system. METHODS: MRI scans of 39 patients (WHO grade II and III gliomas) were analyzed with a standardized grid created by intersecting longitudinal lines on the axial, sagittal, and coronal planes. The anatomical landmarks were chosen from an average brain, spatially normalized to the Montreal Neurological Institute (MNI) space and the Talairach space. Major white matter pathways were reconstructed with a deterministic tracking algorithm on a reference atlas and analyzed using the Brain-Grid system. RESULTS: In all, 48 brain grid voxels (areas defined by 3 coordinates, axial (A), coronal (C), sagittal (S) and numbers from 1 to 4) were delineated in each MRI sequence and on the tractographic atlas. The number of grid voxels infiltrated was consistent, also in the MNI space. The sub-cortical insula/basal ganglia (A3-C2-S2) and the fronto-insular region (A3-C2-S1) were most frequently involved. The inferior fronto-occipital fasciculus, anterior thalamic radiation, uncinate fasciculus, and external capsule were the most frequently associated pathways in both hemispheres. CONCLUSIONS: The Brain-Grid based classification system provides an accurate observational tool in all patients with suspected gliomas, based on the comparison of grid voxels on a morphological MRI and segmented white matter atlas. Important biological information on tumor kinetics including extension, speed, and preferential direction of progression can be observed and even predicted with this system. This novel classification can easily be applied to both prospective and retrospective cohorts of patients and increase our comprehension of glioma behavior. |
format | Online Article Text |
id | pubmed-6345500 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63455002019-02-02 A novel radiological classification system for cerebral gliomas: The Brain-Grid Latini, Francesco Fahlström, Markus Berntsson, Shala G. Larsson, Elna-Marie Smits, Anja Ryttlefors, Mats PLoS One Research Article PURPOSE: Standard radiological/topographical classifications of gliomas often do not reflect the real extension of the tumor within the lobar-cortical anatomy. Furthermore, these systems do not provide information on the relationship between tumor growth and the subcortical white matter architecture. We propose the use of an anatomically standardized grid system (the Brain-Grid) to merge serial morphological magnetic resonance imaging (MRI) scans with a representative tractographic atlas. Two illustrative cases are presented to show the potential advantages of this classification system. METHODS: MRI scans of 39 patients (WHO grade II and III gliomas) were analyzed with a standardized grid created by intersecting longitudinal lines on the axial, sagittal, and coronal planes. The anatomical landmarks were chosen from an average brain, spatially normalized to the Montreal Neurological Institute (MNI) space and the Talairach space. Major white matter pathways were reconstructed with a deterministic tracking algorithm on a reference atlas and analyzed using the Brain-Grid system. RESULTS: In all, 48 brain grid voxels (areas defined by 3 coordinates, axial (A), coronal (C), sagittal (S) and numbers from 1 to 4) were delineated in each MRI sequence and on the tractographic atlas. The number of grid voxels infiltrated was consistent, also in the MNI space. The sub-cortical insula/basal ganglia (A3-C2-S2) and the fronto-insular region (A3-C2-S1) were most frequently involved. The inferior fronto-occipital fasciculus, anterior thalamic radiation, uncinate fasciculus, and external capsule were the most frequently associated pathways in both hemispheres. CONCLUSIONS: The Brain-Grid based classification system provides an accurate observational tool in all patients with suspected gliomas, based on the comparison of grid voxels on a morphological MRI and segmented white matter atlas. Important biological information on tumor kinetics including extension, speed, and preferential direction of progression can be observed and even predicted with this system. This novel classification can easily be applied to both prospective and retrospective cohorts of patients and increase our comprehension of glioma behavior. Public Library of Science 2019-01-24 /pmc/articles/PMC6345500/ /pubmed/30677090 http://dx.doi.org/10.1371/journal.pone.0211243 Text en © 2019 Latini et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Latini, Francesco Fahlström, Markus Berntsson, Shala G. Larsson, Elna-Marie Smits, Anja Ryttlefors, Mats A novel radiological classification system for cerebral gliomas: The Brain-Grid |
title | A novel radiological classification system for cerebral gliomas: The Brain-Grid |
title_full | A novel radiological classification system for cerebral gliomas: The Brain-Grid |
title_fullStr | A novel radiological classification system for cerebral gliomas: The Brain-Grid |
title_full_unstemmed | A novel radiological classification system for cerebral gliomas: The Brain-Grid |
title_short | A novel radiological classification system for cerebral gliomas: The Brain-Grid |
title_sort | novel radiological classification system for cerebral gliomas: the brain-grid |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6345500/ https://www.ncbi.nlm.nih.gov/pubmed/30677090 http://dx.doi.org/10.1371/journal.pone.0211243 |
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