Date Published: January 24, 2019
Publisher: Public Library of Science
Author(s): Francesco Latini, Markus Fahlström, Shala G. Berntsson, Elna-Marie Larsson, Anja Smits, Mats Ryttlefors, Joseph Najbauer.
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.
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.
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.
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.
Gliomas comprise approximately 30% of all primary CNS tumors and 80% of malignant brain tumors [1, 2]. Low-grade gliomas are WHO grade II tumors and are characterized by slow growth but extensive infiltration. They occur mainly in adult life, with a peak incidence around 30–40 years. The clinical course of low-grade gliomas is diverse, but all tumors transform into high-grade gliomas and will eventually lead to death .
We present a novel, easily applicable Brain-Grid system for standardized radiological classification and longitudinal observation of intracerebral gliomas. Cortical anatomy, subcortical white matter bundles, and gliomas are integrated through an anatomical normalization of brain images.
The Brain-Grid classification system provides an accurate and rapid classification of patients with gliomas that includes both cortical and subcortical anatomy. Important information about the tumor kinetics including extension and preferential direction of tumor invasion can be observed and predicted by a comparative analysis of voxels on morphological MRI and a white matter architecture atlas like that included in the Brain-Grid classification system. This new integrated classification of gliomas can potentially help clinicians to plan tailored tumor resection and target volume for radiotherapy based on a prediction of white matter invasion.