Tissue-type mapping of gliomas

Raschke, Felix, Barrick, Thomas/R, Jones, Timothy/L , Yang, Guang, Ye, Xujiong and Howe, Franklyn/A (2019) Tissue-type mapping of gliomas. NeuroImage: Clinical, 21 . p. 101648. ISSN 2213-1582

Full content URL: https://doi.org/10.1016/j.nicl.2018.101648

Tissue-type mapping of gliomas

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Abstract Purpose
To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins.

Materials and Methods
We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n=25). 1H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n=10) and brain metastasis patients (n=10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T2-weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps.

Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p<0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n=16) tumours, in grade II gemistocytic rich astrocytomas (n=3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n=20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p<0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival > 2 years (Mann Witney p=0.0001). Regions classified from mMRI as oedema had non-tumour-like 1H MRS characteristics.

1H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.

Keywords:Magnetic resonance spectroscopy (MRS), multimodal MRI, glioma, nosologic imaging, pattern recognition
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:34616
Deposited On:11 Jan 2019 15:06

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