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MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses

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Brain Tumors

Part of the book series: Neuromethods ((NM,volume 158))

Abstract

Morphometry refers to the quantitative study of form, which has gained popularity in neurosciences for non-invasive in vivo evaluation of the normal and aging brain through the use of neuroimaging data, and hence designated as brain morphometry. In the rapidly evolving field of neuro-oncology, morphological evaluation provided by neuroimaging studies has been a cornerstone for the initial diagnosis, classification, management, and post-treatment follow-up of brain tumors. However, it has historically relied on predominantly subjective and qualitative observations made by imaging experts based on clinical experience. The wealth of knowledge obtained through visual inspection of tumor imaging has made remarkable contributions to the field and enhanced our understanding of tumor biology and natural history; however, further developments have been hampered by the lack of robust methods for more automated and quantitative evaluation. These methods are becoming more readily available and have been fueled by breakthrough developments in imaging post-processing and artificial intelligence. In this chapter, we review past contributions and evolution of the field of brain tumor morphological evaluation as it evolves into more automated computerized methods including radiomics and deep learning.

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Abbreviations

2D :

2-dimensional

3D:

3-dimensional

AA :

Anaplastic astrocytoma

ANN :

Artificial neural network

BBB :

blood-brain barrier

BraTs:

Brain tumor segmentation

CE :

Contrast enhancement

CNS:

Central nervous system

CoLIAGe:

Co-occurrence of local anisotropic gradient orientations

CSF :

cerebrospinal fluid

CT:

Computed tomography

DTI :

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

GBCAs :

Gadolinium-based contrast agents

GBM:

Glioblastoma multiforme, or glioblastoma

GLCM :

Gray-level co-occurrence matrix

GLRLM :

Gray-level run length matrix

GRE :

Gradient echo

ICA :

Iodinated contrast agent

IDH :

Isocitrate dehydrogenase

IV :

Intravenous

KPS :

Karnofski Performance Scale

LBP :

Local binary patterns

LGG :

Low-grade glioma

MRI:

Magnetic resonance imaging

MRS:

Magnetic resonance spectroscopy

nCET :

Non-contrast-enhancing tumor

NEX:

Number of excitations

NSCLC:

Non-small cell lung cancer

OS :

Overall survival

PET :

Positron emission tomography

PFS :

Progression-free survival

PTE:

Peritumoral edema

PWI:

Perfusion-weighted imaging

ROI :

Region of interest

SE :

Spin echo

SFTA :

Segmentation-based fractal texture analysis

SWI :

Susceptibility-weighted imaging

T1W :

T1-weighted

T1W+C:

T1-weighted post contrast

T2W:

T2-weighted

T2W-FLAIR:

T2-weighted fluid-attenuated inversion recovery

TCGA :

The Cancer Genome Atlas

TCIA :

The Cancer Imaging Archive

TN :

Tumor necrosis

TSE:

Turbo spin echo

VASARI :

Visually AcceSAble Rembrandt Images

WHO:

World Health Organization

XRT:

Chemoradiation

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Acknowledgements

Research reported in this publication was supported by The Ohio Third Frontier Technology Validation Fund and The Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University.

Department of Defense Peer Reviewed Cancer Research Program (PRCRP) Career Development Award.

Dana Foundation David Mahoney Neuroimaging Program.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government.

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  1. Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, USA

    Marco C. Pinho

  2. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA

    Kaustav Bera, Niha Beig & Pallavi Tiwari

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  1. Marco C. Pinho
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Correspondence to Marco C. Pinho .

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    Giorgio Seano

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Pinho, M.C., Bera, K., Beig, N., Tiwari, P. (2021). MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses. In: Seano, G. (eds) Brain Tumors. Neuromethods, vol 158. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-0856-2_14

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