Synopsis
This
lecture explains how to extract diagnostic information from the raw MR spectra
possibly combined with information from other MR modalities. Starting from
basic and advanced concepts in machine learning, the most important
classification methods are surveyed.
The
application of these classifiers in assessing brain tumor heterogeneity is
illustrated in a variety of case studies. In-vivo single-voxel MR Spectroscopy
(MRS) as well as chemical Shift Imaging are considered, long-echo time as well
as short-echo time acquisition schemes. Moreover, it is shown how to combine
these measurements with other basic (T1-weighted,T2-weighted) as well as
advanced (PWI, DWI, DTI, DKI) MR modalities.
Introduction
This lecture explains how to extract diagnostic
information from the raw MR spectra possibly combined with information from
other MR modalities. The aim is to present a variety
of machine learning methods for classification of tumours with focus on automated preprocessing and diagnosis of
abnormal brain tissues, in particular for the follow- up of glioblastoma
multiforme (GBM). Current conventional MRI (cMRI) techniques are very useful in
detecting the main features of brain tumours, such as size and location, but
are insufficient in specifying the grade or evolution of the disease.
Therefore, the acquisition of advanced MRI, such as perfusion weighted imaging
(PWI), diffusion kurtosis imaging (DKI), and magnetic resonance spectroscopic
imaging (MRSI), is necessary to provide complementary information such as blood
flow, tissue organisation, and metabolism, induced by pathological changes. In the
GBM experiments our aim is to discriminate and predict the evolution of patients
treated with standard radiochemotherapy and immunotherapy based on conventional
and advanced MRI data.Classification Tools
Starting from basic and advanced concepts in machine learning, the most
important classification methods are surveyed. Supervised (Linear Discriminant
Analysis, Neural Networks and Support Vector Machines [1]), as well as
unsupervised (K-means, blind source separation using nonnegativity constraints)
ones are described, up to their embedding in Decision Support Systems (SpectraClassifier
[2]). Their potential and limitations, in particular regarding their use in clinical practice, are
investigated.Case Studies
The application of these classifiers in assessing brain tumor
heterogeneity is illustrated in a variety of case studies. In-vivo single-voxel
MR Spectroscopy (MRS) as well as chemical Shift Imaging are considered,
long-echo time as well as short-echo time acquisition schemes. Moreover, it is
shown how to combine these measurements with other basic (T1-weighted,T2-weighted)
as well as advanced (PWI, DWI, DTI, DKI) MR modalities. The latter give
additional information about various tissue characteristics and can help with
typing and grading of brain tumours.
We first show how to extract meaningful features from the low
sensitivity MR spectroscopy in-vivo data. MR spectroscopy and MR spectroscopic
imaging can provide concentrations of metabolites that act as biomarkers for
brain tumour characterisation.
We then show how to use MR spectroscopic imaging
directly in classification approaches in order to describe normal and
pathologic tissue types in the brain.
Starting from a patient’s (multiparametric) MR imaging
and an unsupervised classifier (such as blind source separation using
nonnegative matrix and tensor factorisations), allows to discover almost
automatically tissue specific spectral patterns in each individual glioma
patient [3]. Combined with nosologic imaging [4], tumor heterogeneity can be
easily visualized, which opens a promising path towards automated brain tumour
recognition. Based on metabolic characteristics, but also on the perfusion and
diffusion MRI parameters, we can accurately differentiate tissue types in the
brain, which can help in tumour grading and in finding regions where the tumour
is more aggressive.
Finally,
we show how to perform supervised classification in order to predict recurrence
in high grade glioma patients in their post-surgery follow-up [5][6].Acknowledgements
Research supported by FWO project G.0869.12N, ERC
Advanced Grant: BIOTENSORS (n°339804) and EU MC ITN TRANSACT 2012 (n°316679).References
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classification problems in chemometrics'', Analytica
Chimica Acta 665:129-145.
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et al (2010), SpectraClassifier 1.0:
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(2015), ``Tumour relapse prediction using multi-parametric MR data recorded
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(2017), “Classifying glioblastoma multiforme follow-up progressive vs.
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