Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
Health technology innovations in
detecting ill health: imaging biomarkers for cancer
Slide 1
a metabolomics perspective in the future of
diagnostic and preventive medicine
Theodoros N. Arvanitis, RT, DPhil, CEng, MIET, MIEEE, AMIA, FSIM, FRSM
Biomedical Informatics, Signals & Systems Research Laboratory School of Electronic, Electrical & Computer Engineering
College of Engineering and Physical Sciences University of Birmingham
Birmingham Children’s Hospital NHS Foundation Trust
Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
the problem
Childhood Cancers in the UK Childhood Cancer Survival in UK
Slide 2
Reproduced by permission from the Office for National Statistics, UK
Brain tumours are the commonest cause of death among
childhood cancer sufferers and figures from brain tumours
research show that the number of children dying from brain
tumours was 33% higher in 2007 than 2001.
Reproduced by permission from the Cancer Research UK
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
magnetic resonance spectroscopy (MRS):
in vivo biochemistry
• Magnetic Resonance Spectroscopy:
a functional “imaging” technique
• Provides a non-invasive way in
observing the biochemical
processes in humans
– Recent advances in technology have
enabled the acquisition of in vivo
spectra from smaller volumes of
tissue
– A more clinical relevant utility in the
study of specific disorders: Stress,
functional disorders, or diseases can
cause the metabolite concentration to
vary
– However …
– Metabolite concentrations are low,
generating ~10,000 times less signal
intensity than the water signal
Slide 3
Reproduced by permission from R.R. Edelman, J. R. Hesselink, M. B. Zlatkin, J. V. Crues (2006),
Clinical Magnetic Resonance Imaging, 3rd edition, Philadelphia (PA): Saunders-Elsevier
Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
magnetic resonance spectroscopy as a non-invasive diagnostic tool
• Good evidence that MRS varies with histology of brain tumours
• Pruel et al. (Nature Med, 1996) - correctly predicted histology of 90 out of 91 adult brain tumours
• Used pattern recognition for analysis
• Overall Research Hypothesis: MRS a good diagnostic and prognostic tool for childhood brain tumours
• SVS - Single Voxel Spectroscopy
MR Spectroscopy MR Imaging
Slide 4
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
childhood brain:
interpreting MR Spectra
• Myo-Inositol (mIns) -
role uncertain
• tCholine (tCho) - cell
turnover
• Creatine (Cr) - energy
status
• N-Acetyl-Aspartate
(NAA) - neurons
• Lipids/Macromolecule
(LMM) - apoptosis and
necrosis
1H MRS from a normal brain SVS
at TE = 30 ms on a 1.5 T Magnet
A.C. Peet, T. N. Arvanitis, D. P. Auer et al.
Archives of Disease in Childhood 2008;93:725-727
Slide 5
Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
paediatric cerebellar tumours (I)
• A paediatric clinical example of in-vivo metabolomics
• Most common paediatric cerebellar tumours types: low grade astrocytoma, ependymoma and medulloblastoma
• Treatment strategies and prognosis vary greatly depending on diagnosis
• Hypothesis: Single-Voxel MRS offers great potential for non-invasive biological characterisation and an increase in diagnostic accuracy over current methods
Slide 6 NAA
Lac/Lipids Gua?
Ala
GPC/PCh
Cr -CrCH2 Glu/Gln Ins Tau
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
paediatric cerebellar tumours (II)
Mean MR spectra for cerebellar tumour types
(Scale is expanded by factor of 2 in astrocytoma spectrum, shaded region = 95% confidence intervals)
Slide 7
Low Grade Astrocytoma N=12
Ependymoma N=4
Medulloblastoma N=17
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
paediatric cerebellar tumours (III)
Slide 8
Spectral classification: success rate = 91%
(left) LDA plot of first 4 principal components of MR spectra (right) the canonical coefficients of the spectral components
(shaded region = 95% confidence intervals)
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
MRS Quantitation
LCmodelTM
TARQUIN
Slide 9
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
ICA: an alternative solution?
An alternative approach which automatically decomposes a dataset of
spectra into their component metabolite signals would be a useful
advance in the classification of tumours by their MRS profiles.
Hypothesis: Independent component analysis (ICA) has the potential
of determining automatically the metabolite signals which make up
MR spectra.
ICA: “Independent component analysis (ICA) is a method for finding
underlying factors or components from multivariate (multi-
dimensional) statistical data. What distinguishes ICA from other
methods is that it looks for components that are both statistically
independent, and nonGaussian.”
A.Hyvarinen, A.Karhunen, E.Oja (2001), Independent Component Analysis, Wiley-Blackwell: New York.
Slide 10
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
assumptions
• 1H MR Spectra can be considered
as a linear mixture of metabolite,
macromolecular and lipid (MMLip)
components with noise.
– assumption of independence
• MR spectra dataset, have super-
Gaussian distributions.
– calculating the kurtosis of an
MRS dataset has a value
greater than zero, which
proves it has a super-Gaussian
distribution ( assumption of
nonGaussian components)
• A super-Gaussian distribution dataset
has a probability density peaked at
zero and has heavy tails when
compared to a Gaussian density of the
same variance.
Figure: Example of he pdf of the Laplace distribution,
which is a typical super-Gaussian distribution. For
comparison, the Gaussian pdf is given by a dashed line.
Both densities are normalized at unit variance.
Slide 11
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research Slide 12
ICA hybrid method- results (simulated)
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
ICA hybrid method- results (patient)
Slide 13
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
ROC analysis to identify cut-offs
Slide 14
Area Under ROC Curve (AUC)
Ratio Astrocytoma Vs. all Ependymoma Vs.
All
Medulloblastoma Vs. All Ependymoma Vs.
Medulloblastoma
Cr/Cho 0.785 0.924 0.636 0.890
NAA/Cho 0.889 0.527 0.761 0.688
Ins/Cho 0.618 0.935 0.503 0.906
NAA/Cr 0.964 0.810 0.594 0.789
Ins/Cr 0.554 0.712 0.591 0.805
Ins/NAA 0.850 0.946 0.676 0.923
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
results in clinical practice
Slide 15
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
multi-modal imaging
Investigating children’s
cancer using functional
imaging
Metabolite maps
Metabolite profiles Diffusion imaging
Tractography
Perfusion
Quantitative imaging
Slide 16
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
the future: need for large data sets
Slide 17
HR-MAS
NAA
Cho
Cr
mI Lip+Lac
w2 1H
w1 1
3C
1H-13C HSQC
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
the future: agent-based classifiers
Slide 18
Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
brain tumours: current research
Slide 19
Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
acknowledgments: cancer research
• Investigators
– Andrew Peet – UB PI, oncology, MRS
– Theo Arvanitis – UB bio-informatics
– Richard Grundy – UN oncology, biology
– Martin Leach – ICR MR Physics, perfusion
– Chris Clark – ICH MR Physics, diffusion
– Franklyn Howe – StGUL MR Physics, MRS
Collaborators • Professor Dr Dorothee Auer University of
Nottingham
• Dr Thomas Barrick St George's Hospital Medical School
• Mr David Collins Royal Marsden Hospital
• Dr Daniel Ford University Hospital Birmingham
• Dr Darren Hargrave Royal Marsden Hospital
• Mr Donald Macarthur Nottingham University Hospitals NHS Trust
• Dr Lesley MacPherson Birmingham Children’s Hospital NHS Trust
• Dr Paul Morgan University of Nottingham
• Dr Kal Natarajan University Hospital Birmingham
• Dr Oystein Olsen Great Ormond Street Hospital
• Dr Geoffrey S Payne The Institute of Cancer Research
• Professor Andy Pearson Royal Marsden Hospital
• Dr Sucheta Vaidya St George's Hospital Medical School
• Dr Tim Jaspan, University Hospital, Nottingham
• Dr Dawn, Saunders, Great Ormond Street Hospital
Research Group: Nigel Davies, Martin Wilson, Kal Natarajan, Yu Sun, Eleni Orphanidou, Lisa Harris,
Greg Reynolds, Sim Gill, Alex Gibb, M. Saleh,
Suchada Tantisatirapong, Ben Babourina-Brooks,
Jan Novak
Slide 20
Digital Health: design: develop: deploy: evaluate IDH July 2013
Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
realising our imagination
Slide 21
"Leave the beaten track occasionally and dive into the woods. Every time you do so, you will be certain to find something that you have never seen before. Follow it up, explore all around it, and before you know it, you will have something worth thinking about to occupy your mind. All really big discoveries are the result of thought."
Alexander Graham Bell: "electrical
speech machine" of 1876
http://www.chatsubo.com/fitzgerald/bell/inventor.html
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Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research
Thank you
Slide 22