health technology innovations in detecting ill health ... · dr t.n. arvanitis - biomedical...

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



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



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



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



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



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



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



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)



MRS Quantitation

LCmodelTM

TARQUIN

Slide 9



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



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


Dr T.N. Arvanitis - Biomedical Informatics, Signals and Systems Research Slide 12

ICA hybrid method- results (simulated)



ICA hybrid method- results (patient)

Slide 13



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



results in clinical practice

Slide 15



multi-modal imaging

Investigating children’s

cancer using functional

imaging

Metabolite maps

Metabolite profiles Diffusion imaging

Tractography

Perfusion

Quantitative imaging

Slide 16



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



the future: agent-based classifiers

Slide 18



brain tumours: current research

Slide 19



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



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



Thank you

[email protected]

Slide 22

health technology innovations in detecting ill health ... · dr t.n. arvanitis - biomedical...

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