The document presents insights from Professor Sir Michael Brady on the integration of imaging and data analysis in oncology, highlighting advances in liver disease detection and management. It discusses the rise of fatty liver disease and the need for innovative imaging technologies, like Perspectum's LiverMultiScan, to assess early stage liver conditions effectively and reduce the reliance on invasive biopsies. The presentation also emphasizes the importance of commercializing medical imaging research while maintaining a symbiotic relationship between academia and industry.

1. White Board to White Coats
Sir Michael Brady FRS FREng FMedSci
Professor of Oncological Imaging
Department of Oncology
University of Oxford

2. Professor of Information
Engineering, Oxford 1985-2010
Prof Oncological Imaging,
Department of Oncology, 2012-
present
Founder of:
Chairman of
My key message:
I do not have to have a split brain,
carefully keeping these activities
separate – rather, they are
symbiotic
Oxford has encouraged me to do
both

3. White Board to White Coats
max
lucite plate( )imp ( )
0
( ) ( , ) ( )exp exp
E
h h
t p sE V A t T d
µ ε µ ε
φ ε ε
− −
= ∫x x
int int fat fat
int int fat fat
( ) ( ) ( )
( ( ) ( )) ( )
h h h
h H
µ ε µ ε µ ε
µ ε µ ε µ ε
= +
= − +
Medical image fusionFatty liver disease
Breast density, x-ray
dose, and analytics
Computer-aided
detection breast

4. What is the underlying
problem?
• A wonderful target for pharma / biotech
• HEP C drugs have made Gilead - revenues for 2014 increased 122% to $24.89Bn
from $11.20Bn
• NASH market is 6X HEP C ($$), 20X incidence in West
• Imaging needed as endpoints in drug trials – Perspectum’s initial target market
36%
%% 24%
Now : 170 million
2030: 357 million
• 25-35% of Western populations have fatty liver disease (UK: 15-20 Million)
• 1/4 will develop steatohepatitis (UK 4-5 Million) Of these a substantial
fraction will develop cirrhosis and/or liver cancer
• Dame Sally Davies: liver disease is THE main priority1
Example 1: Liver disease pandemic
2000
2030
1. Davies, S.C. “Annual Report of the Chief Medical Officer, Volume One, 2011, On the State of the Public’s Health” London: Department of Health (November 2012)

5. Liver disease progression
NAFLD – Non Alcoholic Fatty Liver Disease – liver enlargement
NASH - Steatohepatitis – chronic liver inflammation
Fibrosis – scarring
Cirrhosis – liver cells destroyed
Heptocellular carcinoma
• Liver disease is the “silent killer”: largely asymptomatic
• Existing technologies can distinguish normal vs severe disease; but not
early progression which is reversible by lifestyle changes & potentially
drug intervention

6. What is the underlying
problem?
Liver disease unmet need – pharma and
biotech
NAFLD – reversible
NASH – reversible
Therapeutic targets are early stage disease
but existing technologies can only distinguish normal vs severe
disease
Perspectum’s LiverMultiscan™ can detect & stage early liver disease…
Dr. Rajarshi Banerjee
CEO
Sir Michael BradyDr. Matthew RobsonProfessor Stefan
Neubauer FMedSci
Lesson 9: Find a CEO who is a MD PhD who is driven by
commercialising his work in order to change medicine

7. Liver biopsy is the “gold standard”
Biopsy with a 20cm needle
 is painful, costly ($1K – rising to $4K in
cases of complications)
 … and samples 0.02% of the 1.5Kg
liver, that is 1/5000th of the liver
normal
cT1 = 733ms
mild disease
cT1 = 869ms
severe disease very severe disease
cT1 = 955ms
We have developed a patented MRI method enables
analysis of the whole liver avoidance of many
biopsies, and more accurate assessment of most kinds
of liver disease
cT1 s
cT1 = 1355ms

8. Average T1 is 817ms –
which is reassuringly normal
… but the T2* image shows massive
iron content (too much red meat or
wine)
LiverMultiscan™: Perspectum’s first product
… after image fusion of T1, T2*, Dixon,
the corrected T1 is 959ms,
indicative of severe disease – confirmed
on biopsy.
This fusion process T1
& T2*  cT1 is a core
patent exclusive to
Perspectum,
surrounded by a “picket
fence” of related
patents

9. LiverMultiscan™ : commercial
product within 9 months of launch
Inflammation & fibrosis
(T1)
Fat
Iron (T2*)
 pending

10. • Summary panel with normal ranges
• Images to assess heterogeneity
• Customizable
• Scan details for audit trail
Clinical report
• Automatically generated within a
minute of receiving the images
• (DICOM secondary capture)
• Can be instantly understood by
anybody familiar with liver disease

11. After weight loss:
cT1 = 783.5ms
Pre operation:
cT1 = 996.1ms
Clear change in
cT1/LIF.
No follow-up biopsy;
no clinical indication
Bariatric surgery

12. Changing the diagnostic pathway for patients
Repeat
blood tests
Liver
ultrasound
Liver clinic
appointment
Liver
biopsy
Appointment
for diagnosis
2-6 weeks 4-8 weeks 4-8 weeks 2-6 weeks 4 weeksSymptoms /
abnormal
blood tests
Saving up to 32 weeks per patient -
diagnosis & management begin earlier
Saving patients from unnecessary and
painful liver biopsy
Less disruptive to the patient’s life,
fewer visits to hospital, less anxiety
Saving over £1000 in cost per patient
16- 32
weeks
Multiparametric
MR to diagnose
and stage disease
Same day
diagnosis
The current diagnostic pathway for patients
Can we persuade the NHS & other
healthcare providers?

13. LiverMultiScan provides the basis for
longitudinal studies

14. LiverMultiscan™ vs Fibroscan
Highly
significant
difference
Severity of
NASH*
The reliable distinction, and accurate staging, of mild to severe disease is a
fundamental requirement of pharma
*biopsy “ground truth”
• Stop-press (as yet unpublished) results from OCMR
• 70 patients with suspected NASH: had LMS, Fibroscan, and biopsy*
• Fibroscan did not work in 30 of the 70 cases – primarily because the
patient was obese
• LMS worked in all 70 cases
• Comparison shown for just the 40 cases for which Fibroscan worked
(though results essentially same for all 70 with LMS)

15. Example 2: Medical image fusion
case study: MRI + PET for head/neck tumour
detection/localisation
… But Image Registration is a solved problem, right?

16. Deformable image registration
academic & reality
• Generally works reliably for the brain, but not much else
• Promising results published at conferences, but rarely
translated to routine clinical practice
• Many practical cases are poorly served in clinical
practice:
– Whole body registration
– Upper body
– Large-scale deformations, e.g. lung
– Breath-hold, e.g. liver
– Substantial differences in image configuration (e.g. breast)
– …

17. Deformable Image Registration

18. During Therapy: Quantitative Tumour Tracking
Apr 07 Oct 07 Apr 08 May 09 Nov 09

19. Quantitative Tumor Tracking
0
10
20
30
40
50
60
70
Air Fat Water Soft
Tissue
Bone
Distribution(%)
0
10
20
30
40
50
60
70
Air Fat Water Soft
Tissue
Bone
Distribution(%)
Efficient, quantitative tools for standardized and reproducible results
3.4 SUV Mean (g/ml)
6.4 SUV Max (g/ml)
5.4 SUV Peak (g/ml)
13.5 Metabolic Volume (cm3)
2.4 SUV Max Ratio to Liver
3.7SUV Mean (g/ml)
9.5SUV Max (g/ml)
7.3SUV Peak (g/ml)
11.4Metabolic Volume (cm3)
3.5SUV Max Ratio to Liver

20. During Therapy: Quantitative Tumour Tracking
0
2
4
6
8
10
12
10-2006 04-2007 11-2007 06-2008 12-2008 07-2009 01-2010
MaxofMaxSUV
Scan date
0
50
100
150
200
250
300
350
10-2006 04-2007 11-2007 06-2008 12-2008 07-2009 01-2010
SumofTotalLesion
Glycolysis
Scan date
0
2
4
6
8
10
12
10-2006 04-2007 11-2007 06-2008 12-2008 07-2009 01-2010MaxofPeakSUV
Scan date

21. CT PET
Overlaid, co-
registered PET-CT
PET-CT-MRI, saving $5M
Mirada’s deformable
registration equates to
research state-of-the-art, and
works almost always
Late breaking news: Mirada Medical + Alliance Medical have
contract to supply all PET-CT analysis for NHS
15 years after the launch of Mirada Solutions…
Lesson 7: don’t base the success of your company on the NHS

22. Radiation Therapy
Multi-modal fusion
Typically PET, CT
and/or any of 10
MRI sequences
This session and
any relevant,
previous images
Multi-atlas
contouring
Typically from a
previous case or
atlas of cases
warped onto this
patient
Dose deformation
and summation
Reduce the
uncertainty around
re-treatment
decisions by aligning
previous dose
volumes to current
planning CT
Adaptive re-planning
rapidly warp the
previous structures
to the new planning
volume

23. Example 3: Breast cancer incidence
• In developed countries, 1 in 8
women will get breast cancer at
some point
• 23% of all cancers in women –
projected to rise to 29% by 2030
• Peak incidence is women over 60
• In developing countries, including BRIC,
numbers are rising rapidly, already 500,000
cases in 2008
• Reasons: increasing urbanisation, changes
in lifestyle
• Impacting particularly on younger women
Early detection + chemo/radio/conservative surgery + risk analysis is transforming
morbidity

24. Post menopausal involution…
• Normal involution of dense tissue to
fat
• Fat is transparent to x-rays
• tumours can be seen on mammos:
98% effective in this case
• 40% of women have dense breasts,
postmenopausal, i.e. involution “abnormal”
• Mammo is only 48% effective in this case
• Perfect storm….
• Breast density is a more significant risk
factor than having a mother and sister with
breast cancer

25. 74M annually worldwide
Compare to previous mammograms
Computer-aided detection
Personalised Screening: Stratification
[5] Berg, W.A. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women
with elevated breast cancer risk. JAMA 2012, 307: 1394 – 1404.
European Union FP7 Project ASSURE, led by Nico Karssemeijer, with Matakina leading WP1 on density
Mammogram
Low density
Await next screening round
(2-3 years)
High density➔ stratification
Additional imaging
Breast MRI Breast Tomosynthesis Breast Ultrasound Molecular Breast Imaging
+
Mammography is 98% effective in fatty
breasts; but only 48% dense breasts
Lesson 10: work to replace ill-
informed debate with sound
science

26. Current Breast Density Classifications
BIRADS: Breast Imaging Reporting And Data Standards
The breast is assigned to one of 4 categories, for example:
Category 3: The breast tissue is heterogeneously dense,
which could obscure detection of small masses
(approximately 51-75% glandular)
… which is a bit like,
“please classify the cloud
state of the sky”

27. Breast Density Legislation
This is welcomed by women; but what
are clinicians supposed to report??

28. A fundamental problem …
Two of the UK’s most experienced breast radiologists each examined the two
mammograms shown, to estimate the percentage of dense tissue.
BK estimated 25%; TLS estimated 40% …. but it is the same breast!!!
Why is that?
Answer: the left image was exposed to x-rays twice as much as the right

29. Quantitative breast density
Intensity 3401
SMF 4.3cm
0.4cm
Intensity 1728
SMF 4.3cm
0.4cm
29kVp 128mAs 28kVp 67mAs
1998 to
2014
Image intensity relates to anatomy in a very complex way, making quantitative image
analysis a hard problem. Evidently, breast density is a volumetric quantity, not reliably
estimated by area measures
Ralph Highnam & I invented a sequence of solutions to this problem in 1992 “absolute
physics” (book).
2008-present: Ralph, I, Nico Karssemeijer (Nijmegen), Martin Yaffe (Toronto), and
colleagues, students, … invented the present solution “relative physics” (product)

30. A model of mammographic image formation
tube
150N

31. ( ), = tube voltage
= exposure time
= pixel size
t
s
p
V
t
A
φ x
A model of mammographic image formation
max
lucite plate( )imp ( )
0
( ) ( , ) ( )exp exp
E
h h
t p sE V A t T d
µ ε µ ε
φ ε ε
− −
= ∫x x
Energy that reaches the imaging sensor:
( ) is transfer function
(spectrum energy, image gain, ...)
T ε
tube
Radiation incident on
upper plate
Radiation incident
upon upper surface of
breast
Radiation exiting the
breast
Known
attenuation of
lucite (PMMA)
Known transfer
function to
image
Known properties of
x-ray tube & air
Kerma*
Output of a typical
mammography x-ray tube
*Kerma is an acronym for "kinetic energy released per unit mass"

32. Volumetric breast density
At this pixel, 5.8cm of fat; 0.2cm of
dense tissue
At this one, 3.6cm fat, 2.4cm of
dense tissue
Volume of Fibroglandular = sum over all pixels in the breast region of
amount of dense tissue,
and has unit of cubic centimetres (cm3)
Volumetric Breast Density = 100.0 * (Volume of Fibroglandular divided
by Volume of the Breast)
Lesson 11: medicine needs
numbers not pretty pictures

33. Example: Volpara Density Grade = BIRADS b
We had processed 4,000,000 mammograms by November
2014
Current rate is 3,000,000 per annum and rising rapidly
Nearly 200 installations in 32 countries
% DensityPressure
applied to
the breast
Personalised
radiation dose

34. Volpara Analytics: another application of breast density
• Many patients within a clinic,
region, country
• Several mammo units &
employees in a breast imaging
centre
Statistical analysis from
many images & machines
• Within an imaging centre
✓What is the distribution of densities across mammo units, for
example by manufacturer?
✓Are any of the radiographers consistently imaging differently
from the others (or established norms)?
✓Are any of the machines consistently delivering abnormal
MGD?
• Across a population
✓Is the population at this imaging centre significantly different
from others?
✓Are there ethnic differences that should be taken account of?

35. A busy centre in Florida
3 mammo systems in 3 locations
A breast ultrasound
machine is bought:
which is the best
location for it?
Dense breasts:
31% location 1
27% location 2
41% location 3
Resource allocation
Lesson 12: selling to the people
who control the budget beats
selling to those who have to
petition the budget holders…

36. Example 4: 2nd generation breast CAD
A cluster of
microcalcifications
– may be indicative
of ductal
carcinoma in situ

37. Example 4: Breast Computer-aided
detection of abnormalities

39. Every researcher has their own personal driver
Publishing papers and books is satisfying; but... our
aim has been that the results of our research are used
daily by thousands of people
Science that addresses
fundamental problems of a well
defined practical problem:
• our systems are used by
nonexperts
• have to work 24/7, 365, 99.9%
Universities don’t (and should not) build
systems within quality processes, sell, or
maintain systems
License technology Start new companies
Everyone at a conference hopes their work will
contribute “eventually” to eng practice/science
Reality
Industry doesn’t download freeware software
systems and use them for routine use
Companies very rarely pick up a published
paper, implement it, & sell it

40. Why start companies?
1. Frustration of dealing with large companies, particularly in medical
image analysis, and particularly in the UK
– 99% of Mirada’s sales are in the USA, as are Matakina’s
2. I can’t help it (Guidance, Mirada Solutions, Mirada Medical,
Matakina, ...)
3. Secure the kids’ futures yet live with academic poverty
4. The dream of a swimming pool in Provence …

41. Conclusions
• 24/7 99.999% can’t be achieved by tricks –
systems must rely upon appropriate science
• There are endless possibilities to applying
science
• There is a symbiosis between industry & science
• Youngsters want to be entrepreneur scientists
Answer:
Michael Faraday
Sir Humphrey Davy was asked “what was your greatest
scientific discovery?” Ralph Highnam,
CEO, Matakina
Styliani!!

42. 42
This is a presentation at the ABCDCAD project
workshop, on June 24, 2015, which is
supported by the Cyprus Research Promotion
Foundation's Grant ΤΠΕ/ΟΡΙΖΟ/311(ΒΙΕ)/29
and is co-funded by the Republic of Cyprus
and the European Regional Development
Fund.