White Board to White Coats

Sir Michael Brady FRS FREng FMedSci Professor of Oncological Imaging

Department of Oncology University of Oxford

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

White Board to White Coats max

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Medical image fusion Fatty liver disease Breast density, x-ray dose, and analytics

Computer-aided detection breast

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)

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

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 Brady Dr. Matthew Robson

Professor 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

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

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

LiverMultiscan™ : commercial product within 9 months of launch

Inflammation & fibrosis (T1)

Fat

Iron (T2*)

pending

• 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

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

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 weeks Symptoms / 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?

LiverMultiScan provides the basis for longitudinal studies

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)

Example 2: Medical image fusion case study: MRI + PET for head/neck tumour

detection/localisation

… But Image Registration is a solved problem, right?

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) – …

Deformable Image Registration

During Therapy: Quantitative Tumour Tracking

Apr 07 Oct 07 Apr 08 May 09 Nov 09

Quantitative Tumor Tracking

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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

3.7 SUV Mean (g/ml)

9.5 SUV Max (g/ml)

7.3 SUV Peak (g/ml)

11.4 Metabolic Volume (cm3)

3.5 SUV Max Ratio to Liver

During Therapy: Quantitative Tumour Tracking

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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

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

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

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

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

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”

Breast Density Legislation

This is welcomed by women; but what are clinicians supposed to report??

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

Quantitative breast density

Intensity 3401 SMF 4.3cm

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Intensity 1728 SMF 4.3cm

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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)

A model of mammographic image formation

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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"

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

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

% Density Pressure applied to the breast

Personalised radiation dose

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?

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…

Example 4: 2nd generation breast CAD

A cluster of microcalcifications – may be indicative of ductal carcinoma in situ

Example 4: Breast Computer-aided detection of abnormalities

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

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 …

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!!

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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.