Download - White Board to White Coats
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White Board to White Coats
Sir Michael Brady FRS FREng FMedSci Professor of Oncological Imaging
Department of Oncology University of Oxford
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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
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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
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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)
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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
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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
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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
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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
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LiverMultiscan™ : commercial product within 9 months of launch
Inflammation & fibrosis (T1)
Fat
Iron (T2*)
pending
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• 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
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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
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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?
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LiverMultiScan provides the basis for longitudinal studies
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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)
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Example 2: Medical image fusion case study: MRI + PET for head/neck tumour
detection/localisation
… But Image Registration is a solved problem, right?
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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) – …
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Deformable Image Registration
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During Therapy: Quantitative Tumour Tracking
Apr 07 Oct 07 Apr 08 May 09 Nov 09
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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
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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
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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
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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
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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
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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
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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”
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Breast Density Legislation
This is welcomed by women; but what are clinicians supposed to report??
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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
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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)
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A model of mammographic image formation
tube
150N
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( ), = tube voltage = exposure time = pixel size
t
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p
VtA
φ x
A model of mammographic image formation
maxlucite plate( )imp ( )
0
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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"
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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
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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
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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?
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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…
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Example 4: 2nd generation breast CAD
A cluster of microcalcifications – may be indicative of ductal carcinoma in situ
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Example 4: Breast Computer-aided detection of abnormalities
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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
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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 …
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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.