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Drug Testing and Risk
Prediction using PET/CT
Dr James Rudd
Senior Lecturer
Honorary Consultant Cardiologist
Addenbrooke’s Hospital
Cambridge, UK
jhfr2@cam.ac.uk
@jhfrudd
THE CHALLENGES
Facts:
• Cardiovascular disease kills 35% of people
• Heart attack and stroke prediction is poor
• Incomplete understanding of underlying pathology
• Developing and testing new therapies is costly and
time-consuming
HEART ATTACK
HEART ATTACK
WHAT IS A HIGH-RISK PLAQUE?
Inflammation
Microcalcification
Positive
Remodeling
Vancraeynest et al, JACC 2011
Thin
Fibrous
Cap
Large
Necrotic
Core
Hypoxia
MULTIPLE OPTIONS FOR IMAGING ATHEROMA
Ultrasound: Wall dimensions
MRI: Composition
Rudd JHF, ATVB 2009
CT: Stenosis, calcium
PET/CT: 18FDG uptake in vessel wall -INFLAMMATION
ATHEROSCLEROTIC PLAQUE RUPTURE
Newby et al Heart 2010
CHALLENGES
Fundamental questions:
• Can we use imaging to inform about vascular
biology?
• Can we use imaging to predict heart attack and
stroke?
• Can we use imaging to test effectiveness of new
therapies?
Why might FDG PET
be suited to imaging
atherosclerosis?
TRACER CHARACTERISTICS
•
•
•
•
•
2-[18F] fluoro-2-deoxy-D-glucose
Most commonly used PET radiopharmaceutical
Cyclotron-produced
Half life of 109 minutes
Analogue of glucose
HO
O
HO
OH
HO
18F-
18F
substituted for the
2-hydroxyl group
FDG UPTAKE REFLECTS INFLAMMATION
IN CAROTID ATHEROSCLEROSIS
Tawakol et al JACC 2006
Menezes et al JNM 2011
Figueroa et al JACC Imaging 2013
ARTERIAL FDG UPTAKE PREDICTS RISK OF CV
EVENTS AND THEIR TIMING
Figueroa et al JACC Imaging 2013
INFLAMMATION IMAGING WITH PET - RATIONALE
Fundamental questions:
• Does FDG signal reflect inflammation? – Correlates
strongly, but non-specific. Coronaries, not useful
• Can we predict heart attack and stroke? - Case open
but promising
• Can we measure change in inflammation with
therapy?
ARTERIAL FDG SIGNAL IS REPRODUCIBLE
Day 1
Day 14
Rudd JHF et al JACC 2007
Rudd JHF et al JNM 2008
INFLAMMATION TRACKING BY FDG PET
Tawakol et al JACC 2013
CLINICAL TRIAL VS. IMAGING TRIAL
PET/CT Imaging Trial
Tawakol et al JACC 2013 and La Rosa et al NEJM 2005
Clinical Endpoint Trial – TNT Study
CMIH - CHALLENGES
• Multi-modal multi-parametric imaging – derive
information from each voxel to understand biology,
treatment effects and risk:
– Heterogeneity, mechanical stress, shear stress
– Cross-platform co-registration
– Atherosclerosis, aneurysm, cancer imaging etc
• How do we integrate electronic patient record data with
derived and raw imaging data in a meaningful way?
One Brave Idea $75 Million Research Award for
Coronary Heart Disease Winner Announced
“A machine-learning approach
to heart attack prediction”