VPH – Opportunities for Biomedical and IT Industries. Hofstraat H. eHealth week 2010 (Barcelona: CCIB Convention Centre; 2010)

1. Virtual Physiological Human –
Biomedical and IT industries providing
tools for clinical decision taking
Hans Hofstraat, Philips Research
March 17, 2010
With contributions from:
Sybo Dijkstra, Olivier Ecabert, Joerg Sabczynski

2. Trends in Healthcare
We’re getting older and sicker Demand for care is growing
We don’t take good care of ourselves We expect better choices
Philips Research, WoHiT, Barcelona, March 17, 2010 2

3. The Meaning of Our Care Cycle Approach
The Philips Healthcare difference
People focused Care cycle driven
We start with the needs of patients and We focus on their
their care providers because understanding specific medical
their experiences ensures we create Oncology needs throughout
solutions that best meet their needs. Cardiology the care cycle …
Women’s
Health
Oncology
Cardiology
Women’s
Health
Oncology
Cardiology
Women’s
Health
And we apply our technology to help improve healthcare
quality and reduce cost because meaningful innovations
create value for patients and care providers. …wherever that care occurs.
Meaningful innovation Care anywhere
Philips Research, WoHiT, Barcelona, March 17, 2010 3

4. Medicine is Transforming from Art to Science
Creating a Need for Clinical Decision Support
Knowledge explosion
Need for solutions
Data explosion that enable
Drive for better evidence-based
outcomes decision taking:
Evidence based medicine Clinical Decision
Support
Personalized medicine
Philips Research, WoHiT, Barcelona, March 17, 2010 4

5. Clinical Decision Support
“Clinical Decision Support solutions interpret the universe of patient data,
acquired from various sources, intelligently filtered and distilled into actionable,
care specific information. In order to simplify clinician workflow, improve
financial outcomes, and help improve and save lives. Decision support -
Anytime. Anywhere”.
Philips Research, WoHiT, Barcelona, March 17, 2010 5

6. Future of Clinical Decision Support
Providing clinical guidance based on multiple data sources
Data Clinical Decision Support Clinical Guidance
Early Warning and
Monitoring
Alarms
Image Recognition
Imaging • Quantification & Interpretation
• Feature Extraction
Targeted • Modeling Diagnostic
Diagnostics • Reasoning Assistance
• Computer-Interpretable
Guidelines Therapy Planning &
Pathology
Monitoring
Clinical data Outcome Prediction
Philips Research, WoHiT, Barcelona, March 17, 2010 6

7. Clinical Decision Support for cardiac interventions
Therapy planning & monitoring for minimally invasive therapy
Data Clinical Decision Support Clinical Guidance
Early Warning and
Monitoring
Alarms
Image Recognition
Imaging • Quantification & Interpretation
• Feature Extraction
Targeted • Modeling Diagnostic
Diagnostics • Reasoning Assistance
• Computer-Interpretable
Guidelines Therapy Planning &
Pathology
Monitoring
Clinical data Outcome Prediction
Philips Research, WoHiT, Barcelona, March 17, 2010 7

8. Minimally Invasive Interventions in Cardiovascular Disease
#1 cause of death and 17-22% of global health spending
Insight:
Less invasive interventions are at the base of a key paradigm shift in healthcare
• Reduction of patient trauma and improvement in quality of life
• Reduction in length of stay in hospital and in cost of healthcare
Examples: Valve repair/replace, ASD/VSD repair, CABG, EP..
Cath Lab Interventional EP Navigator 3D Trans- Innovations for
Tools esophageal Echo Interventions
Philips Research, WoHiT, Barcelona, March 17, 2010

9. Background
Our scanners produce a
huge amount of patient
images with a wealth of
information.
We need a technology that helps to
• inspect the data efficiently,
• derive quantitative information,
• and use the images for therapy.
Philips Research, WoHiT, Barcelona, March 17, 2010 9

10. Road to the Future
Philips Research, WoHiT, Barcelona, March 17, 2010 10

11. Road to the Future
Philips Research, WoHiT, Barcelona, March 17, 2010 11

12. Personalized Cardiac Models - Principle
Training
+
Anatomical knowledge Sample images Generic model
Philips Research, WoHiT, Barcelona, March 17, 2010 12

13. Personalized Cardiac Models - Principle
Training
+
Anatomical knowledge Sample images Generic model
Personalization
+
Generic model New image Adapted model
Philips Research, WoHiT, Barcelona, March 17, 2010 13

14. Diagnosis: Automatic Determination of Heart Function
Volume of four heart chambers
over a heart beat from CT images
• Typical slowly (53 bpm) beating
heart (bottom left)
• Irregularly (> 80 bpm) beating
heart with small ejection fraction
(bottom right)
Philips Research, WoHiT, Barcelona, March 17, 2010 14

15. Image-guided Interventions: EP Navigator
Pre-interventional Intervention
CT or MR images Guidance
Personalized
heart model
Visualize left atrium to support accurate navigation of the catheter
Philips Research, WoHiT, Barcelona, March 17, 2010
Picture courtesy of Catharina Hospital, Eindhoven

16. Road to the Future
Philips Research, WoHiT, Barcelona, March 17, 2010 16

17. Road to the Future
Geometry Microstructure Microcirculation
Fluid Deformation Electrophysiology
Philips Research, WoHiT, Barcelona, March 17, 2010 17

18. euHeart – Biophysical Cardiac Models
Simulation of the patient- Clinical focus areas:
specific heart function - Resynchronization Therapy
- Radiofrequency Ablation
- Heart Failure
Blood Electrical - Coronary Artery Diseases
Flow Signals - Valves and Aorta
Project coordination:
Philips Research
Scientific coordination:
The University of Oxford
Clinical coordination:
King’s College London
Partners:
Micro- Cardiac 6 companies, 6 universities, 5 clinics
structure mechanics Budget:
~19M€ (~14M€ EU funding)
Philips Research, WoHiT, Barcelona, March 17, 2010 18

19. Clinical Decision Support for Oncology
Choosing the therapy with the best outcome tailored to the patient
Data Clinical Decision Support Clinical Guidance
Early Warning and
Monitoring
Alarms
Image Recognition
Imaging • Quantification & Interpretation
• Feature Extraction
Targeted • Modeling Diagnostic
Diagnostics • Reasoning Assistance
• Computer-Interpretable
Guidelines Therapy Planning &
Pathology
Monitoring
Clinical data Outcome Prediction
Philips Research, WoHiT, Barcelona, March 17, 2010 20

20. Personalized cancer treatments
Clinical need
• Cancer is a hyper-complex disease
• Cancer is an ‘individual’ disease
• Cancer treatment decisions today are based on a
statistical approach
• Cancer treatment in personalized medicine must take into
account the individual cancer biology
Philips Research, WoHiT, Barcelona, March 17, 2010

21. Clinical Decision Support in Oncology
Models to select the Best Therapy for an Individual Patient
• Models are mathematical representations of reality
• Models translate available data into meaningful information
• Models for tumor response must be multi-level models
• Models allow for
– treatment decision support and Biopsy material,
fluids
– multi-modal therapy optimization Gene, protein expressions etc.
Gene – protein network Imaging
data
• Models in treatment planning systems
Radiobiological, pharmacodynamic
parameter estimation
Image
processing
– Surgery Candidate
Multi-level cancer
simulator for
tumor and Clinical
therapy normal tissue data
– Radiotherapy response
simulation
– Chemotherapy New candidate
therapy
Prediction
Evaluation of
– Interventional radiology prediction
Final decision
Select optimal and treatment
Sufficient?
schedule application to
patient
Philips Research, WoHiT, Barcelona, March 17, 2010

22. Multi-level Modeling in ContraCancrum
• Molecular Level simulations
– Biochemical modelling  EGFR mutations
– Molecular statistical models of response to therapy
• Cellular and higher biocomplexity level simulator
– Discrete event cytokinetic model of cancer
– Biomechanical simulations
– Medical Image analysis modules
• The integration of all ContraCancrum modules is
implicitly done in clinical ‘multi-level’ scenarios
Philips Research 24

23. Biochemical level
Which drug for which patient?
 Over expression of
Epidermal Growth Factor
Receptor (EGFR) is
associated with cancer
 Tyrosine kinase as target
for inhibitory drugs
 Binding affinity
calculations can be used
to determine mutational
effects
phenethyl-
Cl F amine
aniline 2 3
N
2 3 1
N N 4 NH
1
N 4 NH pyrrolo-
7
5 pyrimidine
8 5
quinazoline
6
7 6
O O
propyl-
morpholino N
N
ethyl-
piperazine N
Philips Research
O
25

24. Biomechanical level
• Simulating tumor growth
• Simulating effect on
normal tissue
• Interaction between
cellular simulation and
biomechanics
Philips Research 26

25. PET/CT
Image Processing
in ContraCancrum
• Registration of multi-modality images
• Registration of time-series base-line follow-up 1 follow-up 2
• Segmentation of tumor
• Segmentation of tumor subregions
• Segmentation of normal tissue
Philips Research 27

26. In Silico Oncology - Simulating Therapy
Modelling cancer G S G M G
Simulating Simulating
at the cellular 1 2 0
Therapy A Therapy B
level N A
Modelling
at the molecular
level
Simulating tissue
biomechanics
Tumour image
analysis
and visualization time
Multi-level Modelling In Silico Optimal therapy planning
Multi-level data Multi-level Modelling
Philips Research 28

27. Oncology Clinical Decision Support
image data
Modelling cancer G S G M G
Simulating
at the cellular 1 2 0
Therapy A
level N A
Modelling
at the molecular patient path personalized
image level
analysis
treatment
protocol
clinical data clinical guidelines
Simulating tissue
biomechanics
• lab values • clinical evidence
• pathology • workflow
cancer therapy
• checklist Tumour image
• patient data analysis model
and visualization tim
Multi-level Modelling In Silico Optimal thera
Philips Research 29

28. Future of Clinical Decision Support
Providing clinical guidance based on multiple data sources
Data Clinical Decision Support Clinical Guidance
Early Warning and
Monitoring
Alarms
Image Recognition
Imaging
• Quantification & Interpretation
• Feature Extraction
Targeted • Modeling Diagnostic
Diagnostics • Reasoning Assistance
• Computer-Interpretable
Guidelines Therapy Planning &
Pathology
Monitoring
Clinical data Outcome Prediction
VPH
Philips Research, WoHiT, Barcelona, March 17, 2010 30

29. Potential impact of VPH on Care Cycles
Treatment
In-silico selection Guidance of
treatment treatment
optimization /
testing
Out-patient
follow-up
Support in
decision
making
Home Health
Improved
Management
Facilitated disease
clinical data understanding
integration
Early warning,
Early avoidance of
detection exacerbations
Risk
stratification
Philips Research, WoHiT, Barcelona, March 17, 2010

30. Anticipated Impact of VPH on Stakeholders
Patients / Society
• Personalization of care: better outcomes, and quality-of-life
• Containment of healthcare costs
Clinicians
• Integration of the fragmented and inhomogeneous data
acquired throughout the Care Cycle
• Higher confidence in decisions through evidence-based
and personalized medicine
Industry
• Tools for personalization of treatment
• Paradigm shift from purely descriptive data interpretation
towards prediction (and monitoring) of disease progression
and treatment outcome
Philips Research, WoHiT, Barcelona, March 17, 2010 33

31. Acknowledgement
Universities and research institutes Industrial partners
• INRIA, Sophia Antipolis, FR • Berlin Heart, DE
• INSERM, Rennes, FR • HemoLab, NL
• University of Karlsruhe, DE • Philips Healthcare, NL & SP
• UPF, Barcelona, SP • Philips Research, DE
• University of Sheffield, UK • PolyDimension, DE
• University of Oxford, UK • Volcano, BE
• Amsterdam Medical Center, NL
Hospitals and clinics
• KCL, London UK
• DKFZ, Heidelberg, DE
• INSERM, Rennes, FR
• HSCM, Madrid, SP
• Amsterdam Medical Center, NL
Philips Research, WoHiT, Barcelona, March 17, 2010 34

32. Acknowledgement
• FORTH, Crete, Greece
• University of Athens, Greece
• Universität des Saarlandes, Germany
• University College London, UK
• Univesity of Bedfordshire, UK
• Charles University Prague, Czech Republic
• University of Bern, Switzerland
• Philips Research Europe – Hamburg, Germany
Philips Research, WoHiT, Barcelona, March 17, 2010 35