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 Were getting older and sicker Demand for care is growing We dont 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 Womens Health Oncology Cardiology Womens Health Oncology Cardiology Womens 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: Kings 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 Universitt 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