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Clinical impact of a discrete event simulation model for radiotherapy demand Raj Jena University of Cambridge Computation | Modelling | Dose Calculation.

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Presentation on theme: "Clinical impact of a discrete event simulation model for radiotherapy demand Raj Jena University of Cambridge Computation | Modelling | Dose Calculation."— Presentation transcript:

1 Clinical impact of a discrete event simulation model for radiotherapy demand Raj Jena University of Cambridge Computation | Modelling | Dose Calculation

2 Disclosures This research programme was funded by the National Cancer Action Team I receive funding from:

3 Overview Radiotherapy : cost effective cancer cure Cottier Report : stand up and be counted NRAG 2007 Model : so near yet so far Malthus & Multi-scale modelling Implementation Impact : curing cancer with computation Next steps & conclusion

4 Radiotherapy Effective spatially and biologically targeted anti-cancer therapy Used in treatment of 40% of patients cured of cancer Cost effective : £2500 for course of treatment

5 Pre-2K era 1997 – 25% of RT machines aged 10 years or older 15% increase in treatment fractions year on year 28% patient waiting more than 4 weeks to start RT RT services deemed inadequate 5 year investment programme in RT hardware (NOF / DOH Funding)

6 2003 Cottier report Survey of 57 NHS radiotherapy centres 25% linacs under 3 years old, but 39% over 10 years old. Nationally running at just over 50% predicted number of linacs Only 39% of centres reaching target of 4 linacs per million population Equipment, Workload and Staffing for Radiotherapy in the UK 1997–2002 Ref No: BFCO(03)3 The Royal College of Radiologists

7 RT Utilisation Models CCORE 2003 SRAG 2006 WCSCG 2006 NRAG 2007 – model of RT demand – 63% increase in RT activity required from 30,000 to 48,000 fractions/million/year – Projected activity of 54,000 fractions per million by 2016 Aspirational targets used in 2007 Cancer Plan

8 Garbage In :: Garbage Out Poor assessment of data quality Usage of data from different healthcare systems Applied to a generic population Poor robustness of computational models Poor fit to local data : commissioning ‘blight’ We could do a lot better!

9 Multi-scale models DNA PARTRAC (Fortran) Monte Carlo transport codes (Fortran) Cell CelCyMUS : Cell cycle model (Fortran) Virtual Petri Dish (C++) Tissue CAMUS : Cellular Automaton (Pascal) Ayatana : 3D Spheroid (F#) Patient BJJK : Discrete event simulation (Fortran) Population ??? (Discrete event simulation) “Dear Prof Richards. We can do this properly. Please can we have some money…”

10 Malthus was born Discrete event simulation Create virtual cancer patient who acquires a cancer diagnosis, treatment events, and radiotherapy treatment Use locale specific base data (population data and cancer incidence) Incorporate population and cancer burden projection to 2030 User facing tool : Windows executable allowing user modification of model

11 Model architecture Curated incidence data feeds from NCAT server User select PCT / Region and disease sites for simulation Virtual population of patients BreastLungH&NUrology … Summary stats Detailed report Evidence based trees Consensus based trees Disease Stage Age Co-morbidity Typically 2000 passes through the decision tree for each cancer patient

12 Decision trees Incidence data for given disease stage Selecting between major treatment groups (esp surgery vs RT) Taking disease and patient specific factors into account for choice of treatment Details of the available treatment options

13 Standards based architecture Simple GUI interface to run and modify model Decision trees encoded in XML Base data in Excel files OLE automation to generate reports in MS Office

14 Advantages over NRAG model Study local variation in breast cancer incidence Breast Cancer : Evidence based fractionation, Haringey PCT vs Torbay PCT Haringey 9025 per M Haringey 9025 per M Torbay per M Torbay per M Simulation summary : Haringey Total number of fractions in the selected population :2032 Total population of selected PCTS : Fraction burden per million of populations : Access rate is 75.27% Simulation performed using NCAT validated decision trees Simulation completed at 19:17:07 Simulation summary : Torbay Total number of fractions in the selected population :2286 Total population of selected PCTS : Fraction burden per million of populations : Access rate is 75.46% Simulation performed using NCAT validated decision trees Simulation completed at 19:19:01 Double the population, roughly the same number of fractions…

15 Advantages over NRAG model Study effect of changes in non-RT related management Prostate cancer, England, no retreatment : change in divide from surveillance and EBRT Simulation summary Total number of fractions in the selected population : Total population of selected PCTS : Fraction burden per million of populations : Access rate is 57.16% Simulation performed using NCAT validated decision trees Simulation completed at 19:34:51 Scenario 1 : Low risk prostate Cancer Surveillance 25% Surgery 20% Brachytherapy 15% EBRT 40% Scenario 2 : Low risk prostate Cancer Surveillance 70% Surgery 10% Brachytherapy 5% EBRT 15% Simulation summary Total number of fractions in the selected population : Total population of selected PCTS : Fraction burden per million of populations : Access rate is 48.05% Simulation performed using NCAT validated decision trees Simulation completed at 19:36:41

16 Sense check of data

17 REAL WORLD IMPACT Decision support | Influencing Policy

18 Installed User Base Over 400 registered users 2012 : Every commissioning lead, RT service manager 2013 : Malthus Cymru developed for Welsh CSAG Canada, Australia, France, Germany Facilitates dialogue between providers and purchasers of RT

19 Impact case studies Support of numerous business cases for new treatment units (e.g. Sheffield, Norwich, Oxford, Brighton) Satellite centres in Peterborough, Manchester Evaluation of new technologies (stereotactic radiotherapy in south-west) WIPT : Workflow planning tool for medical physics

20 Future plans Complexity level of treatment Specialised models for new technologies – Proton Beam Therapy – Radiosurgery BI integration – GIS data for travel isochrones – Cost effectiveness analysis (Jean-Marc Bourque, King’s)

21 University of Manchester : Norman Kirkby, Karen Kirkby University of Surrey : Tom Mee Addenbrooke’s : Mike Williams King’s : Jean-Marc Bourque NCAT : Mike Richards, Tim Cooper Acknowledgements

22 Computation | Modelling | Dose Calculation Models encode knowledge Data empowers models Knowledge informs decision COMODO


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