clinical impact of a discrete event simulation model for radiotherapy demand raj jena university of...
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Clinical impact of a discrete event simulation model for radiotherapy demand
Raj JenaUniversity of Cambridge
Computation | Modelling | Dose CalculationCOMODO
Disclosures
• This research programme was funded by the National Cancer Action Team
• I receive funding from:
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
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
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)
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 populationEquipment, Workload and Staffing for Radiotherapy in the UK 1997–2002 Ref No: BFCO(03)3 The Royal College of Radiologists
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
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!
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…”
Malthus was born
Monte Carlo application for local radiotherapy treatment & hospital usage statistics
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
Model architecture
Curated incidence data
feeds from NCAT server
User select PCT / Region and disease sites for simulation
Virtual population of
patients
Breast Lung H&N Urology …
∑ Summary statsDetailed report
Evidence based trees
Consensus based trees
DiseaseStageAgeCo-morbidity
Typically 2000 passes through the decision tree for each cancer patient
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 treatmentDetails of the available treatment options
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
Advantages over NRAG model• Study local variation in breast cancer incidence• Breast Cancer : Evidence based fractionation, Haringey PCT vs Torbay PCT
Haringey9025 per M
Torbay17091 per M
Simulation summary : HaringeyTotal number of fractions in the selected population :2032Total population of selected PCTS :225138Fraction burden per million of populations :9025.58Access rate is 75.27%Simulation performed using NCAT validated decision treesSimulation completed at 19:17:07
Simulation summary : TorbayTotal number of fractions in the selected population :2286Total population of selected PCTS :133749Fraction burden per million of populations :17091.72Access rate is 75.46%Simulation performed using NCAT validated decision treesSimulation completed at 19:19:01
Double the population, roughly the same number of fractions…
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 summaryTotal number of fractions in the selected population :480479Total population of selected PCTS :51111574Fraction burden per million of populations :9400.59Access rate is 57.16%Simulation performed using NCAT validated decision treesSimulation 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 summaryTotal number of fractions in the selected population :384736Total population of selected PCTS :51111574Fraction burden per million of populations :7527.38Access rate is 48.05%Simulation performed using NCAT validated decision treesSimulation completed at 19:36:41
Sense check of data
REAL WORLD IMPACT Decision support | Influencing Policy
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
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
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)
Malthusramp-upModelling suite for scenario based planning of proton beam Therapy
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
Computation | Modelling | Dose Calculation
Models encode
knowledge
Data empowers
models
Knowledge informs decision
COMODO
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