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