Modelling of Cancer Treatment
Submitting Institution
University of SurreyUnit of Assessment
General EngineeringSummary Impact Type
TechnologicalResearch Subject Area(s)
Mathematical Sciences: Statistics
Medical and Health Sciences: Oncology and Carcinogenesis
Economics: Applied Economics
Summary of the impact
Research conducted at the University of Surrey has resulted in a suite of
clinically-relevant, multi-scale mathematical models being developed and
used within the NHS [1-3].
One of these models, MALTHUS, now funded by the National Cancer Action
Team, predicts demand for radiotherapy across England and Wales. MALTHUS
is a national metric and NHS commissioners are required to use MALTHUS to
justify purchases of new radiotherapy equipment. Ipswich was the first to
use Malthus in evidence to justify successfully the purchase of new
equipment.
Underpinning research
Radiotherapy (RT) is a highly effective cancer treatment, medically and
economically, being responsible for success in 40% of patients cured of
cancer (compared to 49% for surgery and 11% for drug treatment) [1-4]. In
the past two years, there has been a complete revolution in the way the
NHS proposes to commission services. GP commissioning of cancer services
has now been abandoned in favour of central commissioning by the
Radiotherapy Clinical Reference Group operating within the NHS Improving
Quality initiative. It is therefore important and timely that a tool
exists to predict accurately the future demand for radiotherapy regionally
and nationally.
A course of RT is delivered as a series of `fractions' usually daily over
a number of weeks. In a simple treatment, the dose per fraction and the
target volume are constant but in more complicated treatments both can be
varied. Concomitant and adjuvant chemotherapy can also be given to enhance
the effectiveness of the RT and we have developed multi-scale models to
help clinicians understand the complicated interactions between chemo- and
radio-therapy [1-3].
The MALTHUS (Monte Carlo Application for Local Treatment and
Healthcare Usage Simulation) model developed at Surrey predicts demand for
radiotherapy due to all types of cancer [5-6]. It uses clinical decision
trees obtained from the published evidence base and by questioning groups
of leading clinicians. The program can then launch a population of virtual
patients with representative age and sex distributions, socio-economic
characteristics and cancer disease incidence, through these trees to
predict the demand for radiotherapy equipment.
Malthus is a close collaboration between the clinicians in Cambridge and
the modelling expertise at Surrey where a research version of the program
is under continuous development to provide state of the art tools not
normally exploited in the NHS. The modelling and solution algorithms
originate from Surrey, whilst Cambridge provides the clinical decision
trees.
Data for cancer incidence is supplied by the regional cancer registries
via the National Cancer Intelligence Network (NCIN), and population
statistics and predictions are supplied by the Office for National
Statistics (ONS). The model constructs representative populations of
patients which are then fed to a discrete event simulation of the clinical
decision processes in the form of a binary decision tree. This process is
effectively a Monte Carlo integration over all possible clinical pathways.
Each virtual patient collects a virtual patient record as they pass
through the tree, and once a cohort has been `treated', the number of RT
fractions is summed. This cohort calculation process is embedded in a
Monte Carlo simulation in order to predict the effects of uncertainty in
the population and incidence data and the parameters of the clinical
decision trees. Hence users of the program can be supplied with both the
expected number of fractions for the chosen cohort and the likely errors
in this estimate.
An important feature of this methodology is that it has the capacity to
be extended to predict, for instance, the demand for complex RT and the
theatre time required due to surgery.
References to the research
1. Kirkby, N.F., Jefferies, S.J., Jena, R., and Burnet, N.G., `A
mathematical model of the treatment and survival of patients with
high-grade brain tumours', Journal of Theoretical Biology, 245
(1), pp 112-124, 2007
2. Burnet, N.G., Jena, R., Jefferies, S.J., Stenning, S.P., Kirkby, N.F.,
`Mathematical modelling of survival of glioblastoma patients suggests a
role for radiotherapy dose escalation and predicts poorer outcome
following delay to start treatment', Clin. Onc., 18 (2), pp
93-103, 2006
3. Barazzuol, L., Burnet, N.G., Jena R, Jones, B., Jefferies, S., &
Kirkby, N.F., `A mathematical model of brain tumour response to
radiotherapy and chemotherapy considering radiobiological aspects',
Journal of Theoretical Biology, 262(3), pp553-565, 2010.
4. Burnet, N.G., Jefferies, S.J., Benson, R.J., Hunt, D.P., &
Treasure, F.P. , `Years of life lost (YLL) from cancer is an important
measure of population burden--and should be considered when allocating
research funds', Br J Cancer, 92(2), pp241-5, 2005.
5. Round CE, Williams MV, Jena R, Mee T, Kirkby NF, Cooper T, Hoskin P.
(2013) 'Radiotherapy Demand and Activity in England 2006-2020'. Clin.
Onc., doi: 10.1016/j.clon.2013.05.005
6. Round C, Williams MV, Jena R, Mee T, Kirkby NF, Cooper T. (2013) 'The
Malthus Programme: Developing Radiotherapy Demand Models for Breast and
Prostate Cancer at the Local, Regional and National Level'. Clin. Onc.,
doi: 10.1016/j.clon.2013.05.006
Details of the impact
A BBSRC/EPSRC/MRC-funded "discipline hop" enabled Professor Norman Kirkby
from the University of Surrey's Chemical & Process Engineering
Department to spend a year at the Oncology Centre in the Addenbrooke's
Hospital, Cambridge. As a direct result of this opportunity a suite of
multi-scale mathematical models have been generated which go from the
cellular level right though to national policy and decision-making level
[1]. One resulting paper has been a highly cited publication [2 above]. A
consultant neuro-oncologist from the Addenbrookes is now seconded to
Chemical & Process Engineering at Surrey for one day a fortnight; this
arrangement is unique in the UK and critical to ensuring that the research
has a full reach `from bench to bedside'.
One of these models, MALTHUS (Monte Carlo Application for Local
Treatment and Healthcare Usage Simulation) commissioned by the National
Cancer Action Team (NCAT) is available as a download so that clinicians
and commissioners can plan current and future radiotherapy demand for
their network or region. As of March 2013, MALTHUS had over 100 users;
however it is now incorporated in the "standard PC build" for a number of
NHS trusts and so the exact number of users is unknown. The first use of
MALTHUS to justify purchase of new equipment was made by Ipswich using the
beta release version.
MALTHUS was originally developed for England but has been extended to
Wales and recently the program has been made available to research groups
in Scotland, Australia and Canada. The European Society for Radiotherapy
and Oncology is considering MALTHUS for incorporation into their Health
Economics in Radiation Oncology Project [2].
One unique feature of this program is that we have achieved a national
consensus on best RT practice. MALTHUS is also being used by clinicians
because it contains a repository of this evidence base. The decision trees
in MALTHUS are now also used to assess clinical performance in RT
departments
The government has recently announced volcanic changes to the way NHS
commissioning will operate; it has never been more important to have
robust and reliable tools to predict demand for services. The long-term
benefit of MALTHUS is that it represents a nationally-agreed, systematic
way of deciding how many fractions of radiotherapy are likely to be
required in any given location and thereby will remove the `postcode
lottery'.
The impact of Surrey's research is well illustrated by stake holder
feedback, a selection is included below;
In his letter to the Cancer Network Directors, January 2012 [3], the
National Cancer Director, said (our underline):
"... the new Malthus modelling tool for radiotherapy demand. This
tool uses evidence-based radiotherapy decision trees based on UK
clinical practice and local cancer incidence data. From this it
calculates radiotherapy demand requirements and can model forward to
take account of changes in cancer incidence as the population ages.
This is likely to produce a more realistic model for radiotherapy
demand than the NRAG model and I have no doubt will be a focus of
your radiotherapy plans into the future..." Later in the
letter he added: "I would encourage you and your colleagues to
use this model regularly and update the version
in use." And, "I would therefore encourage commissioners to use the
Malthus tool for radiotherapy planning... These results should be used
to inform strong commissioning discussions with providers."
In the Department of Health report; Radiotherapy in England 2012" [4] :
"To support accurate workforce planning the outputs from Malthus are being
used to feed the development of a workforce planning tool for the
physics and radiography workforce (the Workforce Integrated Planning
Tool, WIPT). This will help identify the overall requirements for
national training numbers for the physics and radiographic
professional groups taking account of emerging changes to local skill
mix as a result of new techniques and technologies"
For lung cancer (~1300 avoidable deaths per annum if survival in England
is matched to the best in Europe [5]) the DoH [4] in 2012 added
"Lung cancer fractionation is much more diverse because the intent
of treatment may be palliative or radical. A significant proportion of
patients receive short palliative regimens, all of which are supported
by clincial trial evidence in different settings. Radical treatment is
dominated by 20 fraction regimens which have a poor evidence base. Few
patients receive treatment in 30 or more fractions which would be
standard in most of Europe and North America for many indications.
This should be addressed in the commissioning process through use of
the evidence based decision trees in the Malthus model."
An overview of radiotherapy services from the NHS National Cancer Action
team [6], guidance on the management of radiotherapy capacity from the
Royal College of Radiologists [7] and the NHS standard contract for
radiotherapy [8] each demonstrate that Malthus is now embedded as a
standard and important tool for the planning and management of
radiotherapy services throughout the UK.
The Malthus web site is publicly accessible and provides further
illustrations of the impact achieved [9]
Sources to corroborate the impact
- Jena, R., Round, C., Mee, T., Kirkby, N., Hoskin, P. & Williams,
M., `The Malthus Programme - A New Tool for Estimating Radiotherapy
Demand at a Local Level', Clin. Onc., 24, pp1-3, 2012.
- ESTRO HERO project web site http://www.estro.org/about/health-economics-in-radiation-oncology---hero/hero
-
http://www.sor.org/news/radiotherapy-services-modelling-tool
This link includes a download for the letter from the National Cancer
Director to all NHS Network Directors, dated 16th January
2012.
- Department of Health, "Radiotherapy Services in England 2012
https://www.gov.uk/government/publications/radiotherapy-services-in-england-2012
- Tim Cooper NCAT presentation to North Trent Cancer Network on
Modelling Radiotherapy
http://www.northtrentcancernetwork.nhs.uk/professionals/workshop---14th-june-2011.htm
- National Cancer action Team Delivering world class radiotherapy
http://www.radpro.eu/radproexpo2012/ncat/ncat.html
- Guidance on the management and governance of additional radiotherapy
capacity
http://www.ipem.ac.uk/Portals/0/Documents/Publications/Guidance%20on%20the%20Mgt%20and%20Governance%20of%20additional%20RT%20capacity%202013.pdf
- 2013/14 NHS England Standard contract for Radiotherapy http://www.england.nhs.uk/wp-content/uploads/2013/06/b01-radiotherapy.pdf
- Malthus web site http://www.camradiotherapy.org.uk/malthus