Highlights
- •A model has been developed for assisting proton therapy referral decision making.
- •The Monte Carlo Markov model is based on radiobiological models.
- •The clinical outcome of an individual patient is estimated by the Markov model.
- •An example patient was used to demonstrate the capabilities of the Markov model.
Abstract
Purpose
Proton therapy can be a highly effective strategy for the treatment of tumours. However,
compared with X-ray therapy it is more expensive and has limited availability. In
addition, it is not always clear whether it will benefit an individual patient more
than a course of traditional X-ray therapy. Basing a treatment decision on outcomes
of clinical trials can be difficult due to a shortage of data. Predictive modelling
studies are becoming an attractive alternative to supplement clinical decisions. The
aim of the current work is to present a Markov framework that compares clinical outcomes
for proton and X-ray therapy.
Methods
A Markov model has been developed which estimates the radiobiological effect of a
given treatment plan. This radiobiological effect is estimated using the tumour control
probability (TCP), normal tissue complication probability (NTCP) and second primary
cancer induction probability (SPCIP). These metrics are used as transition probabilities
in the Markov chain. The clinical outcome is quantified by the quality adjusted life
expectancy. To demonstrate functionality, the model was applied to a 6-year-old patient
presenting with skull base chordoma.
Results
The model was successfully developed to compare clinical outcomes for proton and X-ray
treatment plans. For the example patient considered, it was predicted that proton
therapy would offer a significant advantage compared with volumetric modulated arc
therapy in terms of survival and mitigating injuries.
Conclusions
The functionality of the model was demonstrated using the example patient. The proposed
Markov method may be a useful tool for deciding on a treatment strategy for individual
patients.
Keywords
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Article info
Publication history
Accepted:
November 15,
2017
Received in revised form:
November 7,
2017
Received:
September 1,
2017
Identification
Copyright
Crown Copyright © 2017 Published by Elsevier Ltd on behalf of Associazione Italiana di Fisica Medica. All rights reserved.
