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Evaluating the Quality of Radiotherapy Treatment Plans for Prostate Cancer

Research output: Contribution in Book/Report/ProceedingsChapter (peer-reviewed)

Forthcoming
Publication date15/05/2018
Host publicationCases based on Multiple Criteria Decision Making/Aiding methods: Building and Solving Decision Models with Computer Implementations
EditorsSandra Huber, Martin Josef Geiger, Adiel T. de Almeida
PublisherSpringer
Number of pages26
<mark>Original language</mark>English

Abstract

External beam radiation therapy is a common treatment method for cancer. Radiotherapy is planned with the aim to achieve conflicting goals: while a sufficiently high dose of radiation is necessary for tumour control, a low dose of radiation is desirable to avoid complications in normal, healthy, tissue. These goals are encoded in clinical protocols and a plan that does not meet the criteria set out in the protocol may have to be re-optimised using a trial and error process. To support the planning process, it is therefore important to evaluate the quality of treatment plans in order to recognise plans that will benefit from such re-optimisation and distinguish them from
those for which this is unlikely to be the case. In this chapter we present a case study of evaluating the quality of prostate cancer treatment plans based on data collected from Rosemere Cancer Centre at the Royal Preston Hospital in the UK. We use Principal Component Analysis for data reduction, i.e., to select the most relevant data from the entire set available for each patient.
We then apply Data Envelopment Analysis to assess the quality of individual plans. Each plan is compared against the entire set of plans to identify those that could realistically be improved. We further enhance this procedure with simulation techniques to account for uncertainties in the data for treatment plans. The proposed approach to plan evaluation provides a tool to support radiotherapy treatment planners in their task to determine the best possible radiotherapy treatment for cancer patients. With its combination of DEA, PCA and simulation, it allows focusing on the most significant determinants of plan quality, consideration of trade-offs between con icting planning goals and incorporation of uncertainty in treatment data.