Evaluating the quality of radiotherapy treatment plans with uncertainty using data envelopment analysis

External beam radiation therapy is a common treatment method for cancer. Radiotherapy is planned with the aim of achieving 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. This thesis aims to support the radiotherapy treatment planning process for prostate cancer by evaluating the quality of proposed treatment plans relative to previous plans. We develop a variable selection technique, autoPCA, to select the most relevant variables for use in our Data Envelopment Analysis (DEA) models. This allows us to evaluate how well plans perform in terms of achieving the conflicting goals of radiotherapy. We develop the uncertain DEA problem (uDEA) for the case of box uncertainty and show that for small problems this can be solved exactly. This study of uncertainty is motivated by the inherently uncertain nature of the treatment process. Robust DEA, uDEA and simulation are applied to prostate cancer treatment plans to investigate this uncertainty. We identify plans that have the potential to be improved, which clinicians then replan for us. Small improvements were seen and we discuss the potential difference this could make to planning cases that are more complex. To aid this, we develop a prototype software, EvaluatePlan, that assesses the efficiency of a plan compared to past treatment plans

Evaluating the Quality of Radiotherapy Treatment Plans for Prostate Cancer

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