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Evaluating the Quality of Radiotherapy Treatment Plans for Prostate Cancer

  • Emma Stubington
  • Matthias Ehrgott
  • Glyn Shentall
  • Omid Nohadani
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 274)

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 conflicting planning goals and incorporation of uncertainty in treatment data.

Notes

Acknowledgements

The authors are grateful to staff at the Radiotherapy Department of Rosemere Cancer Centre for assistance with data provision and Allen Holder of Rose-Hulman Institute of Technology for valuable discussions regarding simulation.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Emma Stubington
    • 1
  • Matthias Ehrgott
    • 2
  • Glyn Shentall
    • 3
  • Omid Nohadani
    • 4
  1. 1.STOR-i Centre for Doctoral TrainingLancaster UniversityLancasterUK
  2. 2.Department of Management ScienceLancaster University Management SchoolLancasterUK
  3. 3.Radiotherapy Department, Rosemere Cancer CentreRoyal Preston HospitalPrestonUK
  4. 4.Department of Industrial Engineering and Management SciencesNorthwestern UniversityEvanstonUSA

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