Simulating demand for innovative radiotherapies: An illustrative model based on carbon ion and proton radiotherapy

Pascal Pommier*, Yolande Lievens, Fabien Feschet, Josep M. Borras, Marie-Helene Baron, Anastasiya Shtiliyanova, Madelon Pijls-Johannesma

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Web of Science)

Abstract

Background and purpose Innovative therapies are not only characterized by major uncertainties regarding clinical benefit and cost but also the expected recruitment of patients An original model was developed to simulate patient recruitment to a costly particle therapy by varying layout of the facility and patient referral (one vs several countries) and by weighting the treated indication by the expected benefit of particle therapy Material and methods A multi-step probabilistic spatial model was used to allocate patients to the optimal treatment strategy and facility taking into account the estimated therapeutic gain from the new therapy for each tumour type, the geographical accessibility of the facilities and patient preference Recruitment was simulated under different assumptions relating to the demand and supply Results Extending the recruitment area, reducing treatment capacity. equipping all treatment rooms with a carbon ion gantry and inclusion of proton protocols in carbon ion facilities led to an increased proportion of indications with the highest expected benefit Assuming the existence of a competing carbon ions facility, lower values of therapeutic gain, and a greater unwillingness of patients to travel for treatment increased the proportion of indications with low expected benefit Conclusions Modelling patient recruitment may aid decision-making when planning new and expensive treatments
Original languageEnglish
Pages (from-to)243-249
JournalRadiotherapy and Oncology
Volume96
Issue number2
DOIs
Publication statusPublished - Aug 2010

Keywords

  • Protons
  • Carbon ions
  • Modelling
  • Decision-making
  • Recruitment
  • Innovation

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