Using the Malthus programme to predict the recruitment of patients to MR-linac research trials in prostate and lung cancer

Abstract

In this study, we used evidence-based mathematical modelling to predict the patient cohort for MR-linac to assess its feasibility in a time of austerity. We discuss our results and the implications of evidence-based radiotherapy demand modelling tools such as Malthus on the implementation of new technology and value-based healthcare.

European Organization for Research and Treatment of Cancer (EORTC) recommendations for planning and delivery of high-dose, high precision radiotherapy for lung cancer

“…The MRI-linac combines regular linear accelerator technology with MRI guidance on the machine [139]. This could theoretically result in margin reduction and improved adaptation processes.…”

Variations in Demand across England for the Magnetic Resonance-Linac Technology, Simulated Utilising Local-level Demographic and Cancer Data in the Malthus Project

“…Malthus collects information on how many virtual patients were prescribed radiotherapy (either conventional or MR-linac) and the number of fractions prescribed during a simulation. The virtual patients traverse through the CDTs in a Monte-Carlo integration, undertaking 1 000 000 walkthroughs to ensure every clinical decision is adequately represented and the averages An initial piece of scoping work testing the suitability of using Malthus for MR-linac indications was undertaken by Sanderson et al [37] in a single region in England for lung cancer and prostate cancer. Here, Malthus was used to create a statistically representative cohort of virtual cancer patients for every CCG within England, and also for England itself, for the year 2019.…”

“…An initial piece of scoping work testing the suitability of using Malthus for MR-linac indications was undertaken by Sanderson et al. [ 37 ] in a single region in England for lung cancer and prostate cancer. Here, Malthus was used to create a statistically representative cohort of virtual cancer patients for every CCG within England, and also for England itself, for the year 2019.…”

Mathematical Modelling for Patient Selection in Proton Therapy

“…They require comprehensive data to populate the clinical decision trees, linked with granular population and incidence data or projections to capture demand variations within a country over time [21]. Once such a model has been established and validated, it is not computationally expensive to modify parameters, re-run simulations and compare outputs, so that these models can be used to estimate the impact of introducing a new technology into an established health-care system, especially where specific target clinical indications are to be compared [22]. A DEM can be used to estimate the corresponding reduction in demand for existing services.…”

“…A DEM can be used to estimate the corresponding reduction in demand for existing services. These models can also be used to predict the likely patient availability for clinical trials [22], however it is not always possible to predict accurately the success rate in actual patient recruitment.…”