An Update to the Malthus Model for Radiotherapy Utilisation in England. Issue 1 (January 2023)

• Record Type: Journal Article
• Authors: Heritage, S.; Sundaram, S.; Kirkby, N.F.; Kirkby, K.J.; Mee, T.; Jena, R.
• Is Part Of: Clinical oncology. Volume 35:Issue 1(2023)
• Page Start: e1
• Page End: e9
• Publication Date: 2023-01
• Subjects: Breast cancer -- head and neck cancer -- health service needs and demands -- lung cancer -- prostate cancer -- radiotherapy; Oncology -- Periodicals; Tumors -- Periodicals; Cancer -- Treatment -- Periodicals; Radiotherapy -- Periodicals; Neoplasms -- Periodicals; Cancer -- Radiotherapy; Cancer -- Treatment; Oncology; Medical radiology; Radiotherapy; Tumors; Electronic journals; Periodicals; 616.994
• DOI: 10.1016/j.clon.2022.06.006
• Languages: English
• ISSNs: 0936-6555

Abstract

Abstract: Aims: The Malthus Programme predicts national and local radiotherapy demand by combining cancer incidence data with decision trees detailing the indications, and appropriate dose fractionation, for radiotherapy. Since the last model update in 2017, technological advancements and the COVID-19 pandemic have led to increasing hypofractionation of radiotherapy schedules. Indications for radiotherapy have also evolved, particularly in the context of oligometastatic disease. Here we present a brief update on the model for 2021. We have updated the decision trees for breast, prostate, lung and head and neck cancers, and incorporated recent cancer incidence data into our model, generating a current estimate of fraction demand for these four cancer sites across England. Materials and methods: The decision tree update was based on evidence from practice-changing randomised controlled trials, published guidelines, audit data and expert opinion. Site- and stage-specific incidence data were taken from the National Disease Registration Service. We used the updated model to estimate the proportion of patients who would receive radiotherapy (appropriate rate of radiotherapy) and the fraction demand per million population at a national and Clinical Commissioning Group level in 2021. Results: The total predicted fraction demand has decreased by 11.4% across all four cancer sites in our new model, compared with the 2017 version. This reduction can be explained primarily by greater use of hypofractionated treatments (including stereotactic ablative radiotherapy) and a shift towards earlier stage presentation. The only large change in appropriate rate of radiotherapy was an absolute decrease of 3% for lung cancer. Conclusions: Compared with our previous model, the current version predicts a reduction in fraction demand across England. This is driven principally by hypofractionation of radiotherapy regimens, using technology that requires increasingly complex planning. Treatment complexity and local service factors need to be taken into account when translating fraction burden into linear accelerator demand or throughput. Highlights: We present an update of a radiotherapy demand model for England. Overall predicted demand is impacted by hypofractionation. COVID-19 has accelerated adoption of hypofractionation. Reduced fraction burden is matched by an increase in treatment complexity.