title: 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.
authors.affiliation: Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, UK.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Sanderson
authors.affiliation: Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK.
authors.forename: Alan
authors.initials: A
authors.lastname: McWilliam
authors.affiliation: Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, UK; Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK.
authors.forename: Corinne
authors.initials: C
authors.lastname: Faivre-Finn
authors.affiliation: Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK.
authors.forename: Norman Francis
authors.initials: NF
authors.lastname: Kirkby
authors.affiliation: Department of Oncology, Addenbrooke's Hospital, University of Cambridge, UK.
authors.forename: Rajesh
authors.initials: R
authors.lastname: Jena
authors.affiliation: Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK; Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Mee
authors.affiliation: Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, UK; Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK. Electronic address: Ananya.Choudhury@Christie.NHS.UK.
authors.forename: Ananya
authors.initials: A
authors.lastname: Choudhury
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D019317:Evidence-Based Medicine; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging; D008297:Male; D010315:Particle Accelerators; D018579:Patient Selection; D011467:Prostate / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012007:Rectum / Q000000981:diagnostic imaging; D006113:United Kingdom
keywords: Demand modelling; MR-linac; Malthus; Viability modelling