ARCHERY: Artificial Intelligence based Radiotherapy treatment planning for Cervical and Head and Neck cancer

University College London, London

Investigators

Linked publications & trials

Abstract

PROJECT SUMMARY 50% of cancer patients require radiotherapy during their disease course, however, only 10-40% of patients in low and middle-income countries (LMICs), have access to it. A shortfall in the specialised workforce to deliver radiotherapy has been identified as the most significant barrier to expanding radiotherapy capacity. The current radiotherapy workflow is inefficient requiring several labor intensive processes and takes weeks to months to deliver in LMICs. The growing demand for cancer treatment means that the ratio of incidence to mortality will continue to worsen without a scalable solution. Artificial intelligence (AI) based software has been developed to automate two components of the radiotherapy planning pathway 1. Delineation of anatomical areas that are at risk of tumour spread and at risk of radiation damage. 2. Definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to less than a day) and human resources needed to deliver radiotherapy. We propose a non-randomised prospective study to evaluate the quality and economic impact of AI based automated radiotherapy treatment for cervical cancer and head and neck cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 706 patients (353 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures to establish the cost and resource impact of automation using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1), and South Africa (n=2) to ensure we include a broad range of patients and the representativeness of the findings will support implementation of the software in LMICs. If the study objectives are met, the AI based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access, and affordability of this key modality of cancer cure and control.