S2960
Late-Breaking
ESTRO 2026
cervix, head and neck, and prostate cancer undergoing radical - dose radiotherapy. Recruitment occurred across seven public sector hospitals in India (n=3), Jordan (n=1), Malaysia (n=1) and South Africa (n=2). Each patient was treated using the local contouring and planning approach, while CT scans were simultaneously uploaded to the RPA. Prospective timing studies were conducted for both pathways. The use of the same CT for the manual and automated approach meant randomisation was not necessary. The primary outcome was treatment plan acceptability, a composite endpoint encompassing both contouring and planning quality. Automated contours and plans were assessed by two independent reviewers blinded to the study site, using a protocol developed according to international standards by the UK Radiotherapy Trials Quality Assurance (RTTQA) group. A third reviewer was consulted for disconcordant cases. The sample size calculation for each tumour population assumed an overall plan acceptability rate of >90%. The secondary outcome was time and cost savings which were analysed using the ESTRO time-driven activity-based costing model. Patient representatives contributed to study design, materials, and endpoint selection. Results: Between 2nd January 2024 and 14th January 2026, ARCHERY recruited 1029 patients (351 prostate, 331 cervix and 347 head and neck). The sites recruited the following numbers of patients: University of Cape Town 239, Tata Medical Center, Kolkata 212, Stellenbosch University 171, King Hussein Cancer Center 152, Tata Memorial Hospital, Mumbai 103, Universiti Malaya 98 and Homi Bhabha Cancer Hospital, Vizag 54. Demographic and tumour characteristics of participants are included in Table 1. Evaluation is ongoing and the primary outcome results for the cervix and prostate trial arms will be presented at the ESTRO congress.
Proffered Paper 5494
ARCHERY: a global trial evaluating artificial intelligence (AI) based radiotherapy treatment: results for cervix and prostate cancer (NCT05653063) Ajay Aggarwal 1,2 , Laurence Court 3 , Claire Murphy 4 , Zaidah Amlay 5 , Henriette Burger 6 , Raphael Douglas 3 , Sarbani Ghosh-Laskar 7 , Jonathan Helbrow 8 , Mariana Kroiss 8 , Ruth Langley 4 , Yolande Lievens 9 , Katherine Mackay 10 , Umesh Mahantshetty 11 , Rozita Malik 12 , Indranil Mallick 13 , Elizabeth Miles 8 , Raviteja Miriyala 11 , Issa Mohamad 14 , Vedang Murthy 15 , Tucker Netherton 3 , Jeannette Parkes 5 , Christoph Trauernicht 16 , Barbara Vanderstraeten 9 , Matthew Nankivell 4 , Mahesh Parmar 4 1 Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom. 2 Department of Clinical Oncology, Guy's & St Thomas' NHS Trust, London, United Kingdom. 3 Department of Radiation Physics, MD Anderson Cancer Center, Houston, USA. 4 Innovative Clinical Trials Unit, University College London, London, United Kingdom. 5 Department of Radiation Medicine, University of Cape Town, Cape Town, South Africa. 6 Division of Radiation Oncology, Stellenbosch University, Stellenbosch, South Africa. 7 Department of Radiation Oncology, Tata Memorial Centre, Mumbai, India. 8 Radiotherapy Trials Quality Assurance Group, Mount Vernon, Middlesex, United Kingdom. 9 Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium. 10 Department of Radiotherapy, Royal Marsden Hospital, London, United Kingdom. 11 Department of Radiation Oncology, Homi Bhabha Cancer Hospital & Research Centre, Vizag, India. 12 Department of Radiation Oncology, Universiti Malaya Medical Centre, Kuala Lumpur, Malaysia. 13 Department of Radiation Oncology, Tata Memorial Center, Kolkata, India. 14 Department of Radiation Oncology, King Hussein Cancer Center, Amman, Jordan. 15 Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Mumbai, India. 16 Division of Medical Physics, Stellenbosch University, Stellenbosch, South Africa Purpose/Objective: AI can potentially transform radiotherapy services in low-and-middle-income countries, where only 10-40% of those eligible for radiotherapy have access to it (1). In this trial, we assessed the quality and economic impact of the Radiotherapy Planning Assistant (RPA), a web-based AI tool, developed to address global radiotherapy access challenges by automating contouring and treatment planning in one end-to-end process. Material/Methods: Archery is a multi - cancer basket trial that used a master protocol to evaluate the RPA in adults with
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