Artificial Intelligence for Radiotherapy Auto-Contouring: Current Use, Perceptions of and Barriers to Implementation. Issue 4 (April 2023)

• Record Type: Journal Article
• Authors: Hindocha, S.; Zucker, K.; Jena, R.; Banfill, K.; Mackay, K.; Price, G.; Pudney, D.; Wang, J.; Taylor, A.
• Is Part Of: Clinical oncology. Volume 35:Issue 4(2023)
• Page Start: 219
• Page End: 226
• Publication Date: 2023-04
• Subjects: Auto-segmentation -- Artificial intelligence -- Barriers -- Perceptions -- 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.2023.01.014
• Languages: English
• ISSNs: 0936-6555

Abstract

Abstract: Aims: Artificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several commercially available artificial intelligence-based auto-contouring tools have emerged in recent years. Their clinical deployment raises important considerations for clinical oncologists, including quality assurance and validation, education, training and job planning. Despite this, there is little in the literature capturing the views of clinical oncologists with respect to these factors. Materials and Methods: The Royal College of Radiologists realises the transformational impact artificial intelligence is set to have on our specialty and has appointed the Artificial Intelligence for Clinical Oncology working group. The aim of this work was to survey clinical oncologists with regards to perceptions, current use of and barriers to using artificial intelligence-based auto-contouring for radiotherapy. Here we share our findings with the wider clinical and radiation oncology communities. We hope to use these insights in developing support, guidance and educational resources for the deployment of auto-contouring for clinical use, to help develop the case for wider access to artificial intelligence-based auto-contouring across the UK and to share practice from early-adopters. Results: In total, 78% of clinical oncologists surveyed felt that artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk were more varied, but 49% felt that artificial intelligence will decrease risk for patients. There is a marked appetite for urgent guidance, education and training on the safe use of such tools in clinical practice. Furthermore, there is a concern that the adoption and implementation of such tools is not equitable, which risks exacerbating existing inequalities across the country. Conclusion: Careful coordination is required to ensure that all radiotherapy departments, and the patients they serve, may enjoy the benefits of artificial intelligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this. Highlights: 78% of clinical oncologists felt artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk more variable but 49% feel artificial intelligence will decrease risk for patients. 60% felt artificial intelligence will save time in radiotherapy planning. There is a marked appetite for urgent guidance and education on safe use of artificial intelligence in clinical practice.