Artificial Intelligence for Radiotherapy Auto-Contouring: Current Use, Perceptions of and Barriers to Implementation

被引:20
|
作者
Hindocha, S. [1 ,8 ]
Zucker, K. [2 ]
Jena, R. [3 ]
Banfill, K. [4 ]
Mackay, K. [5 ]
Price, G. [4 ]
Pudney, D. [6 ]
Wang, J. [7 ]
Taylor, A. [5 ]
机构
[1] Imperial Coll London, UKRI Ctr Doctoral Training Artificial Intelligence, London, England
[2] Univ Leeds, Sch Med, Worsley Bldg, Leeds, England
[3] Univ Cambridge, Addenbrookes Hosp, Dept Oncol, Cambridge Biomed Campus, Cambridge, England
[4] Christie NHS Fdn Trust, Dept Radiotherapy, Manchester, England
[5] Royal Marsden Hosp, Dept Clin Oncol, London, England
[6] Swansea Bay Univ Hlth Board, Neath Port Talbot Hosp, Southwest Wales Canc Ctr, Dept Oncol, Port Talbot, Wales
[7] Imperial Coll London, Inst Global Hlth Innovat, Computat Oncol Lab, London, England
[8] Imperial Coll London, UKRI Ctr Doctoral Training Artificial Intelligence, London SW7 2AZ, England
基金
英国科研创新办公室;
关键词
Auto; -segmentation; Artificial intelligence; Barriers; Perceptions; Radiotherapy; CANCER; SEGMENTATION; ATLAS;
D O I
10.1016/j.clon.2023.01.014
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Aims: Artificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radio-therapy 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 ra-diation 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 exac-erbating 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 in-telligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of The Royal College of Radiologists. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:219 / 226
页数:8
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