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

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.

A decision impact, decision conflict and economic assessment of routine Oncotype DX testing of 146 women with node-negative or pNImi, ER-positive breast cancer in the U.K.

BACKGROUND: Tumour gene expression analysis is useful in predicting adjuvant chemotherapy benefit in early breast cancer patients. This study aims to examine the implications of routine Oncotype DX testing in the U.K. METHODS: Women with oestrogen receptor positive (ER+), pNO or pN1mi breast cancer were assessed for adjuvant chemotherapy and subsequently offered Oncotype DX testing, with changes in chemotherapy decisions recorded. A subset of patients completed questionnaires about their uncertainties regarding chemotherapy decisions pre- and post-testing. All patients were asked to complete a diary of medical interactions over the next 6 months, from which economic data were extracted to model the cost-effectiveness of testing. RESULTS: Oncotype DX testing resulted in changes in chemotherapy decisions in 38 of 142 (26.8%) women, with 26 of 57 (45.6%) spared chemotherapy and 12 of 85 (14.1%) requiring chemotherapy when not initially recommended (9.9% reduction overall). Decision conflict analysis showed that Oncotype DX testing increased patients' confidence in treatment decision making. Economic analysis showed that routine Oncotype DX testing costs £6232 per quality-adjusted life year gained. CONCLUSION: Oncotype DX decreased chemotherapy use and increased confidence in treatment decision making in patients with ER+ early-stage breast cancer. Based on these findings, Oncotype DX is cost-effective in the UK setting.