UK Contouring Variation in Nasopharyngeal Carcinoma

Fong, C.; Boon, Cheng; Tiffany, M.; Roques, Tom; Brammer, Caroline; Foran, Bernadette; Prestwich, R.; Chan, Annie; Howard, Helen; Sanghera, Paul; Hartley, A.

doi:10.1016/j.ijrobp.2016.06.1620

Cited by 5 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Variation in radiotherapy target delineation exists among clinicians from centre to centre nationally and internationally [19,20]. It will be difficult to eliminate variation completely, as there are no specific tests that will ascertain the CTV for a particular tumour volume is absolutely accurate.…”

Section: Radiotherapy Planning Target Volume Delineation and Qualmentioning

confidence: 99%

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

2018

View full text Add to dashboard Cite

The fields of radiotherapy and clinical oncology have been rapidly changed by the advances of technology. Improvement in computer processing power and imaging quality heralded precision radiotherapy allowing radiotherapy to be delivered efficiently, safely and effectively for patient benefit. Artificial intelligence (AI) is an emerging field of computer science which uses computer models and algorithms to replicate human-like intelligence and perform specific tasks which offers a huge potential to healthcare. We reviewed and presented the history, evolution and advancement in the fields of radiotherapy, clinical oncology and machine learning. Radiotherapy target delineation is a complex task of outlining tumour and organ at risks volumes to allow accurate delivery of radiotherapy. We discussed the radiotherapy planning, treatment delivery and reviewed how technology can help with this challenging process. We explored the evidence and clinical application of machine learning to radiotherapy. We concluded on the challenges, possible future directions and potential collaborations to achieve better outcome for cancer patients.

show abstract

Section: Radiotherapy Planning Target Volume Delineation and Qualmentioning

confidence: 99%

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

2018

View full text Add to dashboard Cite

show abstract

“…Optimal application of available imaging techniques to better define CTV, selective nodal irradiation in node-negative tumours, prospective peer review of target volumes, 47 and evidence-based practice for the use of chemotherapy across all NPC stages should be considered to improve current practice (Table 4). Developing a RT consensus for NPC in the UK is essential, and in this regard, we welcome the recent endeavour of our colleagues to initiate this, 48 and expect that this survey will provide definitive information to finalise a national consensus.…”

Section: Discussionmentioning

confidence: 99%