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Medicines (Basel). 2018 Dec 11;5(4). pii: E131. doi: 10.3390/medicines5040131.

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

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Department of Clinical Oncology, Leeds Cancer Centre, St James's Institute of Oncology, Leeds Teaching Hospitals NHS Trust, Leeds LS9 7TF, UK.
Department of Radiology, Worcestershire Acute Hospitals NHS Trust, Worcester WR5 1DD, UK.
Worcestershire Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester WR5 1DD, UK.


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.


artificial intelligence (AI); clinical oncology; deep learning; image guided radiotherapy (IGRT); intensity modulated radiotherapy (IMRT); machine learning; radiotherapy; stereotactic ablative radiotherapy (SABR); target volume delineation; volumetric modulated arc therapy (VMAT)

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