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Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer

Kosmin, M; Ledsam, J; Romera-Paredes, B; Mendes, R; Moinuddin, S; de Souza, D; Gunn, L; ... Sharma, RA; + view all (2019) Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer. Radiotherapy and Oncology , 135 pp. 130-140. 10.1016/j.radonc.2019.03.004.

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Abstract

Advances in technical radiotherapy have resulted in significant sparing of organs at risk (OARs), reducing radiation-related toxicities for patients with cancer of the head and neck (HNC). Accurate delineation of target volumes (TVs) and OARs is critical for maximising tumour control and minimising radiation toxicities. When performed manually, variability in TV and OAR delineation has been shown to have significant dosimetric impacts for patients on treatment. Auto-segmentation (AS) techniques have shown promise in reducing both inter-practitioner variability and the time taken in TV and OAR delineation in HNC. Ultimately, this may reduce treatment planning and clinical waiting times for patients. Adaptation of radiation treatment for biological or anatomical changes during therapy will also require rapid re-planning; indeed, the time taken for manual delineation currently prevents adaptive radiotherapy from being implemented optimally. We are therefore standing on the threshold of a transformation of routine radiotherapy planning via the use of artificial intelligence. In this article, we outline the current state-of-the-art for AS for HNC radiotherapy in order to predict how this will rapidly change with the introduction of artificial intelligence. We specifically focus on delineation accuracy and time saving. We argue that, if such technologies are implemented correctly, AS should result in better standardisation of treatment for patients and significantly reduce the time taken to plan radiotherapy.

Type: Article
Title: Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer
Location: Ireland
DOI: 10.1016/j.radonc.2019.03.004
Publisher version: https://doi.org/10.1016/j.radonc.2019.03.004
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Artificial intelligence, Deep learning, Radiotherapy, Segmentation
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
URI: https://discovery.ucl.ac.uk/id/eprint/10073917
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