Literature DB >> 27248772

Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck.

Jia Yi Lim1,2, Michelle Leech1.   

Abstract

BACKGROUND: Manual delineation of structures in head and neck cancers is an extremely time-consuming and labor-intensive procedure. With centers worldwide moving towards the use of intensity-modulated radiotherapy and adaptive radiotherapy, there is a need to explore and analyze auto-segmentation (AS) software, in the search for a faster yet accurate method of structure delineation.
MATERIAL AND METHODS: A search for studies published after 2005 comparing AS and manual delineation in contouring organ at risks (OARs) and target volume for head and neck patients was conducted. The reviewed results were then categorized into arguments proposing and opposing the review title.
RESULTS: Ten studies were reviewed and derived results were assessed in terms of delineation time-saving ability and extent of delineation accuracy. The influence of other external factors (observer variability, AS strategies adopted and stage of disease) were also considered. Results were conflicting with some studies demonstrating great potential in replacing manual delineation whereas other studies illustrated otherwise. Six of 10 studies investigated time saving; the largest time saving reported being 59%. However, one study found that additional time of 15.7% was required for AS. Four studies reported AS contours to be between 'reasonably good' and 'better quality' than the clinically used contours. Remaining studies cited lack of contrast, AS strategy used and the need for physician intervention as limitations in the standardized use of AS. DISCUSSION: The studies demonstrated significant potential of AS as a useful delineation tool in contouring target volumes and OARs in head and neck cancers. However, it is evident that AS cannot totally replace manual delineation in contouring some structures in the head and neck and cannot be used independently without human intervention. It is also emphasized that delineation studies should be conducted locally so as to evaluate the true value of AS in head and neck cancers in a specific center.

Entities:  

Mesh:

Year:  2016        PMID: 27248772     DOI: 10.3109/0284186X.2016.1173723

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  16 in total

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3.  A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases.

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Journal:  Front Oncol       Date:  2020-09-23       Impact factor: 6.244

10.  Technical note: Atlas-based Auto-segmentation of masticatory muscles for head and neck cancer radiotherapy.

Authors:  Xiangguo Zhang; Haihui Chen; Wen Chen; Brandon A Dyer; Quan Chen; Stanley H Benedict; Shyam Rao; Yi Rong
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