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Artificial Intelligence-based methods in head and neck cancer diagnosis : an overview
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Mahmood, Hanya, Shaban, Muhammad, Rajpoot, Nasir M. (Nasir Mahmood) and Khurram, Syed A (2021) Artificial Intelligence-based methods in head and neck cancer diagnosis : an overview. British Journal of Cancer, 124 . pp. 1934-1940. doi:10.1038/s41416-021-01386-x ISSN 0007-0920.
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WRAP-artificial-intelligence-based-methods-head-neck-cancer-diagnosis-Rajpoot-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (919Kb) | Preview |
Official URL: http://dx.doi.org/10.1038/s41416-021-01386-x
Abstract
Background:
This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis.
Methods:
Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009–2020). No restrictions were placed on the AI/ML method or imaging modality used.
Results:
In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%).
Conclusions:
There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | R Medicine > RC Internal medicine | ||||||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Head -- Cancer -- Diagnosis -- , Neck -- Cancer -- Diagnosis -- , Head -- Cancer -- Imaging , Neck -- Cancer -- Imaging , Diagnostic imaging -- Digital techniques | ||||||||||||||||||
| Journal or Publication Title: | British Journal of Cancer | ||||||||||||||||||
| Publisher: | Nature Publishing Group | ||||||||||||||||||
| ISSN: | 0007-0920 | ||||||||||||||||||
| Official Date: | 19 April 2021 | ||||||||||||||||||
| Dates: |
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| Volume: | 124 | ||||||||||||||||||
| Page Range: | pp. 1934-1940 | ||||||||||||||||||
| DOI: | 10.1038/s41416-021-01386-x | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 24 June 2021 | ||||||||||||||||||
| Date of first compliant Open Access: | 24 June 2021 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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