Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

• Published in: British journal of cancer Vol. 124; no. 12; pp. 1934 - 1940
• Main Authors: Mahmood, Hanya, Shaban, Muhammad, Rajpoot, Nasir, Khurram, Syed A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.06.2021; Nature Publishing Group
• Subjects: 631/67/1536; 692/4028/67/1536; Artificial Intelligence; Biomedical and Life Sciences; Biomedicine; Cancer Research; Deep learning; Drug Resistance; Epidemiology; Head & neck cancer; Head and Neck Neoplasms - diagnosis; Head and Neck Neoplasms - pathology; Humans; Image processing; Image Processing, Computer-Assisted - methods; Larynx; Learning algorithms; Machine Learning; Medical diagnosis; Medical Oncology - methods; Medical Oncology - trends; Molecular Medicine; Nasopharynx; Oncology; Oral cavity; Oropharynx; Pattern Recognition, Automated; Reviews; Salivary gland
• ISSN: 0007-0920; 1532-1827; 1532-1827
• DOI: 10.1038/s41416-021-01386-x

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.