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Cited by 33AI in cancer detectionHead and Neck Cancer StudiesArtificial Intelligence in Healthcare and Education

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Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review

Open access
Ng, Wai Tong, But, Barton, Choi, Horace CW, de Bree, Remco, Lee, Anne WM, Lee, Victor HF, López, Fernando, Mäkitie, Antti A, Rodrigo, Juan P, Saba, Nabil F, Tsang, Raymond KY, Ferlito, Alfio·2022·Cancer Management and Research
DOI 10.2147/cmar.s341583PMC PMC8801370
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Abstract

INTRODUCTION: Nasopharyngeal carcinoma (NPC) is endemic to Eastern and South-Eastern Asia, and, in 2020, 77% of global cases were diagnosed in these regions. Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. METHODS: The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles. RESULTS: A total of 78 articles were reviewed after removing duplicates and papers that did not meet the inclusion and exclusion criteria. After quality assessment, 60 papers were included in the current study. There were four main types of applications, which were auto-contouring, diagnosis, prognosis, and miscellaneous applications (especially on radiotherapy planning). The different forms of convolutional neural networks (CNNs) accounted for the majority of DL algorithms used, while the artificial neural network (ANN) was the most frequent ML model implemented. CONCLUSION: There is an overall positive impact identified from AI implementation in the management of NPC. With improving AI algorithms, we envisage AI will be available as a routine application in a clinical setting soon.

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  • Emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies.

    “Similarly, emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies”

    From A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling (2026) · cites this paper as [50]

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Metadata

Authors
Ng, W. T., But, B., Choi, H. C., de Bree, R., Lee, A. W., Lee, V. H., López, F., Mäkitie, A. A., Rodrigo, J. P., Saba, N. F., Tsang, R. K., & Ferlito, A.
Affiliations
  • University of Hong Kong - Shenzhen Hospital (CN)
  • University of Hong Kong (HK)
  • University Medical Center Utrecht (NL)
  • Universidad de Oviedo (ES)
  • Karolinska University Hospital (SE)
  • Emory University (US)
Corresponding author
But, Barton
ORCID
Venue
Cancer Management and Research
Publisher
Informa UK Limited
Published
2022-01
Volume
Volume 14
Pages
339-366
ISSN
1179-1322 (ISSN-L)
DOI
10.2147/cmar.s341583
PMC
PMC8801370
OpenAlex
W4207029416
Cited by
33
References
68
Citations by year
2026: 9 · 2025: 9 · 2024: 7 · 2023: 5 · 2022: 3
FWCI
0.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Cancer Management and Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion)
  • Työväentutkimus Vuosikirja (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Otorhinolaryngology › Head and Neck Cancer Studies
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
nasopharyngeal carcinoma, artificial intelligence, deep learning, head and neck cancer, auto-contouring, cancer diagnosis, prognosis, radiotherapy treatment planning, convolutional neural networks, artificial neural networks

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