Please use this identifier to cite or link to this item: https://doi.org/10.2147/CMAR.S341583
Title: Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review
Authors: Ng, WT
But, B
Choi, HCW
de Bree, R
Lee, AWM
Lee, VHF
López, F
Mäkitie, AA
Rodrigo, JP
Saba, NF
Tsang, RKY 
Ferlito, A
Keywords: auto contouring
deep learning
diagnosis
machine learning
neural network
prognosis
Issue Date: 1-Jan-2022
Publisher: Informa UK Limited
Citation: Ng, WT, But, B, Choi, HCW, de Bree, R, Lee, AWM, Lee, VHF, López, F, Mäkitie, AA, Rodrigo, JP, Saba, NF, Tsang, RKY, Ferlito, A (2022-01-01). Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review. Cancer Management and Research 14 : 339-366. ScholarBank@NUS Repository. https://doi.org/10.2147/CMAR.S341583
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.
Source Title: Cancer Management and Research
URI: https://scholarbank.nus.edu.sg/handle/10635/238343
ISSN: 1179-1322
DOI: 10.2147/CMAR.S341583
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