Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A Systematic Review
Year: 2022
Type: review
Study design: Systematic Review
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: Cancer Management and Research
DOI: 10.2147/cmar.s341583
Authors: Wai Tong Ng, Barton But, Horace CW Choi, Remco de Bree, Anne WM Lee, Victor HF Lee, Fernando López, Antti A Mäkitie, Juan P Rodrigo, Nabil F Saba, Raymond KY Tsang, Alfio Ferlito
Institutions: University of Hong Kong - Shenzhen Hospital, University of Hong Kong, University Medical Center Utrecht, Universidad de Oviedo, Hospital Universitario Central de Asturias, Centro de Investigación Biomédica en Red de Cáncer, Instituto de Investigación Sanitaria del Principado de Asturias, Karolinska University Hospital, University of Helsinki, Helsinki University Hospital, Karolinska Institutet, Hospital District of Helsinki and Uusimaa, Emory University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,836
OpenAlex ID: W4207029416