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

Abstract

Wai Tong Ng,1,2 Barton But,2 Horace CW Choi,3 Remco de Bree,4 Anne WM Lee,1,2 Victor HF Lee,1,2 Fernando López,5,6 Antti A Mäkitie,7– 9 Juan P Rodrigo,5,6 Nabil F Saba,10 Raymond KY Tsang,11 Alfio Ferlito12 1Clinical Oncology Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China; 2Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; 3Department of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; 4Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; 5Department of Otolaryngology, Hospital Universitario Central de Asturias (HUCA), Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Instituto Universitario de Oncología del Principado de Asturias (IUOPA), University of Oviedo, Oviedo, 33011, Spain; 6Spanish Biomedical Research Network Centre in Oncology, CIBERONC, Madrid, 28029, Spain; 7Department of Otorhinolaryngology - Head and Neck Surgery, HUS Helsinki University Hospital and University of Helsinki, Helsinki, Finland; 8Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; 9Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden; 10Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, USA; 11Division of Otorhinolaryngology, Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People’s Republic of China; 12Coordinator of the International Head and Neck Scientific Group, Padua, ItalyCorrespondence: Barton ButDepartment of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]: 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.Keywords: machine learning, neural network, deep learning, prognosis, diagnosis, auto contouring

Keywords

• machine learning
• neural network
• deep learning
• prognosis
• diagnosis
• auto contouring