Application of Artificial Intelligence for Nasopharyngeal Carcinoma Management – A 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.

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

“…All the articles included in this review were published in English. Of the 17 included systematic reviews [ 1 , 5 , 7 , 13 – 26 ], 11 were conducted in Europe [ 1 , 5 , 7 , 14 , 15 , 17 – 19 , 21 , 22 , 24 ] while 4 were conducted in Asia [ 16 , 23 , 25 , 26 ] and 2 in the United States [ 13 , 20 ] (Table 2 ). All but one of the included systematic reviews showed high-quality appraisal and low risk of bias (Tables 3 , 4 ).…”

“…The findings of the published systematic reviews (Table 1 ) suggest that the application of AI and its subfields in HNC can be summarized in 5 distinct fundamental themes: (1) detection of precancerous and cancerous lesions in histopathologic slides [ 7 , 18 ]; (2) prediction of histopathologic nature of a given lesion from imaging [ 1 , 5 , 13 – 15 , 17 , 21 , 22 , 24 – 26 ]; (3) prognostication [ 5 , 17 , 18 , 20 , 21 , 23 ]; (4) extraction of pathological findings from imaging [ 15 , 16 , 20 ]; and (5) applications in radiation oncology [ 15 , 17 , 19 ].…”

The role of Epstein–Barr virus in nasopharyngeal carcinoma

“…Artificial intelligence is developed to provide insights into intervention strategies for precision medicine. Researchers have developed machine learning and deep learning models for the prediction of survival, disease progression, and treatment response for NPC patients by integrating several clinical parameters, including images, demographic characteristics, plasma EBV-DNA, serum biochemical profile, treatment regimen and others ( Jing et al, 2019 ; Zhong et al, 2021 ; Chen et al, 2021a ; Zhang et al, 2021a ; Ng et al, 2022 ).…”

Current Radiotherapy Considerations for Nasopharyngeal Carcinoma

“…Numerous studies have examined the role of AI-assisted target delineation for NPC [ 81 ]. The most widely studied area of application is the auto-segmentation of head and neck OARs.…”