Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2023 Aug;40(8):3360-3380.
doi: 10.1007/s12325-023-02527-9. Epub 2023 Jun 8.

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

Antti A Mäkitie  1   2   3 , Rasheed Omobolaji Alabi  4   5 , Sweet Ping Ng  6   7   8   9 , Robert P Takes  10 , K Thomas Robbins  11 , Ohad Ronen  12 , Ashok R Shaha  13 , Patrick J Bradley  14 , Nabil F Saba  15 , Sandra Nuyts  16   17 , Asterios Triantafyllou  18 , Cesare Piazza  19   20 , Alessandra Rinaldo  21 , Alfio Ferlito  22
Affiliations
Meta-Analysis

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

Antti A Mäkitie et al. Adv Ther. 2023 Aug.

Abstract

Introduction: Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management.

Methods: Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines.

Results: Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks.

Conclusion: At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.

Keywords: Artificial intelligence; Head and neck cancer; Machine learning; Systematic review.

PubMed Disclaimer

Conflict of interest statement

Antti A. Mäkitie, Rasheed Omobolaji Alabi, Sweet Ping Ng, Robert P. Takes, K. Thomas Robbins, Ohad Ronen, Ashok R. Shaha, Patrick J Bradley, Nabil F Saba, Sandra Nuyts, Asterios Triantafyllou, Cesare Piazza, Alessandra Rinaldo, and Alfio Ferlito all have nothing to disclose.

Figures

Fig. 1
Fig. 1
The PRISMA flowchart
Fig. 2
Fig. 2
Workflow of ML model development for outcome prediction
See this image and copyright information in PMC

References

    1. Mahmood H, Shaban M, Rajpoot N, Khurram SA. Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview. Br J Cancer. 2021 doi: 10.1038/s41416-021-01386-x. - DOI - PMC - PubMed
    1. Svider PF, Blasco MA, Raza SN, Shkoukani M, Sukari A, Yoo GH, et al. Head and neck cancer: underfunded and understudied? Otolaryngol Head Neck Surg. 2017;156:10–13. doi: 10.1177/0194599816674672. - DOI - PubMed
    1. Pai SI, Westra WH. Molecular pathology of head and neck cancer: implications for diagnosis, prognosis, and treatment. Annu Rev Pathol Mech Dis. 2009;4:49–70. doi: 10.1146/annurev.pathol.4.110807.092158. - DOI - PMC - PubMed
    1. Muto M, Nakane M, Katada C, Sano Y, Ohtsu A, Esumi H, et al. Squamous cell carcinoma in situ at oropharyngeal and hypopharyngeal mucosal sites. Cancer. 2004;101:1375–1381. doi: 10.1002/cncr.20482. - DOI - PubMed
    1. Alabi RO, Bello IO, Youssef O, Elmusrati M, Mäkitie AA, Almangush A. Utilizing deep machine learning for prognostication of oral squamous cell carcinoma—a systematic review. Front Oral Health. 2021 doi: 10.3389/froh.2021.686863. - DOI - PMC - PubMed

Publication types