Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/33070
Title: Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews.
Austin Authors: Mäkitie, Antti A;Alabi, Rasheed Omobolaji;Ng, Sweet Ping ;Takes, Robert P;Robbins, K Thomas;Ronen, Ohad;Shaha, Ashok R;Bradley, Patrick J;Saba, Nabil F;Nuyts, Sandra;Triantafyllou, Asterios;Piazza, Cesare;Rinaldo, Alessandra;Ferlito, Alfio
Affiliation: Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital, University of Helsinki Finland
Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
Department of Surgery, The University of Melbourne, Melbourne, Australia
Department of Otolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Otolaryngology Head Neck Surgery, SIU School of Medicine, Southern Illinois University, Springfield, IL, USA.
Department of Otolaryngology-Head and Neck Surgery, Galilee Medical Center Affiliated with Azrieil Faculty of Medicine, Bar Ilan University, Safed, Israel.
Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
The University of Nottingham, Department of ORLHNS, Queens Medical Centre Campus, Nottingham University Hospital, Derby Road, Nottingham, NG7 2UH, UK.
Department of Hematology and Medical Oncology, The Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Laboratory of Experimental Radiotherapy, Department of Oncology, KU Leuven, 3000, Leuven, Belgium.
Department of Pathology, Liverpool Clinical Laboratories, School of Dentistry, University of Liverpool, Liverpool, UK.
Unit of Otorhinolaryngology-Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
ENT Unit, Policlinico Città di Udine, 33100, Udine, Italy.
Coordinator of the International Head and Neck Scientific Group, Padua, Italy.
Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
Radiation Oncology
Olivia Newton-John Cancer Wellness and Research Centre
School of Cancer Medicine, La Trobe University, Melbourne, Australia
epartment of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, 3000, Leuven, Belgium.
epartment of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy.
School of Imaging and Radiation Sciences, Monash University, Melbourne, Australia
Issue Date: Aug-2023
Date: 2023
Publication information: Advances in Therapy 2023; 40(8)
Abstract: 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. 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. 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. 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/33070
DOI: 10.1007/s12325-023-02527-9
ORCID: 0000-0002-0451-2404
0000-0001-7655-5924
0000-0003-2810-5704
0000-0001-7956-6709
0000-0002-8247-8002
Journal: Advances in Therapy
PubMed URL: 37291378
ISSN: 1865-8652
Type: Journal Article
Subjects: Artificial intelligence
Head and neck cancer
Machine learning
Systematic review
Appears in Collections:Journal articles

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