Artificial intelligence in head and neck cancer diagnosis

Sara Bassani; Nicola Santonicco; Albino Eccher; Aldo Scarpa; Matteo Vianini; Matteo Brunelli; Nicola Bisi; Riccardo Nocini; Luca Sacchetto; Enrico Munari; Liron Pantanowitz; Ilaria Girolami; Gabriele Molteni

doi:10.1016/j.jpi.2022.100153

Artificial intelligence in head and neck cancer diagnosis

J Pathol Inform. 2022 Nov 8:13:100153. doi: 10.1016/j.jpi.2022.100153. eCollection 2022.

Authors

Affiliations

¹ Otolaryngology-Head and Neck Surgery Department, University of Verona, Verona, Italy.
² Department of Diagnostics and Public Health, Section of Pathology, University and Hospital Trust of Verona, Verona, Italy.
³ Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy.
⁴ Department of Otolaryngology, Villafranca Hospital, Verona, Italy.
⁵ Department of Molecular and Translational Medicine, University of Brescia, 25121 Brescia, Italy.
⁶ Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
⁷ Department of Pathology, Provincial Hospital of Bolzano (SABES-ASDAA), Bolzano-Bozen, Italy; Lehrkrankenhaus der Paracelsus Medizinischen Privatuniversität.

Abstract

Introduction: Artificial intelligence (AI) is currently being used to augment histopathological diagnostics in pathology. This systematic review aims to evaluate the evolution of these AI-based diagnostic techniques for diagnosing head and neck neoplasms.

Materials and methods: Articles regarding the use of AI for head and neck pathology published from 1982 until March 2022 were evaluated based on a search strategy determined by a multidisciplinary team of pathologists and otolaryngologists. Data from eligible articles were summarized according to author, year of publication, country, study population, tumor details, study results, and limitations.

Results: Thirteen articles were included according to inclusion criteria. The selected studies were published between 2012 and March 1, 2022. Most of these studies concern the diagnosis of oral cancer; in particular, 6 are related to the oral cavity, 2 to the larynx, 1 to the salivary glands, and 4 to head and neck squamous cell carcinoma not otherwise specified (NOS). As for the type of diagnostics considered, 12 concerned histopathology and 1 cytology.

Discussion: Starting from the pathological examination, artificial intelligence tools are an excellent solution for implementing diagnosis capability. Nevertheless, today the unavailability of large training datasets is a main issue that needs to be overcome to realize the true potential.

Keywords: Artificial intelligence; Diagnosis; Head and neck cancer; Pathology; Whole slide imaging.

Publication types

Review