Litcius/Paper detail

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

2022Journal of Pathology Informatics34 citationsDOIOpen Access PDF

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.

Topics & Concepts

Head and neckHead and neck cancerMedicineMedical physicsHistopathologyPopulationMultidisciplinary approachPathologyCancerSurgeryInternal medicineSocial scienceEnvironmental healthSociologyHead and Neck Cancer StudiesAI in cancer detectionRadiomics and Machine Learning in Medical Imaging