Bensoussan Yael, Vanstrum Erik B, Johns Michael M, Rameau Anaïs
Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, Florida, USA.
Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Otolaryngol Head Neck Surg. 2023 Mar;168(3):319-329. doi: 10.1177/01945998221110839. Epub 2023 Jan 29.
This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC).
Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE.
A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness.
AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality.
AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.
本综述旨在探讨人工智能（AI）在喉癌（LC）筛查、检测、管理和预后评估方面的当代进展。
检索了四个文献数据库：PubMed、EMBASE、Cochrane和IEEE。
对当前文献（截至2022年1月）进行结构化综述。由两名独立评审员确定并查询与LC中AI主题相关的检索词。还对所选研究和综述文章的参考文献进行了评估，以确保全面性。
AI在LC中的应用涵盖了多种数据模式，包括放射组学、基因组学、声学、临床数据和视频组学，以支持筛查、诊断、治疗决策和预后评估。然而，大多数研究仍处于概念验证阶段，因为AI算法是在单机构数据库上进行训练的，数据集有限且数据模式单一。
LC中的AI算法需要在大型多机构数据集上进行训练，并整合多模式数据，以实现从筛查到预后的最佳性能和临床应用价值。在所审查的数据类型中，由于有可用的大型多机构开放获取基因组数据集，基因组学最有潜力提供可推广的模型。语音声学数据是一种廉价且准确的生物标志物，易于且无创获取，为喉癌的筛查和监测提供了独特的机会，尤其是在资源匮乏的环境中。
