Otolaryngology Department, Newcastle-Upon-Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK.
Applied Cancer Therapeutics and Outcomes, Newcastle University, Newcastle Upon Tyne, UK.
Br J Radiol. 2021 Dec;94(1128):20210499. doi: 10.1259/bjr.20210499. Epub 2021 Sep 29.
Radiomics is the conversion of medical images into quantitative high-dimensional data. Laryngeal cancer, one of the most common head and neck cancers, has risen globally by 58.7%. CT, MRI and PET are acquired during the diagnostic process providing potential data for radiomic analysis and correlation with outcomes.This review aims to examine the applications of this technique to laryngeal cancer and the future considerations for translation into clinical practice.
A comprehensive systematic review-informed search of the MEDLINE and EMBASE databases was undertaken. Keywords "laryngeal cancer" OR "larynx" OR "larynx cancer" OR "head and neck cancer" were combined with "radiomic" OR "signature" OR "machine learning" OR "artificial intelligence". Additional articles were obtained from bibliographies using the "snowball method".
The included studies ( = 15) demonstrated that radiomic features are significantly associated with various clinical outcomes (including stage, overall survival, treatment response, progression-free survival) and that predictive models incorporating radiomic features are superior to those that do not. Two studies demonstrated radiomics could improve laryngeal cancer staging whilst 12 studies affirmed its predictive capability for clinical outcomes.
Radiomics has potential for improving multiple aspects of laryngeal cancer care; however, the heterogeneous cohorts and lack of data on laryngeal cancer exclusively inhibits firm conclusions. Large prospective well-designed studies in laryngeal cancer are required to progress this field. Furthermore, to implement radiomics into clinical practice, a unified research effort is required to standardise radiomics practice.
This review has highlighted the value of radiomics in enhancing laryngeal cancer care (including staging, prognosis and predicting treatment response).
放射组学是将医学图像转化为定量的高维数据。喉癌是最常见的头颈部癌症之一，在全球范围内发病率上升了 58.7%。在诊断过程中会获取 CT、MRI 和 PET，为放射组学分析提供潜在的数据，并与结果相关联。本综述旨在探讨该技术在喉癌中的应用以及将其转化为临床实践的未来考虑。
对 MEDLINE 和 EMBASE 数据库进行了全面的系统综述，并对关键词“喉癌”或“喉”或“喉癌”或“头颈部癌症”与“放射组学”或“特征”或“机器学习”或“人工智能”进行了组合。使用“滚雪球法”从参考文献中获得了其他文章。
纳入的研究（n=15）表明，放射组学特征与各种临床结局（包括分期、总生存率、治疗反应、无进展生存率）显著相关，并且包含放射组学特征的预测模型优于不包含的模型。两项研究表明放射组学可以改善喉癌分期，而 12 项研究证实其具有预测临床结局的能力。
放射组学有可能改善喉癌治疗的多个方面；然而，异质队列和缺乏专门针对喉癌的数据限制了得出确切结论。需要在喉癌中进行大型前瞻性精心设计的研究，以推进这一领域的发展。此外，为了将放射组学应用于临床实践，需要进行统一的研究努力来规范放射组学的实践。
本综述强调了放射组学在增强喉癌治疗（包括分期、预后和预测治疗反应）方面的价值。
