Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital, University of Helsinki, P.O. Box 263, 00029, HUS, Helsinki, Finland.
Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Adv Ther. 2023 Aug;40(8):3360-3380. doi: 10.1007/s12325-023-02527-9. Epub 2023 Jun 8.
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.
多项研究强调了人工智能（AI）及其子领域（如机器学习（ML））的潜力，认为它们是优化肿瘤患者治疗的新兴可行方法。因此，临床医生和决策者面临着大量关于 AI 在头颈部癌症（HNC）管理中应用的最新研究综述。本文对当前 AI/ML 作为 HNC 管理辅助决策工具的应用现状及局限性进行了系统综述分析。
从建库到 2022 年 11 月 30 日，我们在电子数据库（PubMed、Ovid 上的 Medline、Scopus 和 Web of Science）中进行了检索。研究选择、搜索和筛选过程、纳入和排除标准均遵循系统评价和荟萃分析的首选报告项目（PRISMA）指南。使用定制和修改版的评估系统评价（AMSTAR-2）工具进行偏倚风险评估，并使用系统评价偏倚风险（ROBIS）指南进行质量评估。
在检索到的 137 个检索结果中，有 17 个符合纳入标准。对这些系统综述的分析表明，将 AI/ML 作为 HNC 管理的决策辅助工具的应用可归纳为以下几类：（1）在组织病理学切片中检测癌前和癌性病变；（2）根据各种来源的医学影像学预测给定病变的组织病理学性质；（3）预测预后；（4）从影像学中提取病理发现；（5）在放射肿瘤学中的不同应用。此外，在临床评估中实施 AI/ML 模型面临的挑战包括缺乏用于收集临床图像、开发这些模型、报告其性能、外部验证程序和监管框架的标准化方法学指南。
由于上述局限性，目前几乎没有证据表明这些模型可在临床实践中得到应用。因此，本文强调需要制定标准化指南，以促进这些模型在日常临床实践中的采用和实施。此外，迫切需要进行更有力的、前瞻性的、随机对照试验，以进一步评估 AI/ML 模型在真实世界的 HNC 临床环境中管理的潜力。
