Objectives: This scoping review aimed to provide both researchers and practitioners with an overview of how machine learning (ML) methods are applied to infrared spectroscopy for the diagnosis and prognosis of head and neck precancer and cancer. Methods: A subject headings and keywords search was conducted in MEDLINE, Embase, and Scopus on 14 January 2024, using predefined search algorithms targeting studies that integrated infrared spectroscopy and ML methods in head and neck precancer/cancer research. The results were managed through the COVIDENCE systematic review platform. Results: Fourteen studies met the eligibility criteria, which were defined by IR spectroscopy techniques, ML methodology, and a focus on head and neck precancer/cancer research involving human subjects. The IR spectroscopy techniques used in these studies included Fourier transform infrared (FTIR) spectroscopy and imaging, attenuated total reflection-FTIR, near-infrared spectroscopy, and synchrotron-based infrared microspectroscopy. The investigated human biospecimens included tissues, exfoliated cells, saliva, plasma, and urine samples. ML methods applied in the studies included linear discriminant analysis (LDA), principal component analysis with LDA, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, support vector machine, extreme gradient boosting, canonical variate analysis, and deep reinforcement neural network. For oral cancer diagnosis applications, the highest sensitivity and specificity were reported to be 100%, the highest accuracy was reported to be 95–96%, and the highest area under the curve score was reported to be 0.99. For oral precancer prognosis applications, the highest sensitivity and specificity were reported to be 84% and 79%, respectively. Conclusions: This review highlights the promising potential of integrating infrared spectroscopy with ML methods for diagnosing and prognosticating head and neck precancer and cancer. However, the limited sample sizes in existing studies restrict generalizability of the study findings. Future research should prioritize larger datasets and the development of advanced ML models to enhance reliability and robustness of these tools.
目的：本范围综述旨在为研究人员和临床医生提供关于机器学习方法如何应用于红外光谱技术以诊断和预测头颈部癌前病变及癌症的概览。方法：于2024年1月14日，使用针对红外光谱与机器学习方法在头颈部癌前病变/癌症研究中整合应用的预定义检索算法，在MEDLINE、Embase和Scopus数据库中进行主题词和关键词检索。结果通过COVIDENCE系统综述平台进行管理。结果：共有14项研究符合纳入标准，这些标准基于红外光谱技术、机器学习方法以及聚焦于涉及人类受试者的头颈部癌前病变/癌症研究。这些研究中使用的红外光谱技术包括傅里叶变换红外光谱及成像、衰减全反射-傅里叶变换红外光谱、近红外光谱以及基于同步辐射的红外显微光谱。研究涉及的人类生物样本包括组织、脱落细胞、唾液、血浆和尿液样本。研究中应用的机器学习方法包括线性判别分析、主成分分析与线性判别分析结合、偏最小二乘判别分析、正交偏最小二乘判别分析、支持向量机、极限梯度提升、典型变量分析以及深度强化神经网络。在口腔癌诊断应用中，报告的最高灵敏度和特异性均为100%，最高准确率为95–96%，最高曲线下面积得分为0.99。在口腔癌前病变预后应用中，报告的最高灵敏度和特异性分别为84%和79%。结论：本综述强调了将红外光谱与机器学习方法相结合在诊断和预测头颈部癌前病变及癌症方面具有广阔前景。然而，现有研究样本量有限，限制了研究结果的普适性。未来研究应优先关注更大规模的数据集和开发更先进的机器学习模型，以提升这些工具的可靠性和稳健性。
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