Alajaji Shahd A, Sabzian Roya, Wang Yong, Sultan Ahmed S, Wang Rong
Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland, 650 W. Baltimore Street, 7 Floor, Baltimore, MD 21201, USA.
Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh 12371, Saudi Arabia.
Cancers (Basel). 2025 Feb 26;17(5):796. doi: 10.3390/cancers17050796.
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.
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.
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.
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.
本范围综述旨在为研究人员和从业者提供关于机器学习（ML）方法如何应用于红外光谱以对头颈部癌前病变和癌症进行诊断和预后评估的概述。
于2024年1月14日在MEDLINE、Embase和Scopus数据库中进行主题词和关键词检索，使用预定义的搜索算法，目标是检索在头颈部癌前病变/癌症研究中整合红外光谱和ML方法的研究。结果通过COVIDENCE系统综述平台进行管理。
14项研究符合纳入标准，这些标准由红外光谱技术、ML方法以及对头颈部癌前病变/癌症涉及人类受试者的研究重点来定义。这些研究中使用的红外光谱技术包括傅里叶变换红外（FTIR）光谱和成像、衰减全反射 - FTIR、近红外光谱以及基于同步加速器的红外显微光谱。所研究的人类生物标本包括组织、脱落细胞、唾液、血浆和尿液样本。研究中应用的ML方法包括线性判别分析（LDA）、主成分分析结合LDA、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、极端梯度提升、典型变量分析和深度强化神经网络。对于口腔癌诊断应用，报道的最高灵敏度和特异性为100%，最高准确率为95 - 96%，最高曲线下面积分数为0.99。对于口腔癌前病变预后应用，报道的最高灵敏度和特异性分别为84%和79%。
本综述强调了将红外光谱与ML方法相结合用于头颈部癌前病变和癌症诊断及预后评估的潜在前景。然而，现有研究中样本量有限限制了研究结果的可推广性。未来研究应优先考虑更大的数据集以及开发先进的ML模型，以提高这些工具的可靠性和稳健性。
