Wu I-Chen, Chen Yen-Chun, Karmakar Riya, Mukundan Arvind, Gabriel Gahiga, Wang Chih-Chiang, Wang Hsiang-Chen
Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No. 100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan.
Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No. 100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan.
Biomedicines. 2024 Oct 11;12(10):2315. doi: 10.3390/biomedicines12102315.
Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Despite these promising findings, challenges persist, such as HSI's sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI's clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.
头颈癌（HNC），主要是鳞状细胞癌（SCC），是一个重大的全球健康负担。传统的诊断方法在实现早期检测和准确诊断方面常常面临挑战。本综述探讨了高光谱成像（HSI）与计算机辅助诊断（CAD）技术相结合的最新进展，以加强对头颈癌的检测和诊断。我们对七项经过严格筛选的研究进行了系统综述。我们重点关注了CAD算法，如卷积神经网络（CNN）、支持向量机（SVM）和线性判别分析（LDA）。这些算法适用于头颈癌组织的高光谱成像。荟萃分析结果表明，LDA优于其他算法，准确率达到92%，灵敏度为91%，特异性为93%。CNN表现出中等性能，准确率为82%，灵敏度为77%，特异性为86%。SVM的性能最低，准确率为76%，灵敏度为48%，但特异性保持在较高水平，为89%。此外，体内研究与体外研究相比表现出更优的性能，报告的准确率更高（81%）、灵敏度更高（83%）和特异性更高（79%）。尽管有这些有前景的发现，但挑战依然存在，例如HSI对外部条件的敏感性、对高分辨率和高速成像的需求，以及缺乏全面的光谱数据库。未来的研究应强调降维技术、多种机器学习模型的整合，以及开发广泛的光谱库，以提高HSI在头颈癌诊断中的临床效用。本综述强调了HSI和CAD技术在彻底改变头颈癌诊断方面的变革潜力，有助于更准确和早期的检测，并改善患者的治疗结果。
