Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Division of Computer Science & Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
Comput Biol Med. 2022 Jul;146:105617. doi: 10.1016/j.compbiomed.2022.105617. Epub 2022 May 18.
The early detection of laryngeal cancer significantly increases the survival rates, permits more conservative larynx sparing treatments, and reduces healthcare costs. A non-invasive optical form of biopsy for laryngeal carcinoma can increase the early detection rate, allow for more accurate monitoring of its recurrence, and improve intraoperative margin control. In this study, we evaluated a Raman spectroscopy system for the rapid intraoperative detection of human laryngeal carcinoma. The spectral analysis methods included principal component analysis (PCA), random forest (RF), and one-dimensional (1D) convolutional neural network (CNN) methods. We measured the Raman spectra from 207 normal and 500 tumor sites collected from 10 human laryngeal cancer surgical specimens. Random Forest analysis yielded an overall accuracy of 90.5%, sensitivity of 88.2%, and specificity of 92.8% on average over 10 trials. The 1D CNN demonstrated the highest performance with an accuracy of 96.1%, sensitivity of 95.2%, and specificity of 96.9% on average over 50 trials. In predicting the first three principal components (PCs) of normal and tumor data, both RF and CNN demonstrated high performances, except for the tumor PC2. This is the first study in which CNN-assisted Raman spectroscopy was used to identify human laryngeal cancer tissue with extracted feature weights. The proposed Raman spectroscopy feature extraction approach has not been previously applied to human cancer diagnosis. Raman spectroscopy, as assisted by machine learning (ML) methods, has the potential to serve as an intraoperative, non-invasive tool for the rapid diagnosis of laryngeal cancer and margin detection.
喉癌的早期检测显著提高了生存率，允许采用更保守的保留喉功能治疗，并降低了医疗保健成本。一种用于喉癌的非侵入性光学活检方法可以提高早期检测率，更准确地监测其复发，并改善术中切缘控制。在这项研究中，我们评估了一种拉曼光谱系统，用于快速术中检测人喉癌。光谱分析方法包括主成分分析（PCA）、随机森林（RF）和一维（1D）卷积神经网络（CNN）方法。我们从 10 个人喉癌手术标本中收集的 207 个正常和 500 个肿瘤部位测量了拉曼光谱。随机森林分析在 10 次试验中的平均总准确率为 90.5%，灵敏度为 88.2%，特异性为 92.8%。1D CNN 的表现最佳，在 50 次试验中的平均准确率为 96.1%，灵敏度为 95.2%，特异性为 96.9%。在预测正常和肿瘤数据的前三个主成分（PC）时，RF 和 CNN 的表现都很高，除了肿瘤 PC2。这是首次使用 CNN 辅助拉曼光谱来识别具有提取特征权重的人喉癌组织的研究。所提出的拉曼光谱特征提取方法以前未应用于人类癌症诊断。拉曼光谱与机器学习（ML）方法相结合，有望成为一种用于快速诊断喉癌和切缘检测的术中非侵入性工具。
