Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer
Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer
复制标题
机器学习辅助的自发拉曼光谱分类和特征提取用于喉癌的诊断
DOI：
10.1016/j.compbiomed.2022.105617
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发表时间：
2022-05-21
影响因子：
7.7
通讯作者：
Xu, Jian
中科院分区：
文献类型：
--
作者：
Li, Zheng;Li, Zhongqiang;Xu, Jian
喉癌的早期发现显著提高了生存率，允许更保守的喉保留治疗，并降低了医疗费用。一种非侵入性的光学形式的活检喉癌可以提高早期检出率，允许更准确地监测其复发，并提高术中边缘control.In这项研究中，我们评估了一个拉曼光谱系统的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）和一维卷积神经网络（CNN），我们测量了10例喉癌手术标本中207个正常组织和500个肿瘤组织的拉曼光谱。随机森林分析得出的总体准确性为90.5%，敏感性为88.2%，特异性为92.8%，平均超过10次试验。在50次试验中，1D CNN表现出最高的性能，平均准确度为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（PCs）时，RF和CNN都表现出了很好的性能，但肿瘤PC2除外。这是第一次将CNN辅助的拉曼光谱与提取的特征权重用于识别人喉癌组织。所提出的拉曼光谱特征提取方法先前尚未应用于人类癌症诊断。拉曼光谱学在机器学习（ML）方法的辅助下，有可能作为一种术中非侵入性工具，用于喉癌的快速诊断和边缘检测。
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.