Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection.
Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection.
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DOI：
10.3390/cancers12113364
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发表时间：
2020-11-13
期刊：
影响因子：
5.2
通讯作者：
Chow L
中科院分区：
文献类型：
--
作者：
Jeng MJ;Sharma M;Sharma L;Huang SF;Chang LB;Wu SL;Chow L
我们的研究旨在开发一种新的定量分析方法，以提高口腔癌筛查的口腔癌检测率。我们使用了两种不同的光学技术，一种是基于光的探测技术(VELScope)，另一种是振动光谱技术(拉曼光谱)。首先，我们分别使用PCA-LDA和PCA-QDA分类器对这两种技术的性能进行了分析和评估。拉曼光谱的PCA-LDA准确度为82.9%，灵敏度为80%，特异度为85.7%，而自体荧光图像上感兴趣区的准确度为90%，灵敏度为100%，特异度为80%。然后，我们将这两种技术结合起来，并对它们的性能进行了评估。两种光学技术相结合可区分肿瘤和正常组织，准确率为97.14%，敏感性为100%，特异性为94.3%。我们研究的主要优势是，我们可以通过使用两种完全独立的不同技术来证实我们的结果。这就是为什么两种技术相结合可以提高诊断的敏感性和特异性。在这项研究中，我们开发了一种新的定量分析方法来提高口腔癌筛查的检测能力。我们结合了两种不同的光学技术，一种是基于光的检测技术(视觉增强的病变范围)，另一种是振动光谱技术(拉曼光谱)。材料和方法：入选35例口腔癌手术患者。对35例癌组织和35例正常口腔黏膜(癌旁组织)进行了分析。从35例癌组织和35例对照组冻存标本中获取了35个自体荧光图像和70个拉曼光谱。计算了70个感兴趣区(ROI)的归一化强度和非均质性，以及70个平均拉曼光谱。用线性判别分析(LDA)和二次判别分析(QDA)结合主成分分析(PCA)来区分癌症组和正常对照组。采用两种不同的验证方法，即留一法交叉验证法(LOOCV)和k重交叉验证法对分类率进行验证。结果：利用PCA-LDA和PCA-QDA模型可以区分冷冻保存的正常组织和肿瘤组织。拉曼光谱的主成分分析准确度为82.9%，敏感度为80%，特异度为85.7%，而自体荧光图像上的感兴趣区的准确度为90%，灵敏度为100%，特异度为80%。两种光学技术相结合对肿瘤和正常组的诊断准确率为97.14%，灵敏度为100%，特异度为94.3%。结论：在本研究中，我们结合了两种不同光学技术的数据。此外，使用PCA-LDA和PCA-QDA定量分析模型来区分肿瘤和正常组，为有效的肿瘤诊断创造了一条互补的途径。RS和VELcope分析的误差率分别为17.10%和10%，两者结合后误差率降至3%。
Our study aims to develop a novel quantitative analysis method that can increase the oral cancer detection rate for screening oral cancer. We used two different optical techniques, a light-based detection technique (VELScope) and a vibrational spectroscopic technique (Raman spectroscopy). First, we analyzed and evaluated the performance of these two techniques individually using PCA–LDA, and PCA–QDA classifiers. The PCA–LDA of Raman spectroscopy had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while the region of interests on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. Afterward, we combined both techniques and evaluated their performance. The combination of two optical techniques can differentiate the cancer and normal groups with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. The main advantage of our study is that we can confirm our results by using two different techniques that are completely independent of each other. That is the reason that the combination of two techniques can increase the sensitivity and specificity. In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.
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影响因子：
3.7
作者：
Jeng, Ming-Jer;Sharma, Mukta;Chow, Lee
通讯作者：
Chow, Lee
影响因子：
4.8
作者：
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口腔潜在的恶性疾病：20多个实体的概述。
DOI：
10.5681/joddd.2014.002
发表时间：
2014
期刊：
Journal of dental research, dental clinics, dental prospects
影响因子：
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作者：
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通讯作者：
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影响因子：
2.1
作者：
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影响因子：
3.3
作者：
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通讯作者：
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