Jeng Ming-Jer, Sharma Mukta, Sharma Lokesh, Huang Shiang-Fu, Chang Liann-Be, Wu Shih-Lin, Chow Lee
Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan.
Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan.
Cancers (Basel). 2020 Nov 13;12(11):3364. doi: 10.3390/cancers12113364.
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). 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 -fold cross-validation. 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. 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.
在本研究中，我们开发了一种新型定量分析方法以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，一种基于光的检测技术（视觉增强病变范围）和一种振动光谱技术（拉曼光谱）。招募了35名接受手术的口腔癌患者。对35个癌性病变和35个正常口腔黏膜（与癌性病变相邻）的对照样本进行了分析。从35个癌性和35个对照组的冷冻样本中获取了35张自发荧光图像和70条拉曼光谱。计算了70个感兴趣区域（ROI）的归一化强度和异质性以及70条平均拉曼光谱。使用线性判别分析（LDA）和二次判别分析（QDA）以及主成分分析（PCA）来区分癌性和对照组（正常组）。使用两种不同的验证方法，留一法交叉验证（LOOCV）和k折交叉验证对分类率进行了验证。使用PCA-LDA和PCA-QDA模型对冷冻保存的正常和肿瘤组织进行了区分。拉曼光谱（RS）的PCA-LDA具有82.9%的准确率、80%的灵敏度和85.7%的特异性，而自发荧光图像上的ROI区分准确率为90%、灵敏度为100%、特异性为80%。两种光学技术的组合区分癌性和正常组的准确率为97.14%、灵敏度为100%、特异性为94.3%。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，为高效肿瘤诊断创建了一条互补途径。RS和VELcope分析的错误率分别为17.10%和10%，当两种光学技术结合时降低到了3%。
