Wang Rong, Naidu Aparna, Wang Yong
School of Dentistry, University of Missouri-Kansas City, Kansas City, MO 64108, USA.
Oral Surgery and Pathology, Truman Medical Center, Kansas City, MO 64108, USA.
Diagnostics (Basel). 2021 Nov 17;11(11):2133. doi: 10.3390/diagnostics11112133.
The Fourier transform infrared (FTIR) imaging technique was used in a transmission model for the evaluation of twelve oral hyperkeratosis (HK), eleven oral epithelial dysplasia (OED), and eleven oral squamous cell carcinoma (OSCC) biopsy samples in the fingerprint region of 1800-950 cm. A series of 100 µm × 100 µm FTIR imaging areas were defined in each sample section in reference to the hematoxylin and eosin staining image of an adjacent section of the same sample. After outlier removal, signal preprocessing, and cluster analysis, a representative spectrum was generated for only the epithelial tissue in each area. Two representative spectra were selected from each sample to reflect intra-sample heterogeneity, which resulted in a total of 68 representative spectra from 34 samples for further analysis. Exploratory analyses using Principal component analysis and hierarchical cluster analysis showed good separation between the HK and OSCC spectra and overlaps of OED spectra with either HK or OSCC spectra. Three machine learning discriminant models based on partial least squares discriminant analysis (PLSDA), support vector machines discriminant analysis (SVMDA), and extreme gradient boosting discriminant analysis (XGBDA) were trained using 46 representative spectra from 12 HK and 11 OSCC samples. The PLSDA model achieved 100% sensitivity and 100% specificity, while both SVM and XGBDA models generated 95% sensitivity and 96% specificity, respectively. The PLSDA discriminant model was further used to classify the 11 OED samples into HK-grade (6), OSCC-grade (4), or borderline case (1) based on their FTIR spectral similarity to either HK or OSCC cases, providing a potential risk stratification strategy for the precancerous OED samples. The results of the current study support the application of the FTIR-machine learning technique in early oral cancer detection.
傅里叶变换红外（FTIR）成像技术用于透射模型，以评估1800 - 950 cm指纹区域的12个口腔过度角化（HK）、11个口腔上皮发育异常（OED）和11个口腔鳞状细胞癌（OSCC）活检样本。参照同一样本相邻切片的苏木精和伊红染色图像，在每个样本切片中定义一系列100 µm×100 µm的FTIR成像区域。在去除异常值、进行信号预处理和聚类分析后，仅为每个区域的上皮组织生成代表性光谱。从每个样本中选择两个代表性光谱以反映样本内的异质性，从而从34个样本中总共获得68个代表性光谱用于进一步分析。使用主成分分析和层次聚类分析的探索性分析表明，HK光谱和OSCC光谱之间有良好的区分，而OED光谱与HK或OSCC光谱存在重叠。基于偏最小二乘判别分析（PLSDA）、支持向量机判别分析（SVMDA）和极端梯度提升判别分析（XGBDA）的三种机器学习判别模型，使用来自12个HK样本和11个OSCC样本的46个代表性光谱进行训练。PLSDA模型实现了100%的灵敏度和100%的特异性，而SVM和XGBDA模型分别产生了95%的灵敏度和96%的特异性。PLSDA判别模型进一步用于根据11个OED样本与HK或OSCC病例的FTIR光谱相似性，将其分类为HK级（6个）、OSCC级（4个）或临界病例（1个），为癌前OED样本提供了一种潜在的风险分层策略。当前研究结果支持FTIR机器学习技术在早期口腔癌检测中的应用。
