Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer
Fourier-transform-infrared-spectroscopy based spectral-biomarker selection towards optimum diagnostic differentiation of oral leukoplakia and cancer
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DOI：
10.1007/s00216-015-8960-3
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发表时间：
2015-10-01
影响因子：
4.3
通讯作者：
Chatterjee, Jyotirmoy
中科院分区：
文献类型：
--
作者：
Banerjee, Satarupa;Pal, Mousumi;Chatterjee, Jyotirmoy
为了寻找用于区分两种口腔病变（即口腔白斑（OLK）和口腔鳞状细胞癌（OSCC））的特异性无标记生物标志物，对47名人类受试者（8名正常（NOM），16名OLK和23名OSCC）的石蜡包埋组织切片进行傅里叶变换红外（FTIR）光谱分析。平均光谱之间的差异（DBMS），曼-惠特尼的U检验，和前向特征选择（FFS）技术用于优化光谱标记选择。疾病的分类进行了线性和二次支持向量机（SVM）在10倍交叉验证，使用不同的组合的光谱特征。观察到通过FFS获得的六个特征能够区分NOM和OSCC组织（1782、1713、1665、1545、1409和1161 cm（-1）），并且是最重要的，能够以81.3%的灵敏度、95.7%的特异性和89.7%的总体准确性对OLK和OSCC进行分类。当使用二次SVM时，通过Mann-Whitney U检验提取的43个光谱标记是最不显著的。考虑到FFS技术的高灵敏度和特异性，因此仅提取六种光谱生物标志物对于OLK和OSCC的诊断最有用，并且克服了诊断最佳实践组织病理学程序中经历的观察者间和观察者内的变异性。通过考虑这六个光谱特征的生化分配，这项工作还揭示了组织切片中糖原和角蛋白含量的改变，这可以区分OLK和OSCC。该方法通过DBMS技术进行光谱选择来验证。因此，该方法具有通过无标记生物标志物鉴定来最小化口腔病变的诊断成本的潜力。
In search of specific label-free biomarkers for differentiation of two oral lesions, namely oral leukoplakia (OLK) and oral squamous-cell carcinoma (OSCC), Fourier-transform infrared (FTIR) spectroscopy was performed on paraffin-embedded tissue sections from 47 human subjects (eight normal (NOM), 16 OLK, and 23 OSCC). Difference between mean spectra (DBMS), Mann-Whitney's U test, and forward feature selection (FFS) techniques were used for optimising spectral-marker selection. Classification of diseases was performed with linear and quadratic support vector machine (SVM) at 10-fold cross-validation, using different combinations of spectral features. It was observed that six features obtained through FFS enabled differentiation of NOM and OSCC tissue (1782, 1713, 1665, 1545, 1409, and 1161 cm(-1)) and were most significant, able to classify OLK and OSCC with 81.3 % sensitivity, 95.7 % specificity, and 89.7 % overall accuracy. The 43 spectral markers extracted through Mann-Whitney's U Test were the least significant when quadratic SVM was used. Considering the high sensitivity and specificity of the FFS technique, extracting only six spectral biomarkers was thus most useful for diagnosis of OLK and OSCC, and to overcome inter and intra-observer variability experienced in diagnostic best-practice histopathological procedure. By considering the biochemical assignment of these six spectral signatures, this work also revealed altered glycogen and keratin content in histological sections which could able to discriminate OLK and OSCC. The method was validated through spectral selection by the DBMS technique. Thus this method has potential for diagnostic cost minimisation for oral lesions by label-free biomarker identification.