Abstract The gold standard for diagnosis of oesophageal adenocarcinoma still requires histological examination of mucosal biopsies obtained at oesophagogastroduodenoscopy. Herein, Raman spectral analysis of ex vivo oesophageal tissue was performed, including all classifications to oesophageal adenocarcinoma. This study adds further validation to previous human oesophageal tissue studies identifying the potential for Raman spectroscopy in differentiating between all classes in oesophageal transformation to oesophageal adenocarcinoma [normal; inflammatory; Barrett’s oesophagus; low-grade dysplasia; high-grade dysplasia; and, oesophageal adenocarcinoma]. Tissue spectra were analysed using principal component analysis-quadratic discriminant analysis (PCA-QDA), successive projections algorithm-quadratic discriminant analysis or genetic algorithm-quadratic discriminant analysis. The variables selected were then used for discriminating the tissue classes based on the unique spectral "fingerprints" of their biochemical composition. Accuracy tests including sensitivity and specificity were determined. The best model using PCA-QDA successfully classified normal, inflammatory, Barrett’s oesophagus, low-grade dysplasia, high-grade dysplasia and oesophageal adenocarcinoma tissues with 91%–100% accuracy, 90%-100% sensitivity, and 71%–100% specificity. Amongst the main distinguishing features in the pre-processed Raman spectra, the wavenumbers at 1296 cm-1 (CH2 deformation), 1448 cm-1 (CH2/CH3 deformation) and 1668 cm−1 (Amide I) were found to increase in amplitude with increasing differentiation. Raman spectroscopy exhibits promise in distinguishing the stages leading to oesophageal adenocarcinoma and may play a role in surveillance programs in premalignant conditions such as Barrett’s oesophagus.

中文翻译：

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使用拉曼显微光谱鉴别人体组织样本中食管转化阶段为腺癌

摘要 食管腺癌诊断的金标准仍然需要对食管胃十二指肠镜下获得的粘膜活检进行组织学检查。在此，对离体食管组织进行了拉曼光谱分析，包括对食管腺癌的所有分类。这项研究进一步验证了之前的人类食管组织研究，确定了拉曼光谱在区分食管转化为食管腺癌的所有类别方面的潜力 [正常；炎症; 巴雷特食管；低度发育不良; 高度不典型增生；和，食管腺癌]。使用主成分分析-二次判别分析 (PCA-QDA) 分析组织光谱，连续投影算法-二次判别分析或遗传算法-二次判别分析。然后将选择的变量用于根据其生化成分的独特光谱“指纹”来区分组织类别。确定了包括敏感性和特异性在内的准确度测试。使用 PCA-QDA 的最佳模型以 91%–100% 的准确度、90%-100% 的灵敏度和 71%–100% 的准确率成功地将正常、炎症、Barrett 食管、低度不典型增生、高度不典型增生和食管腺癌组织分类特异性。在预处理拉曼光谱的主要区别特征中，发现 1296 cm-1（CH2 变形）、1448 cm-1（CH2/CH3 变形）和 1668 cm-1（酰胺 I）处的波数振幅增加随着分化的加剧。