Isabelle M, Dorney J, Lewis A, Lloyd G R, Old O, Shepherd N, Rodriguez-Justo M, Barr H, Lau K, Bell I, Ohrel S, Thomas G, Stone N, Kendall C
Biophotonics Research Unit and Pathology Department, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK.
Biomedical Spectroscopy, School of Physics, University of Exeter, UK.
Faraday Discuss. 2016 Jun 23;187:87-103. doi: 10.1039/c5fd00183h.
The potential for Raman spectroscopy to provide early and improved diagnosis on a wide range of tissue and biopsy samples in situ is well documented. The standard histopathology diagnostic methods of reviewing H&E and/or immunohistochemical (IHC) stained tissue sections provides valuable clinical information, but requires both logistics (review, analysis and interpretation by an expert) and costly processing and reagents. Vibrational spectroscopy offers a complimentary diagnostic tool providing specific and multiplexed information relating to molecular structure and composition, but is not yet used to a significant extent in a clinical setting. One of the challenges for clinical implementation is that each Raman spectrometer system will have different characteristics and therefore spectra are not readily compatible between systems. This is essential for clinical implementation where classification models are used to compare measured biochemical or tissue spectra against a library training dataset. In this study, we demonstrate the development and validation of a classification model to discriminate between adenocarcinoma (AC) and non-cancerous intraepithelial metaplasia (IM) oesophageal tissue samples, measured on three different Raman instruments across three different locations. Spectra were corrected using system transfer spectral correction algorithms including wavenumber shift (offset) correction, instrument response correction and baseline removal. The results from this study indicate that the combined correction methods do minimize the instrument and sample quality variations within and between the instrument sites. However, more tissue samples of varying pathology states and greater tissue area coverage (per sample) are needed to properly assess the ability of Raman spectroscopy and system transferability algorithms over multiple instrument sites.
拉曼光谱能够对多种组织和活检样本进行原位早期诊断并提高诊断效果，这一点已有充分记录。通过查看苏木精-伊红（H&E）和/或免疫组织化学（IHC）染色组织切片的标准组织病理学诊断方法可提供有价值的临床信息，但需要后勤保障（由专家进行审查、分析和解读）以及昂贵的处理过程和试剂。振动光谱提供了一种补充性诊断工具，可提供与分子结构和组成相关的特定和多重信息，但在临床环境中尚未得到广泛应用。临床应用面临的挑战之一是，每个拉曼光谱仪系统都有不同的特性，因此不同系统之间的光谱不容易兼容。这对于临床应用至关重要，因为在临床应用中，分类模型用于将测量的生化或组织光谱与库训练数据集进行比较。在本研究中，我们展示了一种分类模型的开发和验证，该模型用于区分腺癌（AC）和非癌性上皮内化生（IM）食管组织样本，这些样本是在三个不同地点的三种不同拉曼仪器上测量的。使用包括波数偏移（偏移）校正、仪器响应校正和基线去除在内的系统转移光谱校正算法对光谱进行校正。本研究结果表明，组合校正方法确实能最大限度地减少仪器内部和仪器站点之间的仪器和样本质量差异。然而，需要更多不同病理状态的组织样本以及更大的组织面积覆盖（每个样本），才能正确评估拉曼光谱和系统可转移性算法在多个仪器站点上的能力。
