Department of Biophysics and Protein Research Unit Europe (PURE), Ruhr University Bochum, Bochum, Germany.
J Biophotonics. 2013 Jan;6(1):88-100. doi: 10.1002/jbio.201200132. Epub 2012 Dec 7.
During the past years, many studies have shown that infrared spectral histopathology (SHP) can distinguish different tissue types and disease types independently of morphological criteria. In this manuscript, we report a comparison of immunohistochemical (IHC), histopathological and spectral histopathological results for colon cancer tissue sections. A supervised algorithm, based on the "random forest" methodology, was trained using classical histopathology, and used to automatically identify colon tissue types, and areas of colon adenocarcinoma. The SHP images subsequently were compared to IHC-based images. This comparison revealed excellent agreement between the methods, and demonstrated that label-free SHP detects compositional changes in tissue that are the basis of the sensitivity of IHC.
在过去的几年中，许多研究表明，红外光谱组织病理学（SHP）可以独立于形态学标准区分不同的组织类型和疾病类型。在本手稿中，我们报告了对结肠癌组织切片的免疫组织化学（IHC）、组织病理学和光谱组织病理学结果的比较。一种基于“随机森林”方法的有监督算法使用经典组织病理学进行了训练，并用于自动识别结肠组织类型和结肠腺癌区域。随后将 SHP 图像与基于 IHC 的图像进行比较。这种比较显示出两种方法之间极好的一致性，并表明无标记 SHP 检测到组织成分变化，这是 IHC 敏感性的基础。
