Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology.
Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology.
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红外显微光谱成像：腋窝淋巴结组织学中组织类型的区分。
DOI：
10.1186/1472-6890-8-8
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发表时间：
2008-08-29
影响因子：
--
通讯作者：
Diem, Max
中科院分区：
文献类型：
--
作者：
Bird, Benjamin;Miljkovic, Milos;Romeo, Melissa J;Smith, Jennifer;Stone, Nicholas;George, Michael W;Diem, Max
手术标本的组织学评价是一种成熟的疾病识别技术，自临床引入以来一直保持相对不变。虽然它是必不可少的临床研究，组织的组织病理学鉴定仍然是一个耗时和主观的技术，与观察者之间和内部的差异不令人满意的水平。傅里叶变换红外（FT-IR）显微光谱是一种新的组织学识别方法。这种非破坏性的光学技术可以提供样品生物化学的快速测量，并识别健康和患病组织之间发生的变化。这种方法的优点是，它是客观的，并提供可重复的诊断，独立于疲劳，经验和观察者之间的变异性。我们报告了一种基于光谱病理学分析切除淋巴结的方法。在光谱病理学中，未染色（固定或快速冷冻）的组织切片通过一束红外光进行询问，该红外光对大小为25 μm × 25 μm的像素进行采样。该光束在样品上光栅化，并且对于给定的组织样品获取多达100，000个完整的红外光谱。这些光谱随后通过诊断计算机算法进行分析，该算法通过将光谱和组织病理学特征相关联来训练。我们说明了红外显微光谱成像的能力，再加上完全无监督的多元统计分析方法，准确地再现腋窝淋巴结的组织结构。通过将光谱和组织病理学特征相关联，训练了一种诊断算法，该算法允许对不同淋巴结内组成的良性和恶性组织进行准确和快速的分类。这种方法成功地应用于脱蜡和冷冻组织，并表明术中和更传统的手术标本可以通过这种技术进行诊断。本文提供了强有力的证据，自动诊断的红外显微光谱成像是可能的。作者实验室最近对淋巴结的研究也表明，来自不同原发肿瘤的癌症提供了明显不同的光谱特征。因此，低分化和难以确定的转移性浸润病例，如微转移，也可以通过这种技术进行鉴定。最后，我们通过完全自动化的光谱分析方法区分腋窝淋巴结内的良性和恶性组织。
Histopathologic evaluation of surgical specimens is a well established technique for disease identification, and has remained relatively unchanged since its clinical introduction. Although it is essential for clinical investigation, histopathologic identification of tissues remains a time consuming and subjective technique, with unsatisfactory levels of inter- and intra-observer discrepancy. A novel approach for histological recognition is to use Fourier Transform Infrared (FT-IR) micro-spectroscopy. This non-destructive optical technique can provide a rapid measurement of sample biochemistry and identify variations that occur between healthy and diseased tissues. The advantage of this method is that it is objective and provides reproducible diagnosis, independent of fatigue, experience and inter-observer variability. We report a method for analysing excised lymph nodes that is based on spectral pathology. In spectral pathology, an unstained (fixed or snap frozen) tissue section is interrogated by a beam of infrared light that samples pixels of 25 μm × 25 μm in size. This beam is rastered over the sample, and up to 100,000 complete infrared spectra are acquired for a given tissue sample. These spectra are subsequently analysed by a diagnostic computer algorithm that is trained by correlating spectral and histopathological features. We illustrate the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes. By correlating spectral and histopathological features, a diagnostic algorithm was trained that allowed both accurate and rapid classification of benign and malignant tissues composed within different lymph nodes. This approach was successfully applied to both deparaffinised and frozen tissues and indicates that both intra-operative and more conventional surgical specimens can be diagnosed by this technique. This paper provides strong evidence that automated diagnosis by means of infrared micro-spectral imaging is possible. Recent investigations within the author's laboratory upon lymph nodes have also revealed that cancers from different primary tumours provide distinctly different spectral signatures. Thus poorly differentiated and hard-to-determine cases of metastatic invasion, such as micrometastases, may additionally be identified by this technique. Finally, we differentiate benign and malignant tissues composed within axillary lymph nodes by completely automated methods of spectral analysis.