Bird Benjamin, Miljkovic Milos, Romeo Melissa J, Smith Jennifer, Stone Nicholas, George Michael W, Diem Max
Department of Chemistry and Chemical Biology, Northeastern University, Boston, USA.
BMC Clin Pathol. 2008 Aug 29;8:8. doi: 10.1186/1472-6890-8-8.
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 mum x 25 mum 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.
手术标本的组织病理学评估是一种成熟的疾病诊断技术，自临床应用以来相对未变。尽管它对临床研究至关重要，但组织的组织病理学鉴定仍然是一项耗时且主观的技术，观察者间和观察者内的差异水平不尽人意。一种新的组织学识别方法是使用傅里叶变换红外（FT - IR）显微光谱技术。这种无损光学技术能够快速测量样本的生物化学特征，并识别健康组织和病变组织之间的差异。该方法的优点是客观，能提供可重复的诊断，不受疲劳、经验和观察者间差异的影响。
我们报告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，一束红外光对未染色（固定或速冻）的组织切片进行检测，该光束对大小为25微米×25微米的像素进行采样。此光束在样本上进行光栅扫描，为给定的组织样本采集多达100,000个完整的红外光谱。随后，这些光谱由一个诊断计算机算法进行分析，该算法通过将光谱特征与组织病理学特征相关联进行训练。
我们展示了红外显微光谱成像结合完全无监督的多元统计分析方法准确再现腋窝淋巴结组织学结构的能力。通过将光谱特征与组织病理学特征相关联，训练出一种诊断算法，该算法能够准确且快速地对不同淋巴结内的良性和恶性组织进行分类。这种方法成功应用于脱石蜡组织和冷冻组织，表明该技术可用于术中及更传统的手术标本诊断。
本文提供了强有力的证据，证明通过红外显微光谱成像进行自动诊断是可行的。作者实验室近期对淋巴结的研究还表明，来自不同原发肿瘤的癌症具有明显不同的光谱特征。因此，这种技术还可额外识别低分化和难以确定的转移侵袭病例，如微转移。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。
