Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou 350007, China.
Photodiagnosis Photodyn Ther. 2013 May;10(2):111-9. doi: 10.1016/j.pdpdt.2012.07.003. Epub 2012 Aug 21.
The aim of this study was to explore the usefulness of multiple excitation autofluorescence (AF) and a spectral feature-based pattern recognition in classification of colon tissues.
Under four different excitation wavelengths (337, 375, 405 and 460 nm), AF spectra of freshly excised normal and adenocarcinoma colon tissues were measured. Pattern recognition method including features extraction, data reduction using principal component analysis (PCA) and Fisher's discriminant analysis (FDA) were performed for classification.
There was a significantly difference between spectral patterns of normal and adenocarcinoma tissues. Compared with the other three excitation wavelengths, the AF spectra obtained under 337 nm excitation provided more diagnostic information, but also more sensitive to the trivial change resulted from neoplastic transformation. For discriminating normal from adenocarcinoma tissues, the sensitivity, specificity and accuracy using 337 nm excitation in the present study were 88.9%, 80.0% and 83.9%, respectively. Compared these values with those determined from multispectral data analysis, our findings indicate that the latter has higher specificity while maintaining the same sensitivity (sensitivity 88.9% vs. 88.9%, specificity 91.4% vs. 80.0%, and accuracy 90.3% vs. 83.9%).
This study suggests that the pattern recognition of the multiple excitation AF spectra is an effective algorithm for improving the diagnostic accuracy of adenocarcinoma.
本研究旨在探讨多激发自体荧光（AF）和基于光谱特征的模式识别在结肠组织分类中的应用价值。
在四种不同的激发波长（337、375、405 和 460nm）下，测量新鲜切除的正常和腺癌结肠组织的 AF 光谱。采用模式识别方法，包括特征提取、主成分分析（PCA）和 Fisher 判别分析（FDA）的数据降维，进行分类。
正常和腺癌组织的光谱模式存在显著差异。与其他三种激发波长相比，337nm 激发下的 AF 光谱提供了更多的诊断信息，但对肿瘤转化引起的微小变化也更为敏感。用于区分正常组织和腺癌组织，本研究中 337nm 激发的灵敏度、特异性和准确性分别为 88.9%、80.0%和 83.9%。与多光谱数据分析确定的值相比，我们的研究结果表明，后者具有更高的特异性，同时保持相同的灵敏度（灵敏度 88.9%对 88.9%，特异性 91.4%对 80.0%，准确性 90.3%对 83.9%）。
本研究表明，多激发 AF 光谱的模式识别是提高腺癌诊断准确性的有效算法。
