激光生物学报, 2005, 14 (6): 432, 网络出版: 2006-06-12
PLS-ANN判别分析自体荧光光谱识别胃癌
PLS-ANN Discriminant Analysis on Autofluorescence Spectra to Identify Gastric Cancer
胃肿瘤 自体荧光光谱 偏最小二乘法 人工神经网络 stomach neoplasmas autofluorescence spectroscopy partial-least squares artificial neural networks
摘要
本文对58例胃癌病人离体标本的癌浆膜和正常浆膜进行以308 nm为激发光的自体荧光光谱检测,采用多因素分析法进行光谱信息提取,以识别胃癌.研究表明偏最小二乘法结合神经网络法(简称PLS-ANN)进行判别分析,诊断胃癌的灵敏度为86%,特异度为100%,准确率为93%,有望成为手术中快速识别胃癌在胃壁的浸润范围的有效方法.
Abstract
Measurement of fluorescence spectra was performed at excitation wavelength of 308 nm and emission wavelength in the range of 328 nm~596 nm. The partial Least-squares and artificial neural network (PLS-ANN) method was used to analyze autofluorescence spectra of gastric cancer. The 58 cancer samples and normal samples were taken from stomach serosa. The normalized and centerized spectra of two kinds of samples showed similar but divergent patterns. PLS-ANN classification algorithm could differentiate cancer t...
马君, 史晓凤, 郑荣儿, 朱玉平, 李颖, 毛伟征, 孟继武. PLS-ANN判别分析自体荧光光谱识别胃癌[J]. 激光生物学报, 2005, 14(6): 432. 马君, 史晓凤, 郑荣儿, 朱玉平, 李颖, 毛伟征, 孟继武. PLS-ANN Discriminant Analysis on Autofluorescence Spectra to Identify Gastric Cancer[J]. Acta Laser Biology Sinica, 2005, 14(6): 432.
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