基于近红外光谱和支持向量机的子宫内膜癌早期诊断研究

近红外光谱结合化学计量学方法对癌症的辅助诊断已有了文献报道.该文测定了77例不同生理阶段的子官内膜组织病理切片的近红外光谱,对其分别进行多元散射校正(MSC)、正交信号校正(OSC)以及二者联用的预处理方法,采用拉丁配分法选择3/4样本作为训练集,1/4样本作测试集,建立支持向量机(SVM)模型进行分类,并与基于同样预...

Early stage diagnosis of endometrial cancer based on near infrared spectroscopy and support vector machine

Near-infrared spectroscopy combined with chemometrics methods for diagnosis of cancer has been reported in literatures. In our study, the NIR spectra of 77 specimens of different physiological stages of endometrium were collected. Spectral data were pretreated firstly by multiplicative scatter correction (MSC), orthogonal signal correction (OSC), and both of them, respectively, and then by SG smoothing. Latin partition method was used to select 3/4 samples as a training set, and the other 1/4 samples for test set. Support vector machine (SVM) model was built for classification, and the classification results was compared with that of partial least squares (PLS) model based on the same pretreatment methods. Samples of malignant, hyperplasia and normal endometrium were classified better by SVM (classification accuracy was 92%) than PLS (classification accuracy was 90%). The results suggested that classification accuracy was affected by pretreatment methods and models. SVM combined with endometrial tissue near infrared spectroscopy is expected to develop into a new approach to tumor diagnosis.