School of Instrumentation Science and Opto-Electronics Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Haidian District, Beijing, China.
Biomed Res Int. 2013;2013:942427. doi: 10.1155/2013/942427. Epub 2013 Aug 13.
Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.
早期诊断和早期治疗是拯救患者生命和提高生活质量的关键。傅里叶变换红外（FT-IR）光谱技术可以在分子水平上区分恶性组织和正常组织。在本文中，我们使用模式识别方法编写了程序来对未知样本进行分类。通过平滑和标准正态变量（SNV）方法对光谱数据进行预处理。使用留一法交叉验证来评估支持向量机（SVM）方法的判别结果。本研究共使用了 54 个胃组织样本，包括 24 例正常组织样本和 30 例癌组织样本。SVM 方法的判别结果显示，其灵敏度为 100%，特异性为 83.3%，总判别准确率为 92.2%。
