Discrimination of premalignant lesions and cancer tissues from normal gastric tissues using Raman spectroscopy
Autor: | Shaoqin Jin, Changshui Chen, Hua Mao, Shuwen Luo |
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Rok vydání: | 2013 |
Předmět: |
Male
Pathology medicine.medical_specialty Biomedical Engineering Early detection Spectrum Analysis Raman Biomaterials symbols.namesake Adenomatous Polyps Molecular level Stomach Neoplasms Medicine Humans Diagnosis Computer-Assisted Aged Principal Component Analysis business.industry Naive bayesian classifier Stomach Cancer Discriminant Analysis Diagnostic algorithms Bayes Theorem Middle Aged medicine.disease digestive system diseases Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Multivariate Analysis symbols Adenocarcinoma Feasibility Studies Female business Raman spectroscopy Precancerous Conditions Ex vivo Algorithms |
Zdroj: | Journal of biomedical optics. 18(6) |
ISSN: | 1560-2281 |
Popis: | The feasibility of early detection of gastric cancer using near-infrared (NIR) Raman spectroscopy (RS) by distinguishing premalignant lesions (adenomatous polyp, n=27) and cancer tissues (adenocarcinoma, n=33) from normal gastric tissues (n=45) is evaluated. Significant differences in Raman spectra are observed among the normal, adenomatous polyp, and adenocarcinoma gastric tissues at 936, 1003, 1032, 1174, 1208, 1323, 1335, 1450, and 1655 cm(-1). Diverse statistical methods are employed to develop effective diagnostic algorithms for classifying the Raman spectra of different types of ex vivo gastric tissues, including principal component analysis (PCA), linear discriminant analysis (LDA), and naive Bayesian classifier (NBC) techniques. Compared with PCA-LDA algorithms, PCA-NBC techniques together with leave-one-out, cross-validation method provide better discriminative results of normal, adenomatous polyp, and adenocarcinoma gastric tissues, resulting in superior sensitivities of 96.3%, 96.9%, and 96.9%, and specificities of 93%, 100%, and 95.2%, respectively. Therefore, NIR RS associated with multivariate statistical algorithms has the potential for early diagnosis of gastric premalignant lesions and cancer tissues in molecular level. |
Databáze: | OpenAIRE |
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