Cancer staging detection is important for clinician to assess the patients' status and make optimal therapy decision. In this study, the machine learning algorithm based on principal component analysis(PCA) and su...Cancer staging detection is important for clinician to assess the patients' status and make optimal therapy decision. In this study, the machine learning algorithm based on principal component analysis(PCA) and support vector machine(SVM) was combined with urine surface-enhanced Raman scattering(SERS) spectroscopy for improving the identification of colorectal cancer(CRC) at early and advanced stages. Two discriminant methods, linear discriminant analysis(LDA) and SVM were compared, and the results indicated that the diagnostic accuracy of SVM(93.65%) was superior to that of LDA(80.95%). This exploratory study demonstrated the great promise of urine SERS spectra along with PCA-SVM for facilitating more accurate detection of CRC at different stages.展开更多
A surface-enhanced Raman spectroscopy (SERS) method combined with multivariate analysis was developed for non-invasive gastric cancer detection. SERS measurements were performed on two groups of blood plasma samples: ...A surface-enhanced Raman spectroscopy (SERS) method combined with multivariate analysis was developed for non-invasive gastric cancer detection. SERS measurements were performed on two groups of blood plasma samples: one group from 32 gastric patients and the other group from 33 healthy volunteers. Tentative assignments of the Raman bands in the measured SERS spectra suggest interesting cancer-specific biomolecular changes, including an increase in the relative amounts of nucleic acid, collagen, phospholipids and phenylalanine and a decrease in the percentage of amino acids and saccharide in the blood plasma of gastric cancer patients as compared with those of healthy subjects. Principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and cancer plasma with high sensitivity (79.5%) and specificity (91%). A receiver operating characteristic (ROC) curve was employed to assess the accuracy of diagnostic algorithms based on PCA-LDA. The results from this exploratory study demonstrate that SERS plasma analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of gastric cancers.展开更多
基金supported by the National Natural Science Foundation of China (No.61975031)the Natural Science Foundation of Fujian Province (No.2020J011121)+3 种基金the Product-University Cooperation Project of Fujian Province (No.2020Y4006)the National Clinical Key Specialty Construction Program (No.2021)the Fujian Provincial Clinical Research Center for Cancer Radiotherapy and Immunotherapy (No.2020Y2012)the Joint Funds for the Innovation of Science and Technology of Fujian Province (No.2021Y9192)。
文摘Cancer staging detection is important for clinician to assess the patients' status and make optimal therapy decision. In this study, the machine learning algorithm based on principal component analysis(PCA) and support vector machine(SVM) was combined with urine surface-enhanced Raman scattering(SERS) spectroscopy for improving the identification of colorectal cancer(CRC) at early and advanced stages. Two discriminant methods, linear discriminant analysis(LDA) and SVM were compared, and the results indicated that the diagnostic accuracy of SVM(93.65%) was superior to that of LDA(80.95%). This exploratory study demonstrated the great promise of urine SERS spectra along with PCA-SVM for facilitating more accurate detection of CRC at different stages.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60778046 and 60910106016)the Project of Fujian Province (Grant Nos. 2009J01276 and 2008I0015)
文摘A surface-enhanced Raman spectroscopy (SERS) method combined with multivariate analysis was developed for non-invasive gastric cancer detection. SERS measurements were performed on two groups of blood plasma samples: one group from 32 gastric patients and the other group from 33 healthy volunteers. Tentative assignments of the Raman bands in the measured SERS spectra suggest interesting cancer-specific biomolecular changes, including an increase in the relative amounts of nucleic acid, collagen, phospholipids and phenylalanine and a decrease in the percentage of amino acids and saccharide in the blood plasma of gastric cancer patients as compared with those of healthy subjects. Principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and cancer plasma with high sensitivity (79.5%) and specificity (91%). A receiver operating characteristic (ROC) curve was employed to assess the accuracy of diagnostic algorithms based on PCA-LDA. The results from this exploratory study demonstrate that SERS plasma analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of gastric cancers.
