Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines

Abstract

Abstract. The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785-nm laser excitation. A total of 817 tissue Raman spectra were acquired and subjected to principal components analysis (PCA) for SVM-based multi-class classification, in which 324 Raman spectra were from normal, 184 from polyps and 309 from adenocarcinomatous colonic tissue. Two types of SVM (i.e., C-SVM and ν-SVM) with three different kernel functions (linear, polynomial and Gaussian radial basis function (RBF) in combination with PCA were used to develop effective diagnostic algorithms for classification of Raman spectra of different colonic tissues. The performance of various SVMbased algorithms was evaluated and compared using a leave-one-out, cross-validation method. The results showed that in the C-SVM classification, the maximum overall diagnostic accuracy of 99.3, 99.4 and 99.9% can be achieved using the linear, polynomial and RBF kernels, respectively; while in the ν-SVM classification, the maximum overall diagnostic accuracy of 98.4, 98.5 and 99.6% can be obtained using the linear, polynomial and RBF kernels, respectively. All the polyps can be identified from normal and adenocarcinomatous tissue using the C-SVM algorithms. The RBF C-SVM algorithm was proven to be the best classifier for providing the highest diagnostic accuracy (99.9%) for multi-class classification. This study demonstrates that NIR Raman spectroscopy in combination with a powerful SVM technique has great potential for providing an effective and accurate diagnostic schema for cancer diagnosis in the colon.

Surface enhanced Raman spectroscopy (SERS): Potential applications for disease detection and treatment

“…As a diagnostic tool, Raman spectroscopy is forging ahead in the cancer field providing a method by which to detect breast [25][26][27], skin [28,29], prostate [30][31][32], bladder [33][34][35], cervical [36][37][38], colorectal [39,40] and oesophageal cancer [41,42]. With bladder, [34,35] cervical, [37,38] colorectal [43] and oesophageal cancer [42] all being detected in vivo.…”

An unmet medical need: Advances in endoscopic imaging of colorectal neoplasia

“…In gastrointestinal oncology, Raman has been investigated as a diagnostic tool for characterizing cancer cells and early malignant changes. Raman spectroscopy has been used on single cells [39] and tissue [40,41], and has been applied in vivo towards better diagnostics for dysplastic diseases in humans. Very recently, Short and coworkers presented at the Digestive Disease Week 2011 in Chicago an endoscopic Raman spectroscopy system for improving the detection of early colonic neoplasias.…”

Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM)

“…For this purpose, different types of multivariate statistical algorithm such as principal component analysis (PCA) [18], linear discriminate analysis (LDA) [19] and support vector machine (SVM) [20,21] have been used for classification between and normal and malignant cells/tissues. This article, demonstrate the use of Raman spectroscopy and SVM for classification of dengue infected and normal healthy sera.…”