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

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 nu-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 SVM-based 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 nu-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.

[1]  Brian C Wilson,et al.  Diagnostic potential of near-infrared Raman spectroscopy in the colon: differentiating adenomatous from hyperplastic polyps. , 2003, Gastrointestinal endoscopy.

[2]  Christopher J. Frank,et al.  Raman spectroscopy of normal and diseased human breast tissues. , 1995, Analytical chemistry.

[3]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[4]  Wei Zheng,et al.  Near-infrared Raman spectroscopy for colonic cancer diagnosis , 2005, European Conference on Biomedical Optics.

[5]  Alan G. Ryder,et al.  Quantitative analysis of cocaine in solid mixtures using Raman spectroscopy and chemometric methods , 2000 .

[6]  D I McLean,et al.  Rapid near-infrared Raman spectroscopy system for real-time in vivo skin measurements. , 2001, Optics letters.

[7]  Haishan Zeng,et al.  Raman spectroscopy of in vivo cutaneous melanin. , 2004, Journal of biomedical optics.

[8]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  J Popp,et al.  Identification of single eukaryotic cells with micro-Raman spectroscopy. , 2006, Biopolymers.

[10]  Jitendra Malik,et al.  Learning to Detect Natural Image Boundaries Using Brightness and Texture , 2002, NIPS.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  G. Puppels,et al.  Combined in vivo confocal Raman spectroscopy and confocal microscopy of human skin. , 2003, Biophysical journal.

[13]  M. R. Mickey,et al.  Estimation of Error Rates in Discriminant Analysis , 1968 .

[14]  Yukihiro Ozaki,et al.  Near‐infrared Fourier transform Raman spectroscopic study of human brain tissues and tumours , 1994 .

[15]  Jürgen Popp,et al.  On-line monitoring and identification of bioaerosols. , 2006, Analytical chemistry.

[16]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[17]  H. Wulf,et al.  Distinctive Molecular Abnormalities in Benign and Malignant Skin Lesions: Studies by Raman Spectroscopy , 1997, Photochemistry and photobiology.

[18]  S. Lam,et al.  Near‐infrared Raman spectroscopy for optical diagnosis of lung cancer , 2003, International journal of cancer.

[19]  H. Lui,et al.  Raman spectroscopy for optical diagnosis in normal and cancerous tissue of the nasopharynx—preliminary findings , 2003, Lasers in surgery and medicine.

[20]  H. Barr,et al.  Raman Spectroscopy for Early Detection of Laryngeal Malignancy: Preliminary Results , 2000, The Laryngoscope.

[21]  Yoshua Bengio,et al.  Inference for the Generalization Error , 1999, Machine Learning.

[22]  Haishan Zeng,et al.  Raman Spectroscopy in Combination with Background Near-infrared Autofluorescence Enhances the In Vivo Assessment of Malignant Tissues , 2005, Photochemistry and photobiology.

[23]  Brian C. Wilson,et al.  In vivo Near-infrared Raman Spectroscopy: Demonstration of Feasibility During Clinical Gastrointestinal Endoscopy¶ , 2000, Photochemistry and photobiology.

[24]  Yoshua Bengio,et al.  Support vector machines for improving the classification of brain PET images , 1998, Medical Imaging.

[25]  H. Lui,et al.  Raman spectroscopy for optical diagnosis in the larynx: Preliminary findings , 2005, Lasers in surgery and medicine.

[26]  A. Mahadevan-Jansen,et al.  Near‐Infrared Raman Spectroscopy for In Vitro Detection of Cervical Precancers , 1998 .

[27]  S. Majumder,et al.  Support vector machine for optical diagnosis of cancer. , 2005, Journal of biomedical optics.

[28]  J. Roodenburg,et al.  In vivo detection of dysplastic tissue by Raman spectroscopy. , 2000, Analytical chemistry.

[29]  Nirmala Ramanujam,et al.  Comparison of multiexcitation fluorescence and diffuse reflectance spectroscopy for the diagnosis of breast cancer (March 2003) , 2003, IEEE Transactions on Biomedical Engineering.

[30]  Wei Zheng,et al.  Classification of ENT tissue using near-infrared Raman spectroscopy and support vector machines , 2005, European Conference on Biomedical Optics.

[31]  R. Richards-Kortum,et al.  Raman spectroscopy for the detection of cancers and precancers. , 1996, Journal of biomedical optics.

[32]  Richard G. Brereton,et al.  Support vector machines for the discrimination of analytical chemical data: application to the determination of tablet production by pyrolysis-gas chromatography-mass spectrometry , 2004 .

[33]  Xin Yuan,et al.  Classification of in vivo autofluorescence spectra using support vector machines. , 2004, Journal of biomedical optics.

[34]  Chih-Jen Lin,et al.  Training ν-Support Vector Classifiers: Theory and Algorithms , 2001 .

[35]  J. Disario,et al.  Colorectal cancer: screening and surveillance for high-risk individuals , 2003, Expert review of anticancer therapy.

[36]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[37]  Bernhard Scholkopf,et al.  Support Vector Machines: A Practical Consequence of Learning Theory , 1998 .