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Title: In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques
Authors: Duraipandian, S.
Zheng, W. 
Ng, J.
Low, J.J.H.
Ilancheran, A.
Huang, Z. 
Issue Date: 21-Oct-2011
Citation: Duraipandian, S., Zheng, W., Ng, J., Low, J.J.H., Ilancheran, A., Huang, Z. (2011-10-21). In vivo diagnosis of cervical precancer using Raman spectroscopy and genetic algorithm techniques. Analyst 136 (20) : 4328-4336. ScholarBank@NUS Repository.
Abstract: This study aimed to evaluate the clinical utility of applying near-infrared (NIR) Raman spectroscopy and genetic algorithm-partial least squares-discriminant analysis (GA-PLS-DA) to identify biomolecular changes of cervical tissues associated with dysplastic transformation during colposcopic examination. A total of 105 in vivo Raman spectra were measured from 57 cervical sites (35 normal and 22 precancer sites) of 29 patients recruited, in which 65 spectra were from normal sites, while 40 spectra were from cervical precancerous lesions (i.e., 7 low-grade CIN and 33 high-grade CIN). The GA feature selection technique incorporated with PLS was utilized to study the significant biochemical Raman bands for differentiation between normal and precancer cervical tissues. The GA-PLS-DA algorithm with double cross-validation (dCV) identified seven diagnostically significant Raman bands in the ranges of 925-935, 979-999, 1080-1090, 1240-1260, 1320-1340, 1400-1420, and 1625-1645 cm -1 related to proteins, nucleic acids and lipids in tissue, and yielded a diagnostic accuracy of 82.9% (sensitivity of 72.5% (29/40) and specificity of 89.2% (58/65)) for precancer detection. The results of this exploratory study suggest that Raman spectroscopy in conjunction with GA-PLS-DA and dCV methods has the potential to provide clinically significant discrimination between normal and precancer cervical tissues at the molecular level. © 2011 The Royal Society of Chemistry.
Source Title: Analyst
ISSN: 00032654
DOI: 10.1039/c1an15296c
Appears in Collections:Staff Publications

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