Please use this identifier to cite or link to this item: https://doi.org/10.1117/1.JBO.17.8.081418
Title: Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination
Authors: Duraipandian, S.
Bergholt, M.S. 
Zheng, W. 
Ho, K.Y.
Teh, M.
Yeoh, K.G.
So, J.B.Y.
Shabbir, A.
Huang, Z. 
Keywords: Cancer diagnostics
In vivo optical diagnosis
Multivariate analysis
Raman spectroscopy
Issue Date: Aug-2012
Citation: Duraipandian, S., Bergholt, M.S., Zheng, W., Ho, K.Y., Teh, M., Yeoh, K.G., So, J.B.Y., Shabbir, A., Huang, Z. (2012-08). Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination. Journal of Biomedical Optics 17 (8) : -. ScholarBank@NUS Repository. https://doi.org/10.1117/1.JBO.17.8.081418
Abstract: Optical spectroscopic techniques including reflectance, fluorescence and Raman spectroscopy have shown promising potential for in vivo precancer and cancer diagnostics in a variety of organs. However, dataanalysis has mostly been limited to post-processing and off-line algorithm development. In this work, we develop a fully automated on-line Raman spectral diagnostics framework integrated with a multimodal image-guided Raman technique for real-time in vivo cancer detection at endoscopy. A total of 2748 in vivo gastric tissue spectra (2465 normal and 283 cancer) were acquired from 305 patients recruited to construct a spectral database for diagnostic algorithms development. The novel diagnostic scheme developed implements on-line preprocessing, outlier detection based on principal component analysis statistics (i.e., Hotelling's T2 and Q-residuals) for tissue Raman spectra verification as well as for organ specific probabilistic diagnostics using different diagnostic algorithms. Freerunning optical diagnosis and processing time of < 0.5 s can be achieved, which is critical to realizing real-time in vivo tissue diagnostics during clinical endoscopic examination. The optimized partial least squares-discriminant analysis (PLS-DA) models based on the randomly resampled training database (80% for learning and 20% for testing) provide the diagnostic accuracy of 85.6% [95% confidence interval (CI): 82.9% to 88.2%] [sensitivity of 80.5% (95% CI: 71.4% to 89.6%) and specificity of 86.2% (95% CI: 83.6% to 88.7%)] for the detection of gastric cancer. The PLS-DA algorithms are further applied prospectively on 10 gastric patients at gastroscopy, achieving the predictive accuracy of 80.0% (60/75) [sensitivity of 90.0% (27/30) and specificity of 73.3% (33/45)] for in vivo diagnosis of gastric cancer. The receiver operating characteristics curves further confirmed the efficacy of Raman endoscopy together with PLS-DA algorithms for in vivo prospective diagnosis of gastric cancer. This work successfully moves biomedical Raman spectroscopic technique into real-time, on-line clinical cancer diagnosis, especially in routine endoscopic diagnostic applications. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE).
Source Title: Journal of Biomedical Optics
URI: http://scholarbank.nus.edu.sg/handle/10635/88086
ISSN: 10833668
DOI: 10.1117/1.JBO.17.8.081418
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