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1 May 2008 Diagnosis of gastric cancer using near-infrared Raman spectroscopy and classification and regression tree techniques
Seng Khoon Teh, Wei Zheng, Khek Yu Ho, Ming Teh, Khay Guan Yeoh, Zhiwei Huang
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Journal of Biomedical Optics, Vol. 13, Issue 3, 034013 (May 2008). https://doi.org/10.1117/1.2939406
Abstract
The purpose of this study is to apply near-infrared (NIR) Raman spectroscopy and classification and regression tree (CART) techniques for identifying molecular changes of tissue associated with cancer transformation. A rapid-acquisition NIR Raman system is utilized for tissue Raman spectroscopic measurements at 785-nm excitation. 73 gastric tissue samples (55 normal, 18 cancer) from 53 patients are measured. The CART technique is introduced to develop effective diagnostic algorithms for classification of Raman spectra of different gastric tissues. 80% of the Raman dataset are randomly selected for spectral learning, while 20% of the dataset are reserved for validation. High-quality Raman spectra in the range of 800 to 1800 cm-1 are acquired from gastric tissue within 5 s. The diagnostic sensitivity and specificity of the learning dataset are 90.2 and 95.7%; and the predictive sensitivity and specificity of the independent validation dataset are 88.9 and 92.9%, respectively, for separating cancer from normal. The tissue Raman peaks at 875 and 1745 cm-1 are found to be two of the most significant features to discriminate gastric cancer from normal tissue. NIR Raman spectroscopy in conjunction with the CART technique has the potential to provide an effective and accurate diagnostic means for cancer detection in the gastric system.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
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Seng Khoon Teh, Wei Zheng, Khek Yu Ho, Ming Teh, Khay Guan Yeoh, and Zhiwei Huang "Diagnosis of gastric cancer using near-infrared Raman spectroscopy and classification and regression tree techniques," Journal of Biomedical Optics 13(3), 034013 (1 May 2008). https://doi.org/10.1117/1.2939406
Published: 1 May 2008
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KEYWORDS
Raman spectroscopy
Tissues
Cancer
Diagnostics
Data modeling
Tissue optics
Algorithm development
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