In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques

Authors

  • Mads Sylvest Bergholt,

    1. Optical Bioimaging Laboratory, Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore
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  • Wei Zheng,

    1. Optical Bioimaging Laboratory, Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore
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  • Kan Lin,

    1. Optical Bioimaging Laboratory, Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore
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  • Khek Yu Ho,

    1. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Hospital, Singapore
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  • Ming Teh,

    1. Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore and National University Hospital, Singapore
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  • Khay Guan Yeoh,

    1. Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Hospital, Singapore
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  • Jimmy Bok Yan So,

    1. Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Hospital, Singapore
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  • Zhiwei Huang

    Corresponding author
    1. Optical Bioimaging Laboratory, Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore
    • Optical Bioimaging Laboratory, Department of Bioengineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
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    • Tel.: +65-6516-8856, Fax: +65-6872-3069


Abstract

This study aims to evaluate the clinical utility of image-guided Raman endoscopy for in vivo diagnosis of neoplastic lesions in the stomach at gastroscopy. A rapid-acquisition image-guided Raman endoscopy system with 785-nm excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 sec during clinical gastroscopic examinations. A total of 1,063 in vivo Raman spectra were acquired from 238 tissue sites of 67 gastric patients, in which 934 Raman spectra were from normal tissue whereas 129 Raman spectra were from neoplastic gastric tissue. The swarm intelligence-based algorithm (i.e., ant colony optimization (ACO) integrated with linear discriminant analysis (LDA)) was developed for spectral variables selection to identify the biochemical important Raman bands for differentiation between normal and neoplastic gastric tissue. The ACO-LDA algorithms together with the leave-one tissue site-out, cross validation method identified seven diagnostically important Raman bands in the regions of 850–875, 1,090–1,110, 1,120–1,130, 1,170–1,190, 1,320–1,340, 1,655–1,665 and 1,730–1,745 cm−1 related to proteins, nucleic acids and lipids of tissue and provided a diagnostic sensitivity of 94.6% and specificity of 94.6% for distinction of gastric neoplasia. The predictive sensitivity of 89.3% and specificity of 97.8% were also achieved for an independent test validation dataset (20% of total dataset). This work demonstrates for the first time that the real-time image-guided Raman endoscopy associated with ACO-LDA diagnostic algorithms has potential for the noninvasive, in vivo diagnosis and detection of gastric neoplasia during clinical gastroscopy.

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