Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination

• Published in: Journal of biomedical optics 2012-08, Vol.17 (8), p.081418-1-081418-8
• Main Authors: Duraipandian, Shiyamala, Sylvest Bergholt, Mads, Zheng, Wei, Yu Ho, Khek, Teh, Ming, Guan Yeoh, Khay, Bok Yan So, Jimmy, Shabbir, Asim, Huang, Zhiwei
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Biocompatibility; Biomarkers, Tumor - analysis; Biomedical materials; Cancer; Computer Systems; Diagnosis; Diagnosis, Computer-Assisted - instrumentation; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Endoscopy, Gastrointestinal - methods; Humans; In vivo testing; In vivo tests; Online Systems; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Spectrum Analysis, Raman - instrumentation; Spectrum Analysis, Raman - methods; Stomach Neoplasms - diagnosis; Stomach Neoplasms - metabolism; Surgical implants
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.17.8.081418

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, data-analysis 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. Free-running 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.