title: Real-time Raman spectroscopy for in vivo, online gastric cancer diagnosis during clinical endoscopic examination.
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, 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.
authors.affiliation: National University of Singapore, Department of Bioengineering, Faculty of Engineering, Optical Bioimaging Laboratory, Singapore 117576, Singapore.
authors.forename: Shiyamala
authors.initials: S
authors.lastname: Duraipandian
authors.forename: Mads
authors.initials: M
authors.lastname: Sylvest Bergholt
authors.forename: Wei
authors.initials: W
authors.lastname: Zheng
authors.forename: Khek
authors.initials: K
authors.lastname: Yu Ho
authors.forename: Ming
authors.initials: M
authors.lastname: Teh
authors.forename: Khay
authors.initials: K
authors.lastname: Guan Yeoh
authors.forename: Jimmy
authors.initials: J
authors.lastname: Bok Yan So
authors.forename: Asim
authors.initials: A
authors.lastname: Shabbir
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Huang
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D003199:Computer Systems; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation* / Q000379:methods*; D016099:Endoscopy, Gastrointestinal / Q000379:methods*; D006801:Humans; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013059:Spectrum Analysis, Raman / Q000295:instrumentation* / Q000379:methods*; D013274:Stomach Neoplasms / Q000175:diagnosis* / Q000378:metabolism
chemical_list: D014408:Biomarkers, Tumor