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.

The Outline of Prognosis and New Advances in Diagnosis of Oral Squamous Cell Carcinoma (OSCC): Review of the Literature

“…The difference in energy between the original state and this new state leads to a shift in the emitted photon's frequency away from the excitation wavelength [54][55][56]. The reported sensitivity of this technique was of 80.5% and specificity of 86.2% [59].…”

Raman spectroscopy: techniques and applications in the life sciences

“…PLS can also be trained to predict non-chemical properties of the sample. For example, Maher et al used PLS and Raman spectroscopy to measure bone strength [502] and Duraipandian et al estimated the probability of future development of cancer [503].…”

Fluorescence lifetime spectroscopy of tissue autofluorescence in normal and diseased colon measured ex vivo using a fiber-optic probe

“…These include point probe-based diffuse reflectance spectroscopy [12][13][14], which has been applied to in vivo detection of dysplasia in Barrett's esophagus (BE) [15] and to ex vivo surgical specimens of colon cancer [16], and Raman spectroscopy [17], which has recently been applied in a large study of 305 patients [18]. In this work we chose to focus on autofluorescence (AF) spectroscopy and lifetime measurements as they can be extended from point-based measurements to wide-field imaging once the spectroscopic features presenting the greatest contrast between normal and diseased tissue have been identified.…”