Real-time Raman spectroscopy for in vivo skin cancer diagnosis

Using high frequency Raman spectra for colonic neoplasia detection

Raman systems have tremendous potential as adjunct devices for endoscopes to improve the identification of early colon cancers. However, the traditional low frequency (LF) measurement range has several obstacles that make it challenging to develop a routine clinical tool. An alternative is to use high frequency (HF) range. To test this idea Raman spectra were obtained in both the LF and HF ranges from the same colon lesions. Multivariate analyses predicted the pathology with high sensitivity and specificity for both the LF and HF data sets. This suggests that Raman systems that measure HF spectra, and are simpler to adopt into the clinic, could be used in vivo to improve the identification of neoplastic lesions.

New diagnostics for melanoma detection: from artificial intelligence to RNA microarrays

Early detection of melanoma remains crucial to ensuring a favorable prognosis. Dermoscopy and total body photography are well-established noninvasive aids that increase the diagnostic accuracy of dermatologists in their daily routine, beyond that of a naked-eye examination. New noninvasive diagnostic techniques, such as reflectance confocal microscopy, multispectral digital imaging and RNA microarrays, are currently being investigated to determine their utility for melanoma detection. This review presents emerging technologies for noninvasive melanoma diagnosis, and discusses their advantages and limitations.

Real-time molecular imaging throughout the entire cell cycle by targeted plasmonic-enhanced Rayleigh/Raman spectroscopy

Due to their strong enhancement of scattered light, plasmonic nanoparticles have been utilized for various biological and medical applications. Here, we describe a new technique, Targeted Plasmonic-Enhanced Single-Cell Rayleigh/Raman Spectroscopy, to monitor the molecular changes of any cell-component, such as the nucleus, during the different phases of its full cell cycle by simultaneously recording its Rayleigh images and Raman vibration spectra in real-time. The analysis of the observed Raman DNA and protein peaks allowed the different phases of the cell cycle to be identified. This technique could be used for disease diagnostics and potentially improve our understanding of the molecular mechanisms of cellular functions such as division, death, signaling, and drug action.

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

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.