Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with diseased transformation. The purpose of this study was to explore near-infrared (NIR) Raman spectroscopy for identifying dysplasia from normal gastric mucosa tissue. A rapid-acquisition dispersive-type NIR Raman system was utilised for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 76 gastric tissue samples obtained from 44 patients who underwent endoscopy investigation or gastrectomy operation were used in this study. The histopathological examinations showed that 55 tissue specimens were normal and 21 were dysplasia. Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out crossvalidation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues. High-quality Raman spectra in the range of 800 -1800 cm À1 can be acquired from gastric tissue within 5 s. There are specific spectral differences in Raman spectra between normal and dysplasia tissue, particularly in the spectral ranges of 1200 -1500 cm À1 and 1600 -1800 cm À1 , which contained signals related to amide III and amide I of proteins, CH 3 CH 2 twisting of proteins/nucleic acids, and the C ¼ C stretching mode of phospholipids, respectively. The empirical diagnostic algorithm based on the ratio of the Raman peak intensity at 875 cm À1 to the peak intensity at 1450 cm À1 gave the diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the diagnostic algorithms based on PCA-LDA yielded the diagnostic sensitivity of 95.2% and specificity 90.9% for separating dysplasia from normal gastric tissue. Receiver operating characteristic (ROC) curves further confirmed that the most effective diagnostic algorithm can be derived from the PCA-LDA technique. Therefore, NIR Raman spectroscopy in conjunction with multivariate statistical technique has potential for rapid diagnosis of dysplasia in the stomach based on the optical evaluation of spectral features of biomolecules.

We report an integrated Raman spectroscopy and trimodal (white-light reflectance, autofluorescence, and narrow-band) imaging techniques for real-time in vivo tissue Raman measurements at endoscopy. A special 1.8 mm endoscopic Raman probe with filtering modules is developed, permitting effective elimination of interference of fluorescence background and silica Raman in fibers while maximizing tissue Raman collections. We demonstrate that high-quality in vivo Raman spectra of upper gastrointestinal tract can be acquired within 1 s or subseconds under the guidance of wide-field endoscopic imaging modalities, greatly facilitating the adoption of Raman spectroscopy into clinical research and practice during routine endoscopic inspections.

The purpose of this study is to apply near-infrared (NIR) Raman spectroscopy and classification and regression tree (CART) techniques for identifying molecular changes of tissue associated with cancer transformation. A rapid-acquisition NIR Raman system is utilized for tissue Raman spectroscopic measurements at 785-nm excitation. 73 gastric tissue samples (55 normal, 18 cancer) from 53 patients are measured. The CART technique is introduced to develop effective diagnostic algorithms for classification of Raman spectra of different gastric tissues. 80% of the Raman dataset are randomly selected for spectral learning, while 20% of the dataset are reserved for validation. High-quality Raman spectra in the range of 800 to 1800 cm(-1) are acquired from gastric tissue within 5 s. The diagnostic sensitivity and specificity of the learning dataset are 90.2 and 95.7%; and the predictive sensitivity and specificity of the independent validation dataset are 88.9 and 92.9%, respectively, for separating cancer from normal. The tissue Raman peaks at 875 and 1745 cm(-1) are found to be two of the most significant features to discriminate gastric cancer from normal tissue. NIR Raman spectroscopy in conjunction with the CART technique has the potential to provide an effective and accurate diagnostic means for cancer detection in the gastric system.

No abstract

In this work, we evaluated the diagnostic ability of near-infrared (NIR) Raman spectroscopy associated with the ensemble recursive partitioning algorithm based on random forests for identifying cancer from normal tissue in the larynx. A rapid-acquisition NIR Raman system was utilized for tissue Raman measurements at 785 nm excitation, and 50 human laryngeal tissue specimens (20 normal; 30 malignant tumors) were used for NIR Raman studies. The random forests method was introduced to develop effective diagnostic algorithms for classification of Raman spectra of different laryngeal tissues. High-quality Raman spectra in the range of 800-1800 cm(-1) can be acquired from laryngeal tissue within 5 seconds. Raman spectra differed significantly between normal and malignant laryngeal tissues. Classification results obtained from the random forests algorithm on tissue Raman spectra yielded a diagnostic sensitivity of 88.0% and specificity of 91.4% for laryngeal malignancy identification. The random forests technique also provided variables importance that facilitates correlation of significant Raman spectral features with cancer transformation. This study shows that NIR Raman spectroscopy in conjunction with random forests algorithm has a great potential for the rapid diagnosis and detection of malignant tumors in the larynx.