There is an unmet need for methods to help in the early detection of cervical precancer. Optical spectroscopy-based techniques, such as Raman spectroscopy, have shown great potential for diagnosis of different cancers, including cervical cancer. However, relatively few studies have been carried out on liquid-based cytology (LBC) pap test specimens and confounding factors, such as blood contamination, have been identified. Previous work reported a method to remove blood contamination before Raman spectroscopy by pretreatment of the slides with hydrogen peroxide. The aim of the present study was to extend this work to excessively bloody samples to see if these could be rendered suitable for Raman spectroscopy. LBC ThinPrep specimens were treated by adding hydrogen peroxide directly to the vial before slide preparation. Good quality Raman spectra were recorded from negative and high grade (HG) cytology samples with no blood contamination and with heavy blood contamination. Good classification between negative and HG cytology could be achieved for samples with no blood contamination (sensitivity 92%, specificity 93%) and heavy blood contamination (sensitivity 89%, specificity 88%) with poorer classification when samples were combined (sensitivity 82%, specificity 87%). This study demonstrates for the first time the improved potential of Raman spectroscopy for analysis of ThinPrep specimens regardless of blood contamination.
Molecular profiling of tissue using near-infrared (NIR) Raman spectroscopy has shown great promise for in vivo
detection and prognostication of cancer. The Raman spectra measured from the tissue generally contain fundamental
information about the absolute biomolecular concentrations in tissue and its changes associated with disease
transformation. However, producing analogues tissue Raman spectra present a great technical challenge. In this
preliminary study, we propose a method to ensure the reproducible tissue Raman measurements and validated with the in
vivo Raman spectra (n=150) of inner lip acquired using different laser powers (i.e., 30 and 60 mW). A rapid Raman
spectroscopy system coupled with a ball-lens fiber-optic Raman probe was utilized for tissue Raman measurements. The
investigational results showed that the variations between the spectra measured with different laser powers are almost
negligible, facilitating the quantitative analysis of tissue Raman measurements in vivo.
Raman spectroscopy is a unique optical technique that can probe the changes of vibrational modes of biomolecules associated with tissue premalignant transformation. This study evaluates the clinical utility of confocal Raman spectroscopy over near-infrared (NIR) autofluorescence (AF) spectroscopy and composite NIR AF/Raman spectroscopy for improving early diagnosis of cervical precancer in vivo at colposcopy. A rapid NIR Raman system coupled with a ball-lens fiber-optic confocal Raman probe was utilized for in vivo NIR AF/Raman spectral measurements of the cervix. A total of 1240 in vivo Raman spectra [normal (n=993 ), dysplasia (n=247 )] were acquired from 84 cervical patients. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with a leave-one-patient-out, cross-validation method were used to extract the diagnostic information associated with distinctive spectroscopic modalities. The diagnostic ability of confocal Raman spectroscopy was evaluated using the PCA-LDA model developed from the significant principal components (PCs) [i.e., PC4, 0.0023%; PC5, 0.00095%; PC8, 0.00022%, (p<0.05 )], representing the primary tissue Raman features (e.g., 854, 937, 1095, 1253, 1311, 1445, and 1654 cm −1 ). Confocal Raman spectroscopy coupled with PCA-LDA modeling yielded the diagnostic accuracy of 84.1% (a sensitivity of 81.0% and a specificity of 87.1%) for in vivo discrimination of dysplastic cervix. The receiver operating characteristic curves further confirmed that the best classification was achieved using confocal Raman spectroscopy compared to the composite NIR AF/Raman spectroscopy or NIR AF spectroscopy alone. This study illustrates that confocal Raman spectroscopy has great potential to improve early diagnosis of cervical precancer in vivo during clinical colposcopy.
Raman spectroscopy is a vibrational spectroscopic technique capable of optically probing the compositional,
conformational, and structural changes in the tissue associated with disease progression. The main goal of this work is to
develop an integrated fingerprint (FP) and high wavenumber (HW) in vivo confocal Raman spectroscopy for
simultaneous FP/HW tissue Raman spectral measurements. This work further explores the potential of integrated FP/HW
Raman spectroscopy developed as a diagnostic tool for in vivo detection of cervical precancer. A total of 473 in vivo
integrated FP/HW Raman spectra (340 normal and 133 precancer) were acquired from 35 patients within 1 s during
clinical colposcopy. The major tissue Raman peaks are noticed around 854, 937, 1001, 1095, 1253, 1313, 1445, 1654,
2946 and 3400 cm-1, related to the molecular changes (e.g., proteins, lipids, glycogen, nucleic acids, water, etc.) that
accompany the dysplastic transformation of tissue. The FP (800 - 1800 cm-1), HW (2800 - 3800 cm-1) and the integrated
FP/HW Raman spectra were analyzed using partial least squares-discriminant analysis (PLS-DA) together with the
leave-one patient-out, cross-validation. The developed PLS-DA classification models and receiver operating
characteristics (ROC) curves for the FP, HW and integrated FP/HW spectroscopy further discloses that the performance
of integrated FP/HW Raman spectroscopy is superior to that of all others in discriminating the dysplastic cervix. The
results of this work indicate that the co-contributions of underlying rich biochemical information revealed by the
complementary spectral modalities (FP and HW Raman) can improve the in vivo early diagnosis of cervical precancer at
clinical colposcopy
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.
KEYWORDS: Raman spectroscopy, Tissues, Cervix, Spectroscopy, Diagnostics, In vivo imaging, Principal component analysis, Molecular spectroscopy, Cancer, Near infrared
Raman spectroscopy is a unique analytical probe for molecular vibration and is capable of providing specific spectroscopic fingerprints of molecular compositions and structures of biological tissues. The aim of this study is to improve the classification accuracy of cervical precancer by characterizing the variations in the normal high wavenumber (HW - 2800-3700cm-1) Raman spectra arising from the menopausal status of the cervix. A rapidacquisition near-infrared (NIR) Raman spectroscopic system was used for in vivo tissue Raman measurements at 785 nm excitation. Individual HW Raman spectrum was measured with a 5s exposure time from both normal and precancer tissue sites of 15 patients recruited. The acquired Raman spectra were stratified based on the menopausal status of the cervix before the data analysis. Significant differences were noticed in Raman intensities of prominent band at 2924 cm-1 (CH3 stretching of proteins) and the broad water Raman band (in the 3100-3700 cm-1 range) with a peak at 3390 cm-1 in normal and dysplasia cervical tissue sites. Multivariate diagnostic decision algorithm based on principal component analysis (PCA) and linear discriminant analysis (LDA) was utilized to successfully differentiate the normal and precancer cervical tissue sites. By considering the variations in the Raman spectra of normal cervix due to the hormonal or menopausal status of women, the diagnostic accuracy was improved from 71 to 91%. By incorporating these variations prior to tissue classification, we can significantly improve the accuracy of cervical precancer detection using HW Raman spectroscopy.
Visualization of cells and subcellular organelles are currently carried out using available microscopy methods such as
cryoelectron microscopy, and fluorescence microscopy. These methods require external labeling using fluorescent dyes
and extensive sample preparations to access the subcellular structures. However, Raman micro-spectroscopy provides a
non-invasive, label-free method for imaging the cells with chemical specificity at sub-micrometer spatial resolutions.
The scope of this paper is to image the biochemical/molecular distributions in cells associated with cancerous changes.
Raman map data sets were acquired from the human cervical carcinoma cell lines (HeLa) after fixation under 785 nm
excitation wavelength. The individual spectrum was recorded by raster-scanning the laser beam over the sample with
1μm step size and 10s exposure time. Images revealing nucleic acids, lipids and proteins (phenylalanine, amide I) were
reconstructed using univariate methods. In near future, the small pixel to pixel variations will also be imaged using
different multivariate methods (PCA, clustering (HCA, K-means, FCM)) to determine the main cellular constitutions.
The hyper-spectral image of cell was reconstructed utilizing the spectral contrast at different pixels of the cell (due to the
variation in the biochemical distribution) without using fluorescent dyes. Normal cervical squamous cells will also be
imaged in order to differentiate normal and cancer cells of cervix using the biochemical changes in different grades of
cancer. Based on the information obtained from the pseudo-color maps, constructed from the hyper-spectral cubes, the
primary cellular constituents of normal and cervical cancer cells were identified.
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