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In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components. [ABSTRACT FROM AUTHOR]

In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences. [ABSTRACT FROM AUTHOR]

The result of the analysis is the classification of the obtained Raman spectrum and the decision on the probable belonging of the neoplasm to a specific group of skin neoplasms (b4). Today more than half of skin cancer cases are found at advanced stages, which is a direct threat to the patient's life.[1] Potentially dangerous pigmented skin neoplasms may be identified during visual examination in more than 90% of the population. Raman-based optical biopsy shortens the clinical pathway of the patient: Example of pigmented skin neoplasm diagnosis. [Extracted from the article]

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low‐cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near‐infrared region (800–915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10‐fold cross‐validation was performed. We obtained ROC AUCs of 0.75 (0.71–0.79; 95% CI), 0.69 (0.63–0.76; 95% CI) and 0.81 (0.74–0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy‐ and spectroscopy‐based computer‐assisted diagnosis systems to increase accuracy of neoplasms classification. [ABSTRACT FROM AUTHOR]

The pathological state of a human body leads to altered biochemical composition of body fluids. Conventional biochemical analysis of body fluids is notable for its low‐informative value in localizing a particular pathology. As an alternative, Raman spectroscopy provides detailed evaluation of blood characteristics at the molecular level. Raman blood spectra are characterized by multicollinearity as well as by the presence of autofluorescence background and noises of different nature. Choice of a proper method for experimental data processing of blood spectra is crucial for obtaining statistically reliable information regarding a pathological process in the body. This study examines different approaches to multidimensional analysis of the various‐size Raman spectral dataset of human blood samples by a cost‐effective Raman setup in a clinical setting. To discriminate blood samples by the pathology type, statistical processing of experimental data is performed by factor analysis, logistic regression, discriminant analysis, classification tree, projection to latent structures discriminant analysis (PLS‐DA), and soft independent modeling of class analogies. Comparative analysis of the discussed multivariate methods demonstrates that the PLS‐DA method (sensitivity 0.75, specificity 0.81, and accuracy 0.76) proved to be the most effective for the classification of blood samples by cancer localization. In terms of classification for the presence of hyperproteinemia, the most efficient are the logistic regression method (sensitivity 0.89, specificity 0.99, and accuracy 0.97) and the discriminant analysis method (sensitivity 0.83, specificity 1.0, and accuracy 0.97). In general, the selected multivariate methods could serve as a reliable tool for analyzing spectral characteristics of body fluids. [ABSTRACT FROM AUTHOR]

The differentiation of skin melanomas and basal cell carcinomas (BCCs) was demonstrated based on combined analysis of Raman and autofluorescence spectra stimulated by visible and NIR lasers. It was ex vivo tested on 39 melanomas and 40 BCCs. Six spectroscopic criteria utilizing information about alteration of melanin, porphyrins, flavins, lipids, and collagen content in tumor with a comparison to healthy skin were proposed. The measured correlation between the proposed criteria makes it possible to define weakly correlated criteria groups for discriminant analysis and principal components analysis application. It was shown that the accuracy of cancerous tissues classification reaches 97.3% for a combined 6-criteria multimodal algorithm, while the accuracy determined separately for each modality does not exceed 79%. The combined 6-D method is a rapid and reliable tool for malignant skin detection and classification. [ABSTRACT FROM AUTHOR]

Malignant skin tumors of different types were studied in vivo using optical coherence tomography (OCT), backscattering (BS), and Raman spectroscopy (RS). A multimodal method is proposed for early cancer detection based on complex analysis of OCT images by their relative alteration of scattered-radiation spectral intensities between malignant and healthy tissues. An increase in average accuracy of diagnosis was observed for a variety of cancer types (9% sensitivity, 8% specificity) by a multimodal RS-BS-OCT system in comparison with any of the three methods used separately. The proposed approach equalizes the processing rates for all methods and allows for simultaneous imaging and classification of tumors. [ABSTRACT FROM AUTHOR]

The present paper studies the applicability of a portable cost‐effective spectroscopic system for the optical screening of skin tumors. in vivo studies of Raman scattering and autofluorescence (AF) of skin tumors with the 785 nm excitation laser in the near‐infrared region included malignant melanoma, basal cell carcinoma and various types of benign neoplasms. The efficiency of the portable system was evaluated by comparison with a highly sensitive spectroscopic system and with the diagnosis accuracy of a human oncologist. Partial least square analysis of Raman and AF spectra was performed; specificity and sensitivity of various skin oncological pathologies detection varied from 78.9% to 100%. Hundred percent accuracy of benign and malignant skin tumors differentiation is possible only with a combined analysis of Raman and AF signals. Optical biopsy for different skin neoplasm classification was performed by means of the portable cost‐effective Raman system. Biochemical and structural changes of tissues were analyzed using registered Raman and autofluorescence spectral features. Application of the proposed setup and method of optical biopsy has a great potential in clinical applications and mass screening surveys as it demonstrates 100% accuracy in malignant and benign tumors differentiation. [ABSTRACT FROM AUTHOR]