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.

No abstract

The object of this paper is in vivo study of skin spectral‐characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS‐DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS‐DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm−1. In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost‐effective and accurate detection of kidney failure and associated metabolic changes in the skin.

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.

No abstract

In this work, Raman and autofluorescence spectroscopy in the near-infrared region has been used for examining amelanotic melanoma as the most dangerous type of malignant melanoma. There were 9 patients with amelanotic melanoma, 60 with pigmented melanoma and 120 with basal cell carcinoma enrolled in this study. We studied 9 amelanotic melanoma cases to differentiate them from basal cell carcinoma (n = 120) and pigmented malignant melanoma (n = 60) using portable spectroscopy setup with laser excitation source at 785 nm and low-cost spectrometer. The spectra of the different tumor type were classified using projection on the latent structure analysis with 10-Fold cross-validation. The results of the tumor classification were presented using box-plot diagrams and ROC analysis. We obtained 0.53 and 0.88 ROC AUCs for distinguishing amelanotic melanoma versus (1) pigmented melanoma and (2) basal cell carcinoma respectively based on the joint autofluorescence and Raman spectroscopy analysis that allowed one to diagnose amelanotic melanoma as true melanoma but no basal cell carcinoma.

No abstract

No abstract