Using the Method of "Optical Biopsy" of Prostatic Tissue to Diagnose Prostate Cancer.

The possibilities of using optical spectroscopy methods in the differential diagnosis of prostate cancer were investigated. Analytical discrimination models of Raman spectra of prostate tissue were constructed by using the projections onto latent structures data analysis(PLS-DA) method for different wavelengths of exciting radiation-532 and 785 nm. These models allowed us to divide the Raman spectra of prostate cancer and the spectra of hyperplasia sites for validation datasets with the accuracy of 70-80%, depending on the specificity value. Meanwhile, for the calibration datasets, the accuracy values reached 100% for the excitation of a laser with a wavelength of 785 nm. Due to the registration of Raman "fingerprints", the main features of cellular metabolism occurring in the tissue of a malignant prostate tumor were confirmed, namely the absence of aerobic glycolysis, over-expression of markers (FASN, SREBP1, stearoyl-CoA desaturase, etc.), and a strong increase in the concentration of cholesterol and its esters, as well as fatty acids and glutamic acid. The presence of an ensemble of Raman peaks with increased intensity, inherent in fatty acid, beta-glucose, glutamic acid, and cholesterol, is a fundamental factor for the identification of prostate cancer.

In vivo diagnosis of skin cancer with a portable Raman spectroscopic device.

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.

Raman spectroscopy of human skin for kidney failure detection.

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.

Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools: comment.

This paper comments on the article "Use of Raman spectroscopy to screen diabetes mellitus with machine learning tools" by E. Guevara et al. The authors propose an optical method for noninvasive automated screening of type 2 diabetes mellitus. Despite the high performance of the proposed method, results shown by the authors may be ambiguous due to the overestimation of classification models for Raman spectral data analysis.

Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis.

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.

Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions.

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.

Comparative analysis of combined spectral and optical tomography methods for detection of skin and lung cancers.

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.