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.

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.

We present a three-dimensional (3-D) computational method to detect soft tissue sarcomas with the goal of automatic surgical margin assessment based on optical coherence tomography (OCT) images. Three parameters are investigated and quantified from OCT images as the indicators for the tissue diagnosis including the signal attenuation (A-line slope), the standard deviation of the signal fluctuations (speckles), and the exponential decay coefficient of its spatial frequency spectrum. The detection of soft tissue sarcomas relies on the combination of these three parameters, which are related to the optical attenuation characteristics and the structural features of the tissue. Pilot experiments were performed on ex vivo human tissue samples with homogeneous pieces (both normal and abnormal) and tumor margins. Our results demonstrate the feasibility of this computational method in the differentiation of soft tissue sarcomas from normal tissues. The features of A-line-based detection and 3-D quantitative analysis yield promise for a computer-aided technique capable of accurately and automatically identifying resection margins of soft tissue sarcomas during surgical treatment.

Optical coherence tomography (OCT) is employed in the diagnosis of skin cancer. Particularly, quantitative image features extracted from OCT images might be used as indicators to classify the skin tumors. In the present paper, we investigated intensity-based, texture-based and fractal-based features for automatically classifying the melanomas, basal cell carcinomas and pigment nevi. Generalized estimating equations were used to test for differences between the skin tumors. A modified p value of $<$$$0.001 was considered statistically significant. Significant increase of mean and median of intensity and significant decrease of mean and median of absolute gradient were observed in basal cell carcinomas and pigment nevi as compared with melanomas. Significant decrease of contrast, entropy and fractal dimension was also observed in basal cell carcinomas and pigment nevi as compared with melanomas. Our results suggest that the selected quantitative image features of OCT images could provide useful information to differentiate basal cell carcinomas and pigment nevi from the melanomas. Further research is warranted to determine how this approach may be used to improve the classification of skin tumors.