Samara National Research University, Department of Laser and Biotechnical Systems, Samara, Russia.
Samara State Medical University, Department of Oncology, Samara, Russia.
J Biomed Opt. 2017 Feb 1;22(2):27005. doi: 10.1117/1.JBO.22.2.027005.
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.
基于可见和近红外激光激发的拉曼和自发荧光光谱的联合分析，证明了皮肤黑素瘤和基底细胞癌 (BCC) 的分化。在 39 个黑素瘤和 40 个 BCC 上进行了离体测试。提出了六个利用肿瘤中黑色素、卟啉、黄素、脂质和胶原蛋白含量变化的信息与健康皮肤进行比较的光谱标准。所测量的标准之间的相关性使得可以为判别分析和主成分分析应用定义弱相关的标准组。结果表明，对于组合的 6 标准多模态算法，癌组织分类的准确性达到 97.3%，而对于每种模态单独确定的准确性不超过 79%。联合的 6-D 方法是一种快速可靠的恶性皮肤检测和分类工具。
