Evaluation of human melanoma and normal formalin paraffin-fixed samples using Raman and LIBS fused data

Abstract

In this research, we developed a novel method of quantitative analysis to increase the detection potential for screening and classification of skin cancer (melanoma). We fused two distinct optical approaches, an atomic spectroscopic detection technique laser-induced breakdown spectroscopy (LIBS) and a vibrational molecular spectroscopic technique known as Raman spectroscopy. Melanoma is a kind of skin cancer, also known as malignant melanoma, that developed in melanocytes cells, which produced melanin. Classification of melanoma cancerous tissues is a fundamental problem in biomedicine. For early melanoma cancer diagnosis and treatment, precise and accurate categorizing is critically essential. Laser-based spectroscopic approaches can be used as an operating instrument for simultaneous tissue ablation and ablated tissue elemental and molecular analysis. For this purpose, melanoma and normal paraffin-embedded tissues are used as a sample for LIBS and Raman measurement. We studied the data provided by laser-based spectroscopic methods using different machine learning classification techniques of extreme learning machine (ELM), partial least square discriminant analysis (PLS-DA), and K nearest neighbors (kNN). For visualization of melanoma and normal data, principal component analysis (PCA) is also used. Three different ways are used to process the data, LIBS measurement, Raman measurement, and combine data measurement (merged/fused data), and then compared the results. ELM classification model achieved the highest accuracy (100%) for combined data as well as for Raman and LIBS data, respectively. According to the experimental results, we can assume that Raman spectroscopy and LIBS combine can significantly improve the identification and classification accuracy of melanoma and normal specimens.

Keywords: Cancer; Diagnosis; FFPE tissue; Machine learning; Melanoma.