Combining fiber optical tweezers and Raman spectroscopy for rapid identification of melanoma. Issue 12 (11th September 2022)

• Record Type: Journal Article
• Authors: Qiu, Xun; He, Tao; Wu, Xingda; Wang, Peng; Wang, Xin; Fu, Qiuyue; Fang, Xianglin; Li, Shaoxin; Li, Ying
• Is Part Of: Journal of biophotonics. Volume 15:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-11
• Subjects: fiber optical tweezer -- melanoma -- Raman; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.202200158
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Cutaneous melanoma is a skin tumor with a high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Then, combined with Principal Component Analysis and Support Vector Machines (PCA‐SVM) classification model, to achieve the classification of common mutant, wild‐type and drug‐resistant melanoma cells. A total of 150 Raman spectra of 30 cells were collected from mutant, wild‐type and drug‐resistant melanoma cell lines, and the classification accuracy was 92%, 94%, 97.5%, respectively. These results suggest that the study of tumor cells based on fiber optical tweezers and Raman spectroscopy is a promising method for early and rapid identification and diagnosis of tumor cells. Abstract : Cutaneous melanoma is a skin tumor with high degree of malignancy and fatality rate, the incidence of which has increased in recent years. Therefore, a rapid and sensitive diagnostic technique of melanoma cells is urgently needed. In this paper, we present a new approach using fiber optical tweezers to manipulate melanoma cells to measure their Raman spectra. Combined with Principal Component Analysis and Support Vector Machines (PCA‐SVM), common mutant, wild‐type and drug‐resistant melanoma cells can be classified with high accuracy.