Label-free identification of lung cancer cells from blood cells based on surface-enhanced Raman scattering and support vector machine
| Autor: | Peng Wang, Shaoxin Li, Xingda Wu, Qiuyue Fu, Xianglin Fang, Yanjiao Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Lung
Chemistry medicine.disease Atomic and Molecular Physics and Optics respiratory tract diseases Electronic Optical and Magnetic Materials Support vector machine symbols.namesake medicine.anatomical_structure symbols Cancer research medicine Non small cell Electrical and Electronic Engineering Lung cancer neoplasms Raman scattering Label free |
| Zdroj: | Optik. 248:168157 |
| ISSN: | 0030-4026 |
| DOI: | 10.1016/j.ijleo.2021.168157 |
| Popis: | Surface-enhanced Raman scattering (SERS) technique combined with support vector machine (SVM) was implemented to identify and distinguish non-small-cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cells from normal cells including blood cells and immortalized lung cells. Compared with normal cells, the intensity of nucleic acid characteristic peaks of NSCLC cells were slightly enhanced, while that of SCLC cells was significantly enhanced. SVM with linear, polynomial and radial based functions was used to create classification models for comparison and discrimination of the SERS spectra of different types of cells. The classification accuracy of 98.8% could be achieved for differentiation between NSCLC cells and normal cells. While in the classification of SCLC cells and normal cells, as well as SCLC cells and NSCLC cells, the accuracy reached 100%. These results suggested that SERS combined with SVM provide a reliable method for the identification of NSCLC cells and SCLC cells. |
| Databáze: | OpenAIRE |
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