Identification of multiple myeloma resistant cells using machine learning and laser tweezers Raman spectroscopy

Xingfei Xie; Ziqing Wu; Hang Yuan; Zhehai Zhou; Pengfei Zhang

doi:10.1117/12.2686545

27 November 2023 Identification of multiple myeloma resistant cells using machine learning and laser tweezers Raman spectroscopy

Xingfei Xie, Ziqing Wu, Hang Yuan, Zhehai Zhou, Pengfei Zhang

Author Affiliations +

Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 127701X (2023) https://doi.org/10.1117/12.2686545
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Multiple myeloma may develop resistance to certain drugs during chemotherapy, which have a fatal impact on treatment efficacy. At present, the drug resistance detection methods for multiple myeloma, such as proteomic identification and clone formation analysis, are relatively complex, and the accuracy and detection time are not ideal. In our work, laser tweezers Raman spectroscopy was used to collect 412 groups of spectra of two kinds of cells, namely, MM.1R and MM.1S, which were respectively resistant to dexamethasone and sensitive to dexamethasone. We selected support vector machine, random forest, linear discriminant analysis and other algorithms to train the pretreated Raman spectra, and the recognition accuracy on the test set was above 95%. This result shows that the combination of laser tweezers Raman spectroscopy and artificial intelligence algorithm can quickly detect drug resistance of cancer cells.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingfei Xie, Ziqing Wu, Hang Yuan, Zhehai Zhou, and Pengfei Zhang "Identification of multiple myeloma resistant cells using machine learning and laser tweezers Raman spectroscopy", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 127701X (27 November 2023); https://doi.org/10.1117/12.2686545

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