Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning

Cian You; Jui-Yun Yi; Ting-Wei Hsu; Sheng-Lung Huang

doi:10.1117/1.JBO.28.9.096005

14 September 2023 Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning

Cian You, Jui-Yun Yi, Ting-Wei Hsu, Sheng-Lung Huang

Author Affiliations +

Journal of Biomedical Optics, Vol. 28, Issue 9, 096005 (September 2023). https://doi.org/10.1117/1.JBO.28.9.096005

Abstract

Significance

An integrated cellular-resolution optical coherence tomography (OCT) module with near-infrared Raman spectroscopy was developed on the discrimination of various skin cancer cells and normal cells. Micron-level three-dimensional (3D) spatial resolution and the spectroscopic capability on chemical component determination can be obtained simultaneously.

Aim

We experimentally verified the effectiveness of morphology, intensity, and spectroscopy features for discriminating skin cells.

Approach

Both spatial and spectroscopic features were employed for the discrimination of five types of skin cells, including keratinocytes (HaCaT), the cell line of squamous cell carcinoma (A431), the cell line of basal cell carcinoma (BCC-1/KMC), primary melanocytes, and the cell line of melanoma (A375). The cell volume, compactness, surface roughness, average intensity, and internal intensity standard deviation were extracted from the 3D OCT images. After removing the fluorescence components from the acquired Raman spectra, the entire spectra (600 to 2100 cm^{− 1}) were used.

Results

An accuracy of 85% in classifying five types of skin cells was achieved. The cellular-resolution OCT images effectively differentiate cancer and normal cells, whereas Raman spectroscopy can distinguish the cancer cells with nearly 100% accuracy.

Conclusions

Among the OCT image features, cell surface roughness, internal average intensity, and standard deviation of internal intensity distribution effectively differentiate the cancerous and normal cells. The three features also worked well in sorting the keratinocyte and melanocyte. Using the full Raman spectra, the melanoma and keratinocyte-based cell carcinoma cancer cells can be discriminated effectively.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Cian You, Jui-Yun Yi, Ting-Wei Hsu, and Sheng-Lung Huang "Integration of cellular-resolution optical coherence tomography and Raman spectroscopy for discrimination of skin cancer cells with machine learning," Journal of Biomedical Optics 28(9), 096005 (14 September 2023). https://doi.org/10.1117/1.JBO.28.9.096005

Received: 6 February 2023; Accepted: 5 September 2023; Published: 14 September 2023

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