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13 March 2024 Cancer cell classification using phonon microscopy
Fernando Perez-Cota, Giovanna Martinez-Arellano, Salvatore La Cavera III, William Hardiman, Luke Thornton, Rafael Fuentes-Dominguez, Richard J. Smith, Alan McIntyre, Matt Clark
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Proceedings Volume PC12844, Optical Elastography and Tissue Biomechanics XI; PC1284407 (2024) https://doi.org/10.1117/12.3002781
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Cancer remains one of the most important contributors to premature mortality at the global level. The elastic properties of cells and tissue have been shown to correlate with normal, dysplastic, and cancerous states. In this work, we rely on time-resolved Brillouin scattering to characterise cancerous and normal cells with contrast provided by their elastic properties. In doing so, we achieved proof of concept that artificial intelligence can be used to differentiate between cancerous and normal cell lines with a low number of highly localised measurements. A differentiation accuracy of 93%, was obtained probing in a volume of a few microns corresponding to a single phonon measurement. Our findings suggest the possibility of potential applications for diagnostics.
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Fernando Perez-Cota, Giovanna Martinez-Arellano, Salvatore La Cavera III, William Hardiman, Luke Thornton, Rafael Fuentes-Dominguez, Richard J. Smith, Alan McIntyre, and Matt Clark "Cancer cell classification using phonon microscopy", Proc. SPIE PC12844, Optical Elastography and Tissue Biomechanics XI, PC1284407 (13 March 2024); https://doi.org/10.1117/12.3002781
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KEYWORDS
Cancer
Artificial intelligence
Phonons
Elasticity
Microscopy
Colorectal cancer
Picosecond phenomena
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