Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning

Pérez-Cota, Fernando; Martínez-Arellano, Giovanna; La Cavera III, Salvatore; Hardiman, William; Thornton, Luke; Fuentes-Domínguez, Rafael; Smith, Richard J.; McIntyre, Alan; Clark, Matt

Abstract

There is a consensus about the strong correlation between the elasticity of cells and tissue and their normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In this work, we explore the possibility of using phonon acoustics for this purpose. We used phonon microscopy to obtain a measure of the elastic properties between cancerous and normal breast cells. Utilising the raw time-resolved phonon-derived data (300 k individual inputs), we employed a deep learning technique to differentiate between MDA-MB-231 and MCF10a cell lines. We achieved a 93% accuracy using a single phonon measurement in a volume of approximately 2.5 μm3. We also investigated means for classification based on a physical model that suggest the presence of unidentified mechanical markers. We have successfully created a compact sensor design as a proof of principle, demonstrating its compatibility for use with needles and endoscopes, opening up exciting possibilities for future applications.

Citation

Pérez-Cota, F., Martínez-Arellano, G., La Cavera III, S., Hardiman, W., Thornton, L., Fuentes-Domínguez, R., …Clark, M. (2023). Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning. Scientific Reports, 13, Article 16228. https://doi.org/10.1038/s41598-023-42793-9

• Journal Article Type: Article
• Acceptance Date: Sep 14, 2023
• Online Publication Date: Sep 27, 2023
• Publication Date: 2023
• Deposit Date: Sep 18, 2023
• Publicly Available Date: Sep 27, 2023
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Article Number: 16228
• DOI: https://doi.org/10.1038/s41598-023-42793-9
• Keywords: Multidisciplinary
• Public URL: https://nottingham-repository.worktribe.com/output/25361649
• Publisher URL: https://www.nature.com/articles/s41598-023-42793-9
• Additional Information: Received: 17 April 2023; Accepted: 14 September 2023; First Online: 27 September 2023; : The authors declare no competing interests.