Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning.
Sci Rep
; 13(1): 16228, 2023 09 27.
Article
en En
| MEDLINE
| ID: mdl-37758808
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.
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1
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Neoplasias
Tipo de estudio:
Guideline
Idioma:
En
Revista:
Sci Rep
Año:
2023
Tipo del documento:
Article