Atomic Force Microscopy Detects the Difference in Cancer Cells of Different Neoplastic Aggressiveness via Machine Learning

Advanced NanoBiomed Research

Vadim Backman¹  Patrick Song²  Tarun Prasad²  Igor Sokolov²  Siona Prasad²  Nadezda Makarova²  Alex Rankine²  Maxim E. Dokukin³ 

[1] Department of Biomedical Engineering Northwestern University Evanston IL 60208 USA;Department of Mechanical Engineering Tufts University Medford MA 02155 USA;NanoScience Solutions, Inc Arlington VA 22203 USA;

关键词: artificial intelligence;    atomic force microscopy;    cancer;    imaging;    nanomedicine;   

DOI  :  10.1002/anbr.202000116

来源: DOAJ

【 摘 要 】

A novel method based on atomic force microscopy (AFM) working in Ringing mode (RM) to distinguish between two similar human colon epithelial cancer cell lines that exhibit different degrees of neoplastic aggressiveness is reported on. The classification accuracy in identifying the cell line based on the images of a single cell can be as high as 94% (the area under the receiver operating characteristic [ROC] curve is 0.99). Comparing the accuracy using the RM and the regular imaging channels, it is seen that the RM channels are responsible for the high accuracy. The cells are also studied with a traditional AFM indentation method, which gives information about cell mechanics and the pericellular coat. Although a statistically significant difference between the two cell lines is also seen in the indentation method, it provides the accuracy of identifying the cell line at the single‐cell level less than 68% (the area under the ROC curve is 0.73). Thus, AFM cell imaging is substantially more accurate in identifying the cell phenotype than the traditional AFM indentation method. All the obtained cell data are collected on fixed cells and analyzed using machine learning methods. The biophysical reasons for the observed classification are discussed.

【 授权许可】

Unknown