Atomic Force Microscopy Detects the Difference in Cancer Cells of Different Neoplastic Aggressiveness via Machine Learning. Issue 8 (27th May 2021)

• Record Type: Journal Article
• Authors: Prasad, Siona; Rankine, Alex; Prasad, Tarun; Song, Patrick; Dokukin, Maxim E.; Makarova, Nadezda; Backman, Vadim; Sokolov, Igor
• Is Part Of: Advanced nanobiomed research. Volume 1:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-27
• Subjects: artificial intelligence -- atomic force microscopy -- cancer -- imaging -- nanomedicine; Nanomedicine -- Periodicals; Biomedical engineering -- Periodicals; Biomedical materials -- Periodicals; Nanomedicine; Nanostructures; Bioengineering; Biocompatible Materials; Electronic journals; Periodicals; Periodical; 610.28
• DOI: 10.1002/anbr.202000116
• Languages: English
• ISSNs: 2699-9307

Abstract

Abstract : 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. Abstract : Machine learning analysis of atomic force microscopy images obtained in Ringing mode allows to detect cancer cells of different neoplastic aggressiveness. It is demonstrated for the case of human colorectal epithelial cancer. The accuracy reaches 94% at the level of single cell, which is better or comparable to single cell genetic identification. The reasons of the observed differences are discussed.