Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning
Mikhail Petrov 1, Nadezhda Makarova 1, Amir Monemian 2, Jean Pham 2, Małgorzata Lekka 3, Igor Sokolov 1 4 Affiliations Expand Abstract The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by […]
2021 | ATOMIC FORCE MICROSCOPY DETECTS THE DIFFERENCE IN CANCER CELLS OF DIFFERENT NEOPLASTIC AGGRESSIVENESS VIA MACHINE LEARNING
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 […]
2018 | NONINVASIVE DIAGNOSTIC IMAGING USING MACHINE-LEARNING ANALYSIS OF NANORESOLUTION IMAGES OF CELL SURFACES: DETECTION OF BLADDER CANCER
We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic force microscopy (AFM), subresonance tapping, and machine-leaning analysis. The surface parameters, which are typically used in engineering to describe surfaces, are used to classify cells. The method is applied to […]
2009 | AFM DETECTS DIFFERENCES IN THE SURFACE BRUSH OF NORMAL AND CANCEROUS CERVICAL CELLS
The atomic force microscope is broadly used to study the morphology of cells but it can also probe the mechanics of cells. It is now known that cancerous cells may have different mechanical properties than normal cells but the reasons for these differences are poorly understood. Here we report quantitatively the differences between normal and […]
