ArticleBiomedicines2023
Identification of Geometrical Features of Cell Surface Responsible for Cancer Aggressiveness: Machine Learning Analysis of Atomic Force Microscopy Images of Human Colorectal Epithelial Cells.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Nanomechanical Phenotyping of Circulating Tumor Cells with Atomic Force Microscopy.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine learning and artificial intelligence: Enabling the clinical translation of atomic force microscopy-based biomarkers for cancer diagnosis.Computational and structural biotechnology journal · 2024Review
- Mechanical spectroscopy of materials using atomic force microscopy (AFM-MS).Materials today (Kidlington, England) · 2024Article
- On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition.Physical chemistry chemical physics : PCCP · 2024Review
- The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours.Bioengineering (Basel, Switzerland) · 2024Review
- PeakForce AFM Analysis Enhanced with Model Reduction Techniques.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
It has been recently demonstrated that atomic force microscopy (AFM) allows for the rather precise identification of malignancy in bladder and cervical cells. Furthermore, an example of human colorectal epithelial cells imaged in AFM Ringing mode has demonstrated the ability to distinguish cells with varying cancer aggressiveness with the help of machine learning (ML). The previously used ML methods analyzed the entire cell image. The problem with such an approach is the lack of information about which features of the cell surface are associated with a high degree of aggressiveness of the cells. Here we suggest a machine-learning approach to overcome this problem. Our approach identifies specific geometrical regions on the cell surface that are critical for classifying cells as highly or lowly aggressive. Such localization gives a path to colocalize the newly identified features with possible clustering of specific molecules identified via standard bio-fluorescence imaging. The biological interpretation of the obtained information is discussed.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.