ArticleScientific reports2021
Morphological features of single cells enable accurate automated classification of cancer from non-cancer cell lines.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Pleomorphism in Soft Tissue Sarcomas: Molecular Characteristics and Clinical Features.Medical sciences (Basel, Switzerland) · 2026Review
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- Cell shapes decode molecular phenotypes in image-based spatial proteomics.Cell systems · 2026Article
- Maximum Matching Accuracy: An Instance Segmentation Evaluation Metric Utilizing Globally Optimal Matching.ArXiv · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Segmentation Methodologies for the Construction of Hyperspectral Cell Nuclei Databases in Histopathology.Bioengineering (Basel, Switzerland) · 2026Article
- Systematic profiling of cancer-fibroblast interactions reveals drug combinations in ovarian cancer.Molecular oncology · 2025Article
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- Changes in Melanoma Cell Morphology Following Inhibition of Cell Invasion by Third-Generation mTOR Kinase Inhibitors.International journal of molecular sciences · 2025Article
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- Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.npj biomedical innovations · 2025Article
- The Spheroid Light Microscopy Image Atlas for morphometrical analysis of three-dimensional cell cultures.Scientific data · 2025Article
- Melanoma extracellular vesicles membrane coated nanoparticles as targeted delivery carriers for tumor and lungs.Materials today. Bio · 2025Article
- Ovarian cancer cells exhibit diverse migration strategies on stiff collagenous substrata.Biophysical journal · 2024Article
- Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning.Journal of pharmaceutical sciences · 2024Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Simulation of transvascular transport of nanoparticles in tumor microenvironments for drug delivery applications.Scientific reports · 2024Article
- The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology.Discover oncology · 2023Review
- Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods.Biomedicines · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response prediction. Successful cancer characterization relies on both genetic analysis and histological scans from tumor biopsies. It is known that the cytoskeleton is significantly altered in cancer, as cellular structure dynamically remodels to promote proliferation, migration, and metastasis. We exploited these structural differences with supervised feature extraction methods to introduce an algorithm that could distinguish cancer from non-cancer cells presented in high-resolution, single cell images. In this paper, we successfully identified the features with the most discriminatory power to successfully predict cell type with as few as 100 cells per cell line. This trait overcomes a key barrier of machine learning methodologies: insufficient data. Furthermore, normalizing cell shape via microcontact printing on self-assembled monolayers enabled better discrimination of cell lines with difficult-to-distinguish phenotypes. Classification accuracy remained robust as we tested dissimilar cell lines across various tissue origins, which supports the generalizability of our algorithm.
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