ArticleJournal of biomedical optics2020
Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.
Article in Journal of biomedical optics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Cell-MICS: Detecting Immune Cells With Label-Free Two-Photon Autofluorescence and Deep Learning.Journal of biophotonics · 2026Article
- Digital Holographic Microscopy for Phenotypic Profiling of Adherent Cells.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- Morphological and Optical Profiling of Melanocytes and SK-MEL-28 Melanoma Cells Via Digital Holographic Microscopy and Quantitative Phase Imaging.Advanced biology · 2025Article
- Rapid mechanical phenotyping of breast cancer cells based on stochastic intracellular fluctuations.iScience · 2024Article
- Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization.Scientific reports · 2023Article
- Digital Holographic Microscopy to Assess Cell Behavior.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Review
- Article
- Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial-Mesenchymal Transition.Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada · 2021Article
- Label-Free and Quantitative Dry Mass Monitoring for Single Cells during In Situ Culture.Cells · 2021Article
- Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Hypoxia, partial EMT and collective migration: Emerging culprits in metastasis.Translational oncology · 2020Review
- Morphology, Motility, and Cytoskeletal Architecture of Breast Cancer Cells Depend on Keratin 19 and Substrate.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition.Journal of biomedical optics · 2020Article
- Quantitative assessment of morphology and sub-cellular changes in macrophages and trophoblasts during inflammation.Biomedical optics express · 2020Article
- Comparative Study of Transcriptomics-Based Scoring Metrics for the Epithelial-Hybrid-Mesenchymal Spectrum.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
significanceWe introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies.
aimOur objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. APPROACH: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning.
resultsEpithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line.
conclusionsThe proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.
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