Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.

Journal: Journal of biomedical optics

PMID: 32072775

Abstract

SIGNIFICANCE: We 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.

Authors

Van Lam
Thanh Nguyen
Vy Bui
Byung Min Chung

The Catholic Univ. of America, United States.
Lin-Ching Chang
George Nehmetallah

Department of Electrical Engineering, The Catholic University of America, Washington, DC 20064.
Christopher Raub

The Catholic Univ. of America, United States.

Keywords

Algorithms Breast Neoplasms Epithelial Cells Female Fibroblasts Gingiva Holography Humans Machine Learning MCF-7 Cells Mesenchymal Stem Cells

External Resources

View on PubMed Access via DOI PubMed (32072775)

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging.

Abstract

Authors

Keywords

External Resources

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