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

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          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.

          Aim: Our 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.

          Results: Epithelial 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.

          Conclusions: The 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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          Most cited references 60

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                Author and article information

                Contributors
                Journal
                J Biomed Opt
                J Biomed Opt
                JBOPFO
                JBO
                Journal of Biomedical Optics
                Society of Photo-Optical Instrumentation Engineers
                1083-3668
                1560-2281
                18 February 2020
                February 2020
                18 February 2020
                : 25
                : 2
                Affiliations
                [a ]The Catholic University of America , Department of Biomedical Engineering, Washington, DC, United States
                [b ]The Catholic University of America , Department of Electrical Engineering and Computer Science, Washington, DC, United States
                [c ]The Catholic University of America , Department of Biology, Washington, DC, United States
                Author notes
                [* ]Address all correspondence to Christopher B. Raub, E-mail: raubc@ 123456cua.edu
                Article
                JBO-190339R 190339R
                10.1117/1.JBO.25.2.026002
                7026523
                32072775
                © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
                Page count
                Figures: 8, Tables: 2, References: 71, Pages: 17
                Product
                Funding
                Funded by: National Institute of Biomedical Imaging and Bioengineering
                Award ID: 1R03EB28017
                Categories
                Imaging
                Paper
                Custom metadata
                Lam et al.: Quantitative scoring of epithelial and mesenchymal qualities of cancer cells…

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