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Proceedings Paper

Evaluating human breast cancer cell metastasis potential using resonance Raman spectroscopy and machine learning (Conference Presentation)

Paper Abstract

This study evaluated breast cancer cells and metastasis potential using visible (532 nm) resonance Raman spectroscopy (VRRS). Three cell lines were investigated, MCF-10 (fibrocystic disease (benign)), MCF-7 (estrogen- and progesterone-receptive invasive ductal carcinoma (IDC)), and MDA-MB-231 (triple negative IDC). Peak analysis and multivariate unmixing methods including principal component analysis, partial least squares and nonnegative matrix factorization along with support vector machines were used to classify the samples. The cell lines were accurately classified with leave-one-out cross-validation. Optimal features were selected using a wrapper feature selection algorithm which helped to identify key biomarkers related to metastasis potential of the cell lines.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11234, Optical Biopsy XVIII: Toward Real-Time Spectroscopic Imaging and Diagnosis, 112340N (9 March 2020); doi: 10.1117/12.2546936
Show Author Affiliations
Lin Zhang, The City College of New York (United States)
Binlin Wu, Southern Connecticut State Univ. (United States)
Susie Boydston-White, Borough of Manhattan Community College (United States)
Kenneth Jimenez, Southern Connecticut State Univ. (United States)
Cheng-hui Liu, The City College of New York (United States)
Robert R. Alfano, The City College of New York (United States)


Published in SPIE Proceedings Vol. 11234:
Optical Biopsy XVIII: Toward Real-Time Spectroscopic Imaging and Diagnosis
Robert R. Alfano; Stavros G. Demos; Angela B. Seddon, Editor(s)

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