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9 March 2020 Evaluating human breast cancer cell metastasis potential using resonance Raman spectroscopy and machine learning (Conference Presentation)
Lin Zhang, Binlin Wu, Susie Boydston-White, Kenneth Jimenez, Cheng-hui Liu, Robert R. Alfano
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Proceedings Volume 11234, Optical Biopsy XVIII: Toward Real-Time Spectroscopic Imaging and Diagnosis; 112340N (2020) https://doi.org/10.1117/12.2546936
Event: SPIE BiOS, 2020, San Francisco, California, United States
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.
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Lin Zhang, Binlin Wu, Susie Boydston-White, Kenneth Jimenez, Cheng-hui Liu, and Robert R. Alfano "Evaluating human breast cancer cell metastasis potential using resonance Raman spectroscopy and machine learning (Conference Presentation)", Proc. SPIE 11234, Optical Biopsy XVIII: Toward Real-Time Spectroscopic Imaging and Diagnosis, 112340N (9 March 2020); https://doi.org/10.1117/12.2546936
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KEYWORDS
Machine learning
Cancer
Breast cancer
Raman spectroscopy
Principal component analysis
Detection and tracking algorithms
Feature extraction
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