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Proceedings Paper

Resonance Raman Spectroscopy of human brain metastasis of lung cancer analyzed by blind source separation
Author(s): Yan Zhou; Cheng-Hui Liu; Yang Pu; Gangge Cheng; Xinguang Yu; Lixin Zhou; Dongmei Lin; Ke Zhu; Robert R. Alfano
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Paper Abstract

Resonance Raman (RR) spectroscopy offers a novel Optical Biopsy method in cancer discrimination by a means of enhancement in Raman scattering. It is widely acknowledged that the RR spectrum of tissue is a superposition of spectra of various key building block molecules. In this study, the Resonance Raman (RR) spectra of human metastasis of lung cancerous and normal brain tissues excited by a visible selected wavelength at 532 nm are used to explore spectral changes caused by the tumor evolution. The potential application of RR spectra human brain metastasis of lung cancer was investigated by Blind Source Separation such as Principal Component Analysis (PCA). PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (PCs). The results show significant RR spectra difference between human metastasis of lung cancerous and normal brain tissues analyzed by PCA. To evaluate the efficacy of for cancer detection, a linear discriminant analysis (LDA) classifier is utilized to calculate the sensitivity, and specificity and the receiver operating characteristic (ROC) curves are used to evaluate the performance of this criterion. Excellent sensitivity of 0.97, specificity (close to 1.00) and the Area Under ROC Curve (AUC) of 0.99 values are achieved under best optimal circumstance. This research demonstrates that RR spectroscopy is effective for detecting changes of tissues due to the development of brain metastasis of lung cancer. RR spectroscopy analyzed by blind source separation may have potential to be a new armamentarium.

Paper Details

Date Published: 8 February 2017
PDF: 7 pages
Proc. SPIE 10051, Neural Imaging and Sensing, 100511I (8 February 2017); doi: 10.1117/12.2254465
Show Author Affiliations
Yan Zhou, Air Force General Hospital, PLA (China)
Cheng-Hui Liu, The City College of New York (United States)
Yang Pu, The City College of New York (United States)
Gangge Cheng, Air Force General Hospital, PLA (China)
Xinguang Yu, PLA General Hospital (China)
Lixin Zhou, Beijing Cancer Hospital (China)
Dongmei Lin, Beijing Cancer Hospital (China)
Ke Zhu, Institute of Physics (China)
Robert R. Alfano, The City College of New York (United States)


Published in SPIE Proceedings Vol. 10051:
Neural Imaging and Sensing
Qingming Luo; Jun Ding, Editor(s)

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