Combined spatial frequency spectroscopy analysis with visible resonance Raman for optical biopsy of human brain metastases of lung cancers

Author:

Zhou Yan1,Liu Cheng-Hui2,Pu Yang2,Wu Binlin3ORCID,Nguyen Thien An2,Cheng Gangge1,Zhou Lixin4,Zhu Ke5,Chen Jun6,Li Qingbo7,Alfano Robert R.2

Affiliation:

1. The Air Force Medical Center, PLA, No. 30, Fuchenglu, Haidian District, Beijing 100142, P. R. China

2. Department of Physics, The City College of the City, University of New York, Institute of Ultrafast Spectroscopy and Lasers, NY 10031, USA

3. Physics Department and CSCU Center for Nanotechnology, Southern Connecticut State University, 501 Crescent Street, New Haven, CT 06515, USA

4. Beijing Cancer Hospital, No. 52, Fuchenglu, Haidian District, Beijing 100142, P. R. China

5. Institute of Physics, Chinese Academy of Sciences (CAS), Beijing 100190, P. R. China

6. Department of Cardiology, Tianjin Medical, University General Hospital, Tianjin 300052, P. R. China

7. Beihang University, Xueyuan Road, No. 37, Haidian District, Beijing 100191, P. R. China

Abstract

The purpose of this study is to examine optical spatial frequency spectroscopy analysis (SFSA) combined with visible resonance Raman (VRR) spectroscopic method, for the first time, to discriminate human brain metastases of lung cancers adenocarcinoma (ADC) and squamous cell carcinoma (SCC) from normal tissues. A total of 31 label-free micrographic images of three types of brain tissues were obtained using a confocal micro-Raman spectroscopic system. VRR spectra of the corresponding samples were synchronously collected using excitation wavelength of 532[Formula: see text]nm from the same sites of the tissues. Using SFSA method, the difference in the randomness of spatial frequency structures in the micrograph images was analyzed using Gaussian function fitting. The standard deviations, [Formula: see text] calculated from the spatial frequencies of the micrograph images were then analyzed using support vector machine (SVM) classifier. The key VRR biomolecular fingerprints of carotenoids, tryptophan, amide II, lipids and proteins (methylene/methyl groups) were also analyzed using SVM classifier. All three types of brain tissues were identified with high accuracy in the two approaches with high correlation. The results show that SFSA–VRR can potentially be a dual-modal method to provide new criteria for identifying the three types of human brain tissues, which are on-site, real-time and label-free and may improve the accuracy of brain biopsy.

Publisher

World Scientific Pub Co Pte Lt

Subject

Biomedical Engineering,Atomic and Molecular Physics, and Optics,Medicine (miscellaneous),Electronic, Optical and Magnetic Materials

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