7 March 2016 Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies
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Abstract
Raman spectroscopy has become widely used for diagnostic purpose of breast, lung and brain cancers. This report introduced a new approach based on spatial frequency spectra analysis of the underlying tissue structure at different stages of brain tumor. Combined spatial frequency spectroscopy (SFS), Resonance Raman (RR) spectroscopic method is used to discriminate human brain metastasis of lung cancer from normal tissues for the first time. A total number of thirty-one label-free micrographic images of normal and metastatic brain cancer tissues obtained from a confocal micro- Raman spectroscopic system synchronously with examined RR spectra of the corresponding samples were collected from the identical site of tissue. The difference of the randomness of tissue structures between the micrograph images of metastatic brain tumor tissues and normal tissues can be recognized by analyzing spatial frequency. By fitting the distribution of the spatial frequency spectra of human brain tissues as a Gaussian function, the standard deviation, σ, can be obtained, which was used to generate a criterion to differentiate human brain cancerous tissues from the normal ones using Support Vector Machine (SVM) classifier. This SFS-SVM analysis on micrograph images presents good results with sensitivity (85%), specificity (75%) in comparison with gold standard reports of pathology and immunology. The dual-modal advantages of SFS combined with RR spectroscopy method may open a new way in the neuropathology applications.
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Yan Zhou, Yan Zhou, Cheng-hui Liu, Cheng-hui Liu, Yang Pu, Yang Pu, Gangge Cheng, Gangge Cheng, Lixin Zhou, Lixin Zhou, Jun Chen, Jun Chen, Ke Zhu, Ke Zhu, Robert R. Alfano, Robert R. Alfano, "Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies", Proc. SPIE 9703, Optical Biopsy XIV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 97031R (7 March 2016); doi: 10.1117/12.2213287; https://doi.org/10.1117/12.2213287
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