Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy - Bendau, Ethan; Smith, Jason; Zhang, Lin; Ackerstaff, Ellen; Kruchevsky, Natalia; Wu, Binlin; Koutcher, Jason A.; Alfano, Robert; Shi, Lingyan (Journal of Biophotonics, 2020)
Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.
22 citations (Crossref) [2023-10-31] _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jbio.202000005
Keywords: Breast; Breast Neoplasms; Collagen; Diseases; Factorization; Feature extraction; Female; Harmonic generation; Humans; Label-free detection; Lactate concentration; Magnetic resonance; Mammography; Matrix algebra; Molecular imaging; Morphological features; Morphometric analysis; Nonlinear optics; Nonnegative matrix factorization; Raman spectrometry; Raman spectroscopy; Resonance Raman spectroscopy; Second Harmonic Generation Microscopy; Spectrum Analysis; Raman; Subcellular resolution; Support vector machines; Triple Negative Breast Neoplasms; Triple-negative breast cancers; Tumors; breast; breast tumor; dept.phy; diagnostic imaging; female; human; mammography; peer-reviewed; second harmonic generation microscopy; triple negative breast cancer

Glioma tumors classified using visible resonance raman spectroscopy and machine learning - Wu, Binlin; Zhou, Yan; Zhang, Liang; Zhang, Shengjia; Yu, Xinguang; Wang, Eric; Zhu, Ke; Liu, Cheng-hui; Alfano, Robert R. (Frontiers in Optics, 2020)
Machine learning algorithms were used to classify and analyze spectral data collected by visible resonance Raman spectroscopy to distinguish normal human brain tissue and glioma tumor tissues at different grades and show promising results. © OSA 2020 © 2020 The Author(s)
0 citations (Crossref) [2023-10-31]
Keywords: dept.phy; ⛔ No DOI found

Human glioma tumors detection by a portable visible resonance Raman analyzer with a hand-held optical fiber probe - Zhou, Yan; Zhang, Shengjia; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Zhao, Mingyue; Zheng, Jichun; Zhang, Liang; Zhang, Mingqian; Liang, Qijun; Go, Yun Ii; et al. (Biomedical Vibrational Spectroscopy 2020: Advances in Research and Industry, 2020)
Based on Visible Resonance Raman (VRR) method, we have developed a novel label-free portable VRR LRR2000 Raman analyzer with a portable fiber-optic probe and used it for the classification of human gliomas ex vivo and for the analysis of changes in tumor chemical compositions in molecular level. The purpose of this study was to examine the performance of the LRR2000 Raman analyzer as an optical biopsy tool for detecting human brain tumors compared to the commercial laboratory HR800 and WITec300 micro confocal Raman spectroscopy instruments. As of 2018, a total 1,938 VRR spectra were collected using LRR2000, HR800 and WITec300 Raman system, ex vivo. Identification of the four grades of glioma tumors and control tissues was performed based on the characteristic native molecular fingerprints. LRR2000 demonstrated consistent diagnostic results with HR800 and WITec300 Raman systems. LRR2000 showed the advantages of high speed, convenience and low cost compared to the two confocal micro Raman systems. Using artificial intelligence (AI)-based analysis of part of the data, the cross-validated accuracy for identifying glioma tumors is ~90% compared with gold standard histopathology examination.
3 citations (Crossref) [2023-10-31]
Keywords: Biopsy; Brain; Chemical analysis; Handhelds; Histology; Human brain; Industrial research; Molecular biology; Optical biopsies; Optical fibers; Optical molecular Pathology; Palmprint recognition; Principal component analysis; Principle component analysis; Probes; Resonance; Resonance Raman; Spectroscopy; Spectrum analysis; Support vector machines; Tissue engineering; Tumors; Vibrational spectroscopy; dept.phy; glioma tumor; handheld LRR2000 analyzer; human brain tissues; optical biopsy; principle component analysis(PCA); support vector machine (SVM); visible resonance Raman (VRR)