Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy. - Zhou, Yan; Liu, Cheng-Hui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhu, Ke; Wang, Kai; Zhang, Chunyuan; Zhao, Mingyue; Zong, Rui; Zhang, Lin; Shi, Lingyan; et al. (Journal of biomedical optics, 2019)
Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins.
Keywords: *Biomarkers, Tumor/me [Metabolism]; *Brain Neoplasms/me [Metabolism]; *Brain Neoplasms/pa [Pathology]; *Glioma/me [Metabolism]; *Glioma/pa [Pathology]; *Spectrum Analysis, Raman/mt [Methods]; Amides; Amino acids; Biomarkers; Biomarkers, Tumor; Biomarkers, Tumor/ch [Chemistry]; Biopsy; Brain; Brain Neoplasms; Brain Neoplasms/su [Surgery]; Brain/me [Metabolism]; Brain/pa [Pathology]; Brain/su [Surgery]; Cancer grades; Cancer margins; Carotenoids; Carotenoids/me [Metabolism]; Diseases; Glioblastoma; Glioblastomas; Glioma; Glioma/su [Surgery]; Gliomas; Grading; Histology; Histopathology; Humans; Label free; Label-free; Lipid Metabolism; Margins of Excision; Neoplasm Grading; Nerve Tissue Proteins; Nerve Tissue Proteins/ch [Chemistry]; Nerve Tissue Proteins/me [Metabolism]; Optical Phenomena; Optical biopsies; Optical biopsy; Pigments; Principal Component Analysis; Principal component analysis; Protein Structure, Secondary; Raman spectrometry; Resonance; Resonance Raman; Spectrum Analysis, Raman; Support Vector Machine; Support vector machines; Surgery; Tryptophan; Tryptophan/me [Metabolism]; Tumors; brain; brain tumor; cancer grading; carotenoid; chemistry; dept.phy; glioma; human; light related phenomena; lipid metabolism; metabolism; nerve protein; pathology; peer-reviewed; principal component analysis; procedures; protein secondary structure; support vector machine; surgical margin; tryptophan; tumor marker

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.
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)