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.
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.
Laser-induced fluorescence (LIF) technique was used to generate spectral signatures of endogenous fluorophores relevant to the tissue molecular composition changes in human brain glioma tumors. The goal is to study the changes of fluorescence emission spectra from endogenous fluorophores in human brain glioma of different grades, and to find new biomarkers for prognostic optical molecular pathological diagnosis. Two hundred and thirty-seven (237) native fluorescence spectra from 61 subjects were measured using LabRAM HR Evolution micro photoluminescence (PL) system for four grades of glioma tumors in ex-vivo. The differences of four grades of glioma tumors were identified by the characteristic fluorophores fingerprints under the excitation laser wavelength at UV 325nm. To our best knowledge, this is the first report for human brain study using this technique. The fluorescence peaks of biomarkers with major contribution were found, including tryptophan, collagen, elastin, reduced nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD) and phospholipids that play important roles in the cellular energy metabolism and glycolysis pathway. The ratios of peak intensities and the peak positions in fluorescence spectra of may be used to diagnose human brain diseases or to guide biopsy during surgical resection.