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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.

42 citations (Crossref) [2023-10-31] Place: United States Zhou, Yan. PLA Air Force Medical Center, Department of Neurosurgery, Beijing, China. Liu, Cheng-Hui. City University of New York, Institute for Ultrafast Spectroscopy and Lasers, Department of Physics, United States. Wu, Binlin. Southern Connecticut State University, CSCU Center for Nanotechnology, Physics Department, New Haven, United States. Yu, Xinguang. PLA General Hospital, Department of Neurosurgery, Beijing, China. Cheng, Gangge. PLA Air Force Medical Center, Department of Neurosurgery, Beijing, China. Zhu, Ke. Chinese Academy of Sciences, Institute of Physics, Beijing, China. Wang, Kai. Jilin University, State Key Laboratory of Superhard Materials, Changchun, China. Zhang, Chunyuan. City University of New York, Institute for Ultrafast Spectroscopy and Lasers, Department of Physics, United States. Zhao, Mingyue. PLA Air Force Medical Center, Department of Neurosurgery, Beijing, China. Zong, Rui. PLA General Hospital, Department of Neurosurgery, Beijing, China. Zhang, Lin. City University of New York, Institute for Ultrafast Spectroscopy and Lasers, Department of Physics, United States. Shi, Lingyan. University of California San Diego, Department of Bioengineering, La Jolla, California, United States. Alfano, Robert R. City University of New York, Institute for Ultrafast Spectroscopy and Lasers, Department of Physics, United States.