A pilot study for distinguishing basal cell carcinoma from normal human skin tissues using visible resonance Raman spectroscopy - Liu, Cheng-hui; Wu, Binlin; Sordillo, Laura A.; Boydston-White, Susie; Sriramoju, Vidyasagar; Zhang, Chunyuan; Beckman, Hugh; Zhang, Lin; Pei, Zhe; Shi, Lingyan; Alfano, Robert R. (Journal of Cancer Metastasis and Treatment, 2019)
Keywords: dept.phy; peer-reviewed

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