Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy
| Autor: | Zong Rui, Yan Zhou, Xinguang Yu, Lin Zhang, Kai Wang, Binlin Wu, Ke Zhu, Mingyue Zhao, Gangge Cheng, Robert R. Alfano, Cheng-hui Liu, Chunyuan Zhang, Lingyan Shi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Support Vector Machine
Optical Phenomena Optical Physics Spectrum Analysis Raman 01 natural sciences Protein Structure Secondary Nuclear magnetic resonance optical biopsy cancer grades Principal Component Analysis medicine.diagnostic_test Brain Neoplasms Chemistry carotenoids Margins of Excision Glioma Human brain Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials cancer margins medicine.anatomical_structure histopathology Paper medicine.medical_specialty brain Brain tumor Biomedical Engineering Nerve Tissue Proteins label-free resonance Raman 010309 optics Biomaterials Opthalmology and Optometry 0103 physical sciences Biopsy Biomarkers Tumor medicine Humans tryptophan General Grading (tumors) Receiver operating characteristic glioblastoma biomarkers Optics Optical Biopsy Lipid Metabolism medicine.disease gliomas Histopathology Neoplasm Grading |
| Zdroj: | Journal of biomedical optics, vol 24, iss 9 Journal of Biomedical Optics |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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