Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

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

Invited Article: Molecular biomarkers characterization for human brain glioma grading using visible resonance Raman spectroscopy

The accurate identification of the human brain tumor boundary and the complete resection of the tumor are two essential factors for the removal of the glioma tumor in brain surgery. We present a visible resonance Raman (VRR) spectroscopy technique for differentiating the brain tumor margin and glioma grading. Eighty-seven VRR spectra from twenty-one human brain specimens of four types of brain tissues, including the control, glioma grade II, III, and IV tissues, were observed. This study focuses on observing the characteristics of new biomarkers and their changes in the four types of brain tissue. We found that two new RR peaks at 1129 cm−1 and 1338 cm−1 associated with molecular vibrational bonds in four types of brain tissues are significantly different in peak intensities of VRR spectra. These two resonance enhanced peaks may arise from lactic acid/phosphatidic acid and adenosine triphosphate (ATP)/nicotinamide adenine dinucleotide, respectively. We found that lactic acid and ATP concentrations vary with glioma gratings. The higher the grade of malignancy, the more the increase in lactic acid and ATP concentrations. These two RR peaks may be considered as new molecular biomarkers and used to evaluate glioma grades and identify the margin of gliomas from the control tissues. The metabolic process of lactic acid and ATP in glioma cells based on the VRR spectral changes may reveal the Warburg hypothesis

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

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