Quantitative characterization for identification of brain cancer surgical margin using resonance Raman spectroscopy - Zhou, Yan; Liu, Chenghui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhang, Chunyuan; Alfano, R R (OSA - The Optical Society, 2017)
A new criterion was developed to characterize brain tissue using resonance Raman spectroscopy, by which, negative margins of cancer can be differentiated from normal tissues. This method may help a surgeon better decide surgical margins. © OSA 2017.
Keywords: Brain cancer; Brain tissue; Diseases; Normal tissue; Quantitative characterization; Raman spectroscopy; Resonance Raman spectroscopy; Surgery; Tissue; dept.phy

Assessment of basal cell carcinoma from normal human skin tissues using Resonance Raman spectroscopy - Liu, Chenghui; Wu, Binlin; Sordillo, Laura A; Boydstonwhite, Susie; Sriramoju, Vidyasagar; Zhang, Chunyuan; Beckman, Hugh; Zhang, Lin; Pei, Zhe; Shi, Lingyan; Alfano, R R (OSA - The Optical Society, 2018)
VRR spectroscopy was used for BCC and normal skin tissues with 532nm excitation. The spectra showed significant changes in collagen, carotenoids and lipids. These enhanced fingerprints demonstrate a potential use as label-free pathology method. © 2018 The Author(s).
Keywords: Basal cell carcinoma; Histology; Human skin; Label free; Normal skin; Resonance Raman spectroscopy; Tissue; dept.phy

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

Diagnosis of glioma brain cancer using visible resonance Raman spectroscopy - Zhou, Yan; Wu, Binlin; Liu, Chenghui; Yu, Xinguang; Cheng, Gangge; Wang, Kai; Zhang, Chunyuan; Shi, Lingyan; Alfano, R R (OSA, 2018)
Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).
Keywords: Brain cancer; Brain tissue; Different grades; Glioma tissues; High-accuracy; Principal component analysis; Raman spectroscopy; Resonance Raman spectroscopy; Spectral data; Tumors; dept.phy

Statistical analysis and machine learning algorithms for optical biopsy - Wu, Binlin; Liu, Cheng-hui; Boydston-White, Susie; Beckman, Hugh; Sriramoju, Vidyasagar; Sordillo, Laura; Zhang, Chunyuan; Zhang, Lin; Shi, Lingyan; Smith, Jason; Bailin, Jacob; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
Analyzing spectral or imaging data collected with various optical biopsy methods is often times difficult due to the complexity of the biological basis. Robust methods that can utilize the spectral or imaging data and detect the characteristic spectral or spatial signatures for different types of tissue is challenging but highly desired. In this study, we used various machine learning algorithms to analyze a spectral dataset acquired from human skin normal and cancerous tissue samples using resonance Raman spectroscopy with 532nm excitation. The algorithms including principal component analysis, nonnegative matrix factorization, and autoencoder artificial neural network are used to reduce dimension of the dataset and detect features. A support vector machine with a linear kernel is used to classify the normal tissue and cancerous tissue samples. The efficacies of the methods are compared.
Keywords: Artificial neural network; Biological basis; Biopsy; Cancer diagnosis; Cancerous tissues; Factorization; Learning algorithms; Learning systems; Least squares approximations; Machine learning; Matrix algebra; Neural networks; Nonnegative matrix factorization; Optical biopsies; Optical biopsy; Partial least square (PLS); Partial least squares; Principal component analysis; Resonance Raman spectroscopy; Spatial signature; Support vector machine; Support vector machines; Tissue; Tissue engineering; dept.phy

Visible resonance Raman spectroscopy detect key molecular biomarker vibrations to characterize for human brain gliomas - Zhou, Yan; Liu, Cheng-hui; Wu, Binlin; Yu, Xinguang; Gangge Cheng; Zhang, Chunyuan; Chen, Hong; Li, Shenglin; Zong, Rui; Liang, Qijun; Zhang, Ce; Zhang, Mingqian; et al. (Biomedical Imaging and Sensing Conference, 2018)
Visible resonant Raman (VRR) spectroscopy provides an effective way to enhance Raman signal from particular bonds associated with key molecules due to changes on molecular level. This paper reports on the VRR use for detection of human brain the control and gliomas of three grades. From the RR spectra additional two molecular vibrational biomarkers at 1129cm-1 and 1338cm-1, for the four types of brain tissues are significantly different in intensity. The new RR spectral peaks can be used as molecular biomarkers to evaluate glioma grades and identify the margin of gliomas from the controls. The metabolic process of glioma cells based on the RR spectral changes may reveal the Warburg hypothesis.
Keywords: 532nm; Biomarkers; Brain; Human brain tumors; Medical imaging; Raman probe; Raman spectroscopy; Resonance Raman; Tumors; Visible resonance Raman (VRR); confical Raman probe; dept.phy; gliomas; human brain tumor

Invited Article: Molecular biomarkers characterization for human brain glioma grading using visible resonance Raman spectroscopy - Zhou, Yan; Liu, Cheng-hui; Wu, Binlin; Zhang, Chunyuan; Yu, Xinguang; Cheng, Gangge; Chen, Hong; Li, Shenglin; Liang, Qijun; Zhang, Mingqian; Zhu, Ke; Shi, Lingyan; et al. (APL Photonics, 2018)
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. © 2018 Author(s).
Keywords: Adenosinetriphosphate; Biomarkers; Brain; Control tissues; Grading; Histology; Human brain tumors; Lactic acid; Metabolic process; Molecular biomarker; Resonance; Resonance Raman; Resonance Raman spectroscopy; Resonance enhanced; Spectral change; Tumors; 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

Resonance raman of BCC and normal skin - Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; M.d, Hugh Beckman; Alfano, Robert R. (Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2017)
The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.
Keywords: Basal cell carcinoma; Basel cell carcinoma (BCC); Biopsy; Cell carcinoma; Diagnostic algorithms; Histology; Human skin; Human skin tissues; Laser excitation; Principal component analysis; Principal component analysis (PCA); Resonance; Resonance Raman spectra; Resonance Raman spectroscopy; Skin cancer; Skin cancers; Spectral differences; Statistical methods; Support vector machine (SVM); Support vector machines; Tissue; Tissue engineering; dept.phy

Resonance raman imaging for detecting and monitoring molecular pathological changes in human brain tumors related to Warburg effect - Zhou, Yan; Liu, Cheng-hui; Zhu, Ke; Zhang, Chunyuan; Yang, Yang; Yu, Xinguang; Hu, Hailong; Cheng, Gangge; Wu, Binlin; Shi, Lingyan; Alfano, Robert R. (Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2018)
The goal of the research is to determine the prognostic molecular pathological changes in components and composition, for human brain glioma gradings in comparison with normal tissues in three-dimensional Raman imaging profiles by visible Resonance Raman (VRR) imaging. VRR images from twenty-five specimens including three healthy tissues, one normal control, and twenty-one glioma tissues of grades II, II-III and III-IV with histology examination were measured and investigated using WITec300R confocal micro Raman imaging system with laser excitation of 532nm. Two-dimensional RR spectral mappings performed in 20μm x 20μm generated 400 images which integrated the intensity of the specific biochemical bonds as the third dimension. The three-dimension (3D) map demonstrated the spatial distributions of three selected sets of RR spectra of molecular biomarkers, and revealed significant differences in the spectra between normal and glioma tissues of different grades due to the composition changes in key molimageecules. These RR molecular spectral fingerprints have displayed: a clear enhancement of RR vibrational modes at 1129-1131cm-1 and 2934cm-1 which are supposed to be arising from lipoproteins; evident decreased RR vibrational modes at 1442cm-1 and 2854cm-1 which are from saturated fatty acids bonds in all-grades of glioma brain tissues compared with normal tissues; and the enhanced RR spectral modes of 1129 cm-1 and 2938cm-1 which suggest contribution from lactate. These findings may provide a novel proof for anaerobic glycolysis metabolic process in brain glioma cancer tissues that has been explained by Warburg effects.
Keywords: 2-d imaging; 2D imaging; Biopsy; Brain; Gliomas; Histology; Human Brain Tissues; Human brain; Laser excitation; Molecular imaging; Resonance; Resonance Raman; Saturated fatty acids; Tumors; Visible resonance Raman (VRR); Warburg effect; Warburg effects; dept.phy