Raman spectroscopy as a promising noninvasive tool in brain cancer detection

Kumar, Piyush

doi:10.1142/s1793545817300129

Cited by 10 publications

(9 citation statements)

References 48 publications

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“…In particular, Raman spectroscopy could be used to automatically evaluate lipids and amino acids concentrations from the analysis of the fingerprint region, as they seem to be important biomarkers for brain tumor recognition. It should be noted that these results are consistent with 10 years of successful Raman applications 7 , 39 – 42 (both ex vivo 43 and in vivo 14 ) to the detection of brain tumors, of whom many studies involving handheld fiber probes such as ours and involving similar numbers of patients/subjects. For example, Jermyn et al.…”

Section: Discussionsupporting

confidence: 82%

In vivo detection of murine glioblastoma through Raman and reflectance fiber-probe spectroscopies

et al. 2020

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Significance: Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. With a worldwide incidence rate of 2 to 3 per 100,000 people, it accounts for more than 60% of all brain cancers; currently, its 5-year survival rate is <5%. GBM treatment relies mainly on surgical resection. In this framework, multimodal optical spectroscopy could provide a fast and label-free tool for improving tumor detection and guiding the removal of diseased tissues. Aim: Discriminating healthy brain from GBM tissues in an animal model through the combination of Raman and reflectance spectroscopies. Approach: EGFP-GL261 cells were injected into the brains of eight laboratory mice for inducing murine GBM in these animals. A multimodal optical fiber probe combining fluorescence, Raman, and reflectance spectroscopy was used to localize in vivo healthy and tumor brain areas and to collect their spectral information. Results: Tumor areas were localized through the detection of EGFP fluorescence emission. Then, Raman and reflectance spectra were collected from healthy and tumor tissues, and later analyzed through principal component analysis and linear discriminant analysis in order to develop a classification algorithm. Raman and reflectance spectra resulted in 92% and 93% classification accuracy, respectively. Combining together these techniques allowed improving the discrimination between healthy and tumor tissues up to 97%. Conclusions: These preliminary results demonstrate the potential of multimodal fiber-probe spectroscopy for in vivo label-free detection and delineation of brain tumors, and thus represent an additional, encouraging step toward clinical translation and deployment of fiber-probe spectroscopy.

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Section: Discussionsupporting

confidence: 82%

In vivo detection of murine glioblastoma through Raman and reflectance fiber-probe spectroscopies

et al. 2020

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“…The decrease in the lipid concentration in the malignant gliomas has been reported in previous studies on glioblastoma multiforme (GBM) in the human brain using Raman spectroscopy in vitro and ex vivo. [20][21][22]27,81,82…”

Section: Identification Of Glioma Grades By the Intensity Ratio Of Prmentioning

confidence: 99%

“…In tissue, the 532 nm serves as the key wavelength to excite tissue for the resonance process located in pre-electronic region for incoming and outgoing Raman resonance for many native molecules in tissue of flavins, NADH, collagen, and tryptophan via one-or two-photon excitation. Among the conventional histopathology and newly explored molecular diagnosis methods mentioned above, 14 Raman spectroscopy can analyze the changes of the chemical composition of different lesions in human tissues and the slight changes of protein structure and nucleic acids [15][16][17][18][19][20] using intrinsic molecular fingerprints in situ, label-free at near real time, 17,[21][22][23][24][25][26][27][28][29][30][31][32][33][34] and low cost. Raman spectral changes can reveal the metabolic processes of brain tissue 24,25,30 and can potentially be used for margin assessment even during surgery.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Liu

et al. 2019

J. Biomed. Opt.

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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.

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“…Among the newly explored molecular diagnosis methods, Raman spectroscopy can analyze the changes in the chemical compositions of different lesions in human tissue and the small changes in protein structure and nucleic acids [ 15 , 16 , 17 , 18 , 19 , 20 ] using intrinsic molecular fingerprints in situ, being label-free and near-real-time [ 17 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], and at a low cost. Raman spectral changes can reveal the metabolic processes of brain tissue [ 24 , 25 , 30 ] and can potentially be used for margin assessment, even during surgery [ 24 , 30 , 31 , 32 , 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma

Zhang

Zhou

et al. 2023

Cancers

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There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

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Raman spectroscopy as a promising noninvasive tool in brain cancer detection

Cited by 10 publications

References 48 publications

In vivo detection of murine glioblastoma through Raman and reflectance fiber-probe spectroscopies

In vivo detection of murine glioblastoma through Raman and reflectance fiber-probe spectroscopies

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

A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma

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