Human brain cancer studied by resonance Raman spectroscopy

Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability.

Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma

In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy

BACKGROUND

Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.

METHODS

Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.

RESULTS

Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.

CONCLUSIONS

Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer

Brain cancer is one of those few cancers with very high mortality and low five-year survival rate. First and foremost reason for the woes is the difficulty in diagnosing and monitoring the progression of brain tumors both benign and malignant, noninvasively and in real time. This raises a need in this hour for a tool to diagnose the tumors in the earliest possible time frame. On the other hand, Raman spectroscopy which is well-known for its ability to precisely represent the molecular markers available in any sample given, including biological ones, with great sensitivity and specificity. This has led to a number of studies where Raman spectroscopy has been used in brain tumors in various ways. This review article highlights the fundamentals of Raman spectroscopy and its types including conventional Raman, SERS, SORS, SRS, CARS, etc. are used in brain tumors for diagnostics, monitoring, and even theragnostics, collating all the major works in the area. Also, the review explores how Raman spectroscopy can be even more effectively used in theragnostics and the clinical level which would make them a one-stop solution for all brain cancer needs in the future.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

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

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.

Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy

BACKGROUND

Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression. Raman spectroscopy (RS), a vibrational spectroscopic label-free technique, has already shown promise in cancer diagnostics.

METHODS

RS was combined with machine learning to discriminate glioma grades. Raman spectral signatures of glycosylation patterns were used in serum samples and fixed tissue biopsy samples, as well as in single cells and spheroids.

RESULTS

Glioma grades in fixed tissue patient samples and serum were discriminated with high accuracy. Discrimination between higher malignant glioma grades (III and IV) was achieved with high accuracy in tissue, serum, and cellular models using single cells and spheroids. Biomolecular changes were assigned to alterations in glycosylation corroborated by analysing glycan standards and other changes such as carotenoid antioxidant content.

CONCLUSION

RS combined with machine learning could pave the way for more objective and less invasive grading of glioma patients, serving as a useful tool to facilitate glioma diagnosis and delineate biomolecular glioma progression changes.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm^-1, 1004 cm^-1, 1449 cm^-1, 1089-1128 cm^-1, 1311-1367 cm^-1 and 1585-1665 cm^-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm^-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

Impact of preprocessing methods on the Raman spectra of brain tissue

Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.

Holistic Analysis of Glioblastoma Stem Cell DNA Using Nanoengineered Plasmonic Metasensor for Glioblastoma Diagnosis

The clinical relevance of liquid biopsy for glioblastoma (GBM) remains undetermined due to practical and biological limitations such as absence of a reliable GBM-specific biomarker, trace levels in circulation due to the blood-brain-barrier, and lack of a sensitive method to detect the trace levels of biomarkers. It is hypothesized that GBM stem cell (GSC)-associated cell free DNA can function as reliable biomarker for GBM because it accounts for tumor heterogeneity and provide accurate molecular information about the cancer. An integrative methodology is used for GBM diagnosis by utilizing the sub-single molecular sensitivity of nanoengineered plasmonic metasensors for real-time genomic profiling of GSC DNA. The nanoengineered metasensors can detect the rare circulating GSC-DNA accurately from just 5 µL of blood and the test can be performed in under 10 min. Analysis of clinical serum samples from GBM patients and healthy volunteers demonstrates that the technology yielded an accurate classification of GBM in an independent validation cohort (accuracy 98.3%, specificity 100%). The methodology detects GBM-signatures from the patient blood rapidly within the half-life period of cfDNA in circulation, non-invasively and amplification-free with a high diagnostic accuracy. With clinical validation, this methodology can evolve as a clinically viable diagnostic tool for fatal and hard-to-detect cancer like GBM.

Superparamagnetic MoS2@Fe3O4 nanoflowers for rapid resonance-Raman scattering biodetection

Sensors for rapid and reliable detection of biomolecules are crucial for clinical medical diagnoses. Here, a rapid, ultra-sensitive, magnetic-assisted biosensor based on resonance Raman scattering at MoS2@Fe3O4 composite nanoflowers is presented. Raman shifts and X-ray photoelectron spectra indicated that the composite was formed via Fe-S covalent bonds. Convenient magnetic separations could be performed because of the superparamagnetic Fe3O4 nanoparticles. MoS2 E12g and A1g Raman peaks were used as probe signals for anti-interference immunoassays. The probe unit of the immunoassay also included goat anti-human IgG molecules that were used as the target analyte. Au substrates coupled with the goat anti-human IgG were used as capture units to form sandwich biosensors. Because of the magnetic enrichment, the detection limit was improved by three orders-of-magnitude and the detection time was reduced from 1.5 h to 1 min. Sandwich biosensors using MoS2@Fe3O4 nanoflowers as Raman probes could be very promising sensors for proteins, antigens, and other immunogenic biopolymers, as well as for corpuscular viruses and cells.

Ex vivo confocal Raman microspectroscopy of porcine dura mater supported by optical clearing

The effect of tissue optical clearing (TOC) to increase the probing depth and observe in-depth structure of the ex vivo porcine dura mater was studied by confocal Raman microspectroscopy (CRM). Raman intensities were significantly increased at the depth of 250 μm for all collagen bands after treatment with glycerol. The influence of glycerol on collagen hydration was also investigated. The results indicate that the process of TOC can be divided into three main steps. The first one is a fast process of tissue dehydration accompanied by collagen shrinkage while the second relatively slow process is related to the glycerol penetration into the interfibrillar space of collagen combined with swelling of tissue. The third step is collagen dissociation caused by the high concentration of glycerol. To the best of our knowledge, this study is the first example to introduce the TOC technique in assisting CRM of ex vivo dura mater in-depth probing.

Assessing Nordihydroguaiaretic Acid Therapeutic Effect for Glioblastoma Multiforme

In this study, we demonstrate that Raman microscopy combined with computational analysis is a useful approach to discriminating accurately between brain tumor bio-specimens and to identifying structural changes in glioblastoma (GBM) bio-signatures after nordihydroguaiaretic acid (NDGA) administration. NDGA phenolic lignan was selected as a potential therapeutic agent because of its reported beneficial effects in alleviating and inhibiting the formation of multi-organ malignant tumors. The current analysis of NDGA's impact on GBM human cells demonstrates a reduction in the quantity of altered protein content and of reactive oxygen species (ROS)-damaged phenylalanine; results that correlate with the ROS scavenger and anti-oxidant properties of NDGA. A novel outcome presented here is the use of phenylalanine as a biomarker for differentiating between samples and assessing drug efficacy. Treatment with a low NDGA dose shows a decline in abnormal lipid-protein metabolism, which is inferred by the formation of lipid droplets and a decrease in altered protein content. A very high dose results in cell structural and membrane damage that favors transformed protein overexpression. The information gained through this work is of substantial value for understanding NDGA's beneficial as well as detrimental bio-effects as a potential therapeutic drug for brain cancer.

Use of Handheld Raman Spectroscopy for Intraoperative Differentiation of Normal Brain Tissue From Intracranial Neoplasms in Dogs

The aim of this study was to assess feasibility and accuracy of a hand-held, intraoperative Raman spectroscopy device as a neuronavigation aid to accurately detect neoplastic tissue from adjacent normal gray and white matter. Although Raman spectra are complicated fingerprints of cell signature, the relative shift corresponding to lipid and protein content (2,845 and 2,930 cm−1, respectively), can provide a rapid assessment of whether tissue is normal white or gray matter vs. neoplasia for real-time guidance of tumor resection. Thirteen client-owned dogs were initially enrolled in the study. Two were excluded from final analysis due to incomplete data acquisition or lack of neoplastic disease. The diagnoses of the remaining 11 dogs included six meningiomas, two histiocytic sarcomas, and three gliomas. Intraoperatively, interrogated tissues included normal gray and/or white matter and tumor. A total of five Raman spectra readings were recorded from the interrogated tissues, and samples were submitted for confirmation of Raman spectra by histopathology. A resultant total of 24 samples, 13 from neoplastic tissue and 11 from normal gray or white matter, were used to calculate sensitivity and specificity of Raman spectra compared to histopathology. The handheld Raman spectroscopy device had sensitivity of 85.7% and specificity of 90% with a positive predictive value of 92.3% and negative predictive value of 81.6%. The Raman device was feasible to use intraoperatively with rapid interpretation of spectra. Raman spectroscopy may be useful for intraoperative guidance of tumor resection.

Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy

Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm $$\end{document}± 0.03% and a specificity of 95.44 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pm $$\end{document}± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.

Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization

Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches

Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.

Intraoperative detection of human meningioma using a handheld visible resonance Raman analyzer

To report for the first time the preliminary results for the evaluation of a VRR-LRR™ analyzer based on visible resonance Raman technique to identify human meningioma grades and margins intraoperatively. Unprocessed primary and recurrent solid human meningeal tissues were collected from 33 patients and underwent Raman analysis during surgeries. A total of 1180 VRR spectra were acquired from fresh solid tissues using a VRR-LRR™ analyzer. A confocal HR Evolution (HORIBA, France SAS) Raman system with 532-nm excitation wavelength was also used to collect data for part of the ex vivo samples after they were thawed from - 80 °C for comparison. The preliminary analysis led to the following observations. (1) The intensity ratio of VRR peaks of protein to fatty acid (I₂₉₃₄/I₂₈₈₈) decreased with the increase of meningioma grade. (2) The ratio of VRR peaks of phosphorylated protein to amid I (I₁₅₈₈/I₁₆₃₉) decreased for the higher grade of meningioma. (3) Three RR vibration modes at 1378, 3174, and 3224 cm^-1 which were related to the molecular vibrational bands of oxy-hemeprotein, amide B, and amide A protein significantly changed in peak intensities in the two types of meningioma tissues compared to normal tissue. (4) The changes in the intensities of VRR modes of carotenoids at 1156 and 1524 cm^-1 were also found in the meningioma boundary. The VRR-LRR™ analyzer demonstrates a new approach for label-free, rapid, and objective identification of primary human meningioma in quasi-clinical settings. The accuracy for detecting meningioma tissues using support vector machines (SVMs) was over 70% based on Raman peaks of key biomolecules and up to 100% using principal component analysis (PCA).

Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples

Identifying tumor cells infiltrating normal-appearing brain tissue is critical to achieve a total glioma resection. Raman spectroscopy (RS) is an optical technique with potential for real-time glioma detection. Most RS reports are based on formalin-fixed or frozen samples, with only a few studies deployed on fresh untreated tissue. We aimed to probe RS on untreated brain biopsies exploring novel Raman bands useful in distinguishing glioma and normal brain tissue. Sixty-three fresh tissue biopsies were analyzed within few minutes after resection. A total of 3450 spectra were collected, with 1377 labelled as Healthy and 2073 as Tumor. Machine learning methods were used to classify spectra compared to the histo-pathological standard. The algorithms extracted information from 60 different Raman peaks identified as the most representative among 135 peaks screened. We were able to distinguish between tumor and healthy brain tissue with accuracy and precision of 83% and 82%, respectively. We identified 19 new Raman shifts with known biological significance. Raman spectroscopy was effective and accurate in discriminating glioma tissue from healthy brain ex-vivo in fresh samples. This study added new spectroscopic data that can contribute to further develop Raman Spectroscopy as an intraoperative tool for in-vivo glioma detection.

Rapid Intraoperative Diagnosis of Meningiomas using Stimulated Raman Histology

BACKGROUND

Frozen section is a time- and labor-intensive method for intraoperative pathologic diagnosis. As a result, there exists a need to expedite and streamline the acquisition and interpretation of diagnostic histologic data to inform surgical decision making. Stimulated Raman histology (SRH) is an emerging technology that may serve to expedite the acquisition and interpretation of histologic data in the operating room.

METHODS

A blinded, prospective cohort study of 82 patients undergoing resection for tumors of the central nervous system was performed. Twenty-six patients with diagnoses of meningioma on SRH, frozen, or permanent section were included in this subanalysis. Diagnostic time and accuracy of stimulated SRH histology images were compared with the gold standard (frozen section). Agreement of SRH and frozen section diagnosis with permanent section (true) diagnosis was also compared.

RESULTS

Mean time-to-diagnosis was significantly shorter for SRH-mediated diagnosis compared with frozen section (9.2 vs. 35.8, P < 0.0001). Diagnostic accuracy was not significantly different between methods (P = 0.15). Diagnostic agreement was not significantly different between SRH versus frozen, SRH versus permanent, or frozen versus permanent section methods (P = 0.5, P = 0.5, P = 1.00).

CONCLUSIONS

SRH is a promising adjuvant technology that may expedite intraoperative neuropathologic consult without sacrificing diagnostic accuracy.

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.

Use of Raman spectroscopy to evaluate the biochemical composition of normal and tumoral human brain tissues for diagnosis

Raman spectroscopy was used to identify biochemical differences in normal brain tissue (cerebellum and meninges) compared to tumors (glioblastoma, medulloblastoma, schwannoma, and meningioma) through biochemical information obtained from the samples. A total of 263 spectra were obtained from fragments of the normal cerebellum (65), normal meninges (69), glioblastoma (28), schwannoma (8), medulloblastoma (19), and meningioma (74), which were collected using the dispersive Raman spectrometer (830 nm, near infrared, output power of 350 mW, 20 s exposure time to obtain the spectra), coupled to a Raman probe. A spectral model based on least squares fitting was developed to estimate the biochemical concentration of 16 biochemical compounds present in brain tissue, among those that most characterized brain tissue spectra, such as linolenic acid, triolein, cholesterol, sphingomyelin, phosphatidylcholine, β-carotene, collagen, phenylalanine, DNA, glucose, and blood. From the biochemical information, the classification of the spectra in the normal and tumor groups was conducted according to the type of brain tumor and corresponding normal tissue. The classification used in discrimination models were (a) the concentrations of the biochemical constituents of the brain, through linear discriminant analysis (LDA), and (b) the tissue spectra, through the discrimination by partial least squares (PLS-DA) regression. The models obtained 93.3% discrimination accuracy through the LDA between the normal and tumor groups of the cerebellum separated according to the concentration of biochemical constituents and 94.1% in the discrimination by PLS-DA using the whole spectrum. The results obtained demonstrated that the Raman technique is a promising tool to differentiate concentrations of biochemical compounds present in brain tissues, both normal and tumor. The concentrations estimated by the biochemical model and all the information contained in the Raman spectra were both able to classify the pathological groups.

Empirical study on the effects of acquisition parameters for FTIR hyperspectral imaging of brain tissue

Fourier transform infrared (FTIR) spectroscopic imaging is a powerful technique for molecular imaging of pathologies associated with the nervous systems including multiple sclerosis research. However, there is no standard methodology or standardized protocol for FTIR imaging of tissue sections that maximize the ability to discriminate between the molecular, white and granular layers, which is essential in the investigation of the mechanism of demyelination process. Tissue sections are heterogeneous, complex and delicate, hence the parameters to generate high quality images in minimal time becomes essential in the modern clinical laboratory. This article presents an FTIR spectroscopic imaging study of post-mortem human brain tissue testing the effects of various measurement parameters and data analysis methods on image quality and acquisition time. Hyperspectral images acquired from the same region of a tissue using a range of the most common optical and collection parameters in different combinations were compared. These included magnification (4× and 15×), number of co-added scans (1, 4, 8, 16, 32, 64 and 128 scans) and spectral resolution (4, 8 and 16 cm-1). Images were compared in terms of acquisition time, signal-to-noise (S/N) ratio, and accuracy of the discrimination between three major tissue types in a section from the cerebellum (white matter, granular and molecular layers). In the latter case, unsupervised k-means cluster (KMC) analysis was employed to generate images from the hyperspectral images, which were compared to a reference image. The classification accuracy for tissue class discrimination was highest for the 4× magnifying objective, with 4 cm-1 spectral resolution and 128 co-added scans. The 15× magnifying objective gave the best accuracy for a spectral resolution of 4 cm-1 and 64 scans (96.3%), which was just above what was achieved using the 4× magnifying objective, with 4 cm-1 spectral resolution and 32 and 64 co-added scans (95.4 and 95.6%, respectively). These findings were correlated with a decrease in S/N ratio with increasing number of scans and was generally lower for the 15× objective. However, longer scan times were required using the 15× magnifying objective, which did not justify the very small improvement in the classification of tissue types.

Early label-free analysis of mitochondrial redox states by Raman spectroscopy predicts septic outcomes

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Resonance Raman spectroscopy was applied to in vivo detection of the mitochondrial redox state in septic mice for the first time.

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Monitoring mitochondrial redox states using resonance Raman spectroscopy had higher prognostic accuracy for mortality than the lactate level during sepsis and could be a novel diagnostic marker for predicting septic outcomes at an early time point.

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Resonance Raman spectroscopy could detect mitochondrial dysfunction in sepsis and provide a biomarker that can be a specific target of adjunctive treatment.

Background

Sepsis remains an unacceptably high mortality due to the lack of biomarkers for predicting septic outcomes in the early period. Mitochondrial redox states play a pivotal role in this condition and are disturbed early in the development of sepsis. Here, we hypothesized that visualizing mitochondrial redox states via resonance Raman spectroscopy (RRS) could identify septic outcomes at an early time point. Sepsis was induced by cecal ligation and puncture (CLP). We applied RRS analysis at baseline and 30 min, 1 h, 2 h, 4 h, and 6 h after CLP, and the mitochondrial redox states were identified. The levels of blood lactate as a predictor in sepsis were assessed. Our study is the first to reveal the possibility of in vivo detection of the mitochondrial redox state by using RRS in septic mice. The peak area for the Raman reduced mitochondrial fraction, the indicator of mitochondrial redox states, fluctuated significantly at 2 h after CLP. This fluctuation occurred earlier than the change in lactate level. Moreover, this fluctuation had higher prognostic accuracy for mortality than the lactate level during sepsis and could be a novel diagnostic marker for predicting septic outcomes according to the cutoff value of 1.059, which had a sensitivity of 80% and a specificity of 90%.

Objectives

To explore an effective indicator concerning mitochondrial redox states in the early stage of sepsis and to predict septic outcomes accurately in vivo using non-invasive and label-free Resonance Raman spectroscopy (RRS) analysis.

Methods

Mitochondria, primary skeletal muscle cells andex-vivo muscles harvested from gastrocnemius were detected mitochondrial redox states respectively by using RRS. Sepsis was induced by cecal ligation and puncture (CLP). We applied RRS analysis at baseline and 30 min, 1 h, 2 h, 4 h, and 6 h after CLP, and the mitochondrial redox states were identified. The levels of blood lactate as a predictor in sepsis were assessed. The predictive correlation of mitochondrial redox states on mortality, inflammation and organ dysfunction was further assessed.

Results

Mitochondrial redox states were clearly recognized in ex-vivo gastrocnemius muscles as well as purified mitochondria and primary skeletal muscle cells by using RRS. The peak area for the Raman reduced mitochondrial fraction, the indicator of mitochondrial redox states, fluctuated significantly at 2 h after CLP. This fluctuation occurred earlier than the change in lactate level. Moreover, this fluctuation had higher prognostic accuracy for mortality than the lactate level during sepsis and could be a novel diagnostic marker for predicting septic outcomes according to the cutoff value of 1.059, which had a sensitivity of 80% and a specificity of 90%.

Conclusions

This study demonstrated that monitoring mitochondrial redox states using RRS as early as 2 h could indicate outcomes in septic mice. These data may contribute to developing a non-invasive clinical device concerning mitochondrial redox states by using bedside-RRS.

Distinguishing metastatic triple-negative breast cancer from nonmetastatic breast cancer using second harmonic generation imaging and resonance Raman spectroscopy

Triple-negative breast cancer (TNBC) is an aggressive subset of breast cancer that is more common in African-American and Hispanic women. Early detection followed by intensive treatment is critical to improving poor survival rates. The current standard to diagnose TNBC from histopathology of biopsy samples is invasive and time-consuming. Imaging methods such as mammography and magnetic resonance (MR) imaging, while covering the entire breast, lack the spatial resolution and specificity to capture the molecular features that identify TNBC. Two nonlinear optical modalities of second harmonic generation (SHG) imaging of collagen, and resonance Raman spectroscopy (RRS) potentially offer novel rapid, label-free detection of molecular and morphological features that characterize cancerous breast tissue at subcellular resolution. In this study, we first applied MR methods to measure the whole-tumor characteristics of metastatic TNBC (4T1) and nonmetastatic estrogen receptor positive breast cancer (67NR) models, including tumor lactate concentration and vascularity. Subsequently, we employed for the first time in vivo SHG imaging of collagen and ex vivo RRS of biomolecules to detect different microenvironmental features of these two tumor models. We achieved high sensitivity and accuracy for discrimination between these two cancer types by quantitative morphometric analysis and nonnegative matrix factorization along with support vector machine. Our study proposes a new method to combine SHG and RRS together as a promising novel photonic and optical method for early detection of TNBC.

Rise of Raman spectroscopy in neurosurgery: a review

SIGNIFICANCE

Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons.

AIM

We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices.

APPROACH

We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting.

RESULTS

The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing.

CONCLUSIONS

The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

A Review of Chinese Raman Spectroscopy Research Over the Past Twenty Years

This paper introduces the major Chinese research groups in the fields of biomedicine, food safety, environmental testing, material research, archaeological and cultural relics, gem identification, forensic science, and other research areas of Raman spectroscopy and combined methods spanning the two decades from 1997 to 2017. Briefly summarized are the research directions and contents of the major Chinese Raman spectroscopy research groups, giving researchers engaged in Raman spectroscopy research a more comprehensive understanding of the state of Chinese Raman spectroscopy research and future development trends to further develop Raman spectroscopy and its applications.

Comparison of continuous wave versus picosecond SRS and the resonance SRS effect

Stimulated Raman scattering (SRS) is a powerful optical technique for probing the vibrational states of molecules in biological tissues and provides greater signal intensities than when using spontaneous Raman scattering. In this study, we examined the use of continuous wave (cw) and picosecond (ps) laser excitations to generate SRS signals in pure methanol, a carotene-methanol solution, acetone, and brain tissue samples. The cw-SRS system, which utilized two cw lasers, produced better signal-to-noise (S/N) than the conventional ps-SRS system, suggesting that the cw-SRS system is an efficient and cost-effective approach for studying SRS in complex systems like the brain. The cw-SRS approach will reduce the size of the SRS system, allowing for stimulated Raman gain/loss microscopy. In addition, we showed that there exists a resonance SRS (RSRS) effect from the carotene-methanol solution and brain tissue samples using cw laser excitations. The RSRS effect will further improve the signal-to-noise and may be utilized as an enhanced, label-free SRS microscopic tool for the study of biological tissues.

Stimulated Raman Histology for Intraoperative Guidance in the Resection of a Recurrent Atypical Spheno-orbital Meningioma: A Case Report and Review of Literature

Meningiomas are the most common intracranial, extra-axial neoplasms and account for a significant proportion of all central nervous system (CNS) tumors. Regardless of the grade, treatment typically involves upfront surgical resection. However, in many instances, especially in meningiomas arising from the skull base, complete removal is often difficult given the close proximity to important anatomic structures. In this report, we discuss the use of stimulated Raman histology as a means to identify tissue boundaries during the resection of an extensive, recurrent, atypical spheno-orbital meningioma.

We report a 75-year-old male with a history of a prior left frontotemporal craniotomy for a grade II meningioma three years prior, who presented with worsening left-sided visual loss and pronounced temporal bossing. Repeat magnetic resonance imaging (MRI) revealed a recurrent left spheno-orbital tumor suggestive of a meningioma extending into the middle cranial fossa, the lateral orbit, and the temporalis muscle. He underwent an extended orbito-pterional craniotomy, and intraoperative stimulated Raman histology aided in the identification of tumor margins within the orbit and the temporalis muscle in order to better preserve the normal orbital contents and muscle bulk of the infratemporal fossa.

This case demonstrates the utility of stimulated Raman histology during the resection of invasive skull base tumors. The immediate intraoperative Raman histologic sections can clearly identify tissue boundaries and thus help preserve important anatomic structures. Continued development of this method can potentially improve the accuracy of intraoperative diagnoses and guide surgeons during tumor resections near eloquent anatomical regions or important normal structures.

Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies

Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We therefore developed a line scan Raman microspectroscopy (LSRM) system to detect, discriminate and hyperspectrally visualize pituitary gland from pituitary adenomas based on molecular differences. By applying principal component analysis followed by a k-nearest neighbor algorithm, specific hormone states were identified and a clear discrimination between pituitary gland and various adenoma subtypes was achieved. The classifier yielded an accuracy of 95% for gland tissue and 84–99% for adenoma subtypes. With an overall accuracy of 92%, our LSRM system has proven its potential to differentiate pituitary gland from pituitary adenomas. LSRM images based on the presence of specific Raman bands were created, and such images provided additional insight into the spatial distribution of particular molecular compounds. Pathological states could be molecularly differentiated and characterized with texture analysis evaluating Grey Level Cooccurrence Matrices for each LSRM image, as well as correlation coefficients between LSRM images.

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.

Multimodal quantitative imaging of brain cancer in cultured cells

Fluorescence emission, polarization and subcellular localization of methylene blue (MB) were studied in four cancerous and two normal human brain cell lines. Fluorescence emission and polarization images were acquired and analyzed. The co-localization of MB with mitochondria, lysosomes and nuclei of the cells was evaluated. Glioblastoma cells exhibited significantly higher MB fluorescence polarization compared to normal astrocytes. Preferential accumulation of MB in mitochondria of glioblastoma cells may explain higher fluorescence polarization values in cancer cells as compared to normal. These findings may lead to the development of a quantitative method for the detection of brain cancer in single cells.

Advances in Raman imaging combined with AFM and fluorescence microscopy are beneficial for oncology and cancer research

Aim: The aim of this paper is to provide images of the universal cancer Raman biomarkers based on lipidomic, proteomic, glycomic profiles and nanomechanical properties. Materials & methods: Biochemical mapping and nanomechanical properties (topography, stiffness and adhesion) of human breast and brain for normal and cancer tissues and cell culture line U87 MG of glioblastoma were obtained using Raman imaging combined with atomic force microscopy (AFM) and fluorescence microscopy. Results & conclusion: Detailed analysis of breast ductal carcinoma in situ, and astrocytoma brain tissues as well as cells of glioblastoma U87 MG showed that Raman scattering generates images as accurately as histology hematoxylin and eosin stain used in clinical practice with additional advantage of biochemical information. Combination of AFM maps and Raman images allows to correlate mechanical properties with biochemical composition of cells.

The Use of Spectroscopy Handheld Tools in Brain Tumor Surgery: Current Evidence and Techniques

The fundamental principle in the operative treatment of brain tumors involves achieving maximal safe resection in order to improve postoperative outcomes. At present, challenges in visualizing microscopic disease and residual tumor remain an impediment to complete tumor removal. Spectroscopic tools have the theoretical advantage of accurate tissue identification, coupled with the potential for manual intraoperative adjustments to improve visualization of remaining tumor tissue that would otherwise be difficult to detect. The current evidence and techniques for handheld spectroscopic tools in surgical neuro-oncology are explored here.

Determination of meningioma brain tumour grades using Raman microspectroscopy imaging

Raman microspectroscopy imaging was used to distinguish 90 brain tissue samples into meningiomas Grade I and Grade II.

Accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue

Raman spectroscopy could be applied to distinguish tumor from normal tissues. This meta-analysis was conducted to assess the accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue. PubMed and Embase were searched to identify suitable studies prior to Jan 1st, 2016. We estimated the pooled sensitivity, specificity, positive and negative likelihood ratios (LR), diagnostic odds ratio (DOR), and constructed summary receiver operating characteristics (SROC) curves to identity the accuracy of Raman spectroscopy in differentiating brain tumor from normal brain tissue. A total of six studies with 1951 spectra were included. For glioma, the pooled sensitivity and specificity of Raman spectroscopy were 0.96 (95% CI 0.94-0.97) and 0.99 (95% CI 0.98-0.99), respectively. The area under the curve (AUC) was 0.9831. For meningioma, the pooled sensitivity and specificity were 0.98 (95% CI 0.94-1.00) and 1.00 (95% CI 0.98-1.00), respectively. The AUC was 0.9955. This meta-analysis suggested that Raman spectroscopy could be an effective and accurate tool for differentiating glioma and meningioma from normal brain tissue, which would help us both avoid removal of normal tissue and minimize the volume of residual tumor.

Epileptic rat brain tissue analyzed by 2D correlation Raman spectroscopy

Absence epilepsy is the neurological disorder characterized by the pathological spike-and wave discharges present in the electroencephalogram, accompanying a sudden loss of consciousness. Experiments were performed on brain slices obtained from young male WAG/Rij rats (2-3weeks old), so that they were sampled before the appearance of brain-damaging seizures symptoms. Two differing brain areas of the rats' brain tissue were studied: the somatosensory cortex (Sc) and the dorsal lateral geniculate nucleus of the thalamus (DLG). The Raman spectra of the fresh brain scraps, kept during measurements in artificial cerebrospinal fluid, were collected using as an excitation source 442nm, 514.5nm, 785nm and 1064nm laser line. The average spectra were analyzed by 2D correlation method regarding laser line as an external perturbation. In 2D synchronous spectra positive auto-peaks corresponding to the CC stretching and amide I band vibrations show maxima at 1660cm^-1 and 1662cm^-1 for Sc and DLG, respectively. The prominent auto-peak at 2937cm^-1, originated from the CH₃ mode in DLG brain area, seems to indicate the importance of methylation, considered to be significant in epileptogenesis. Synchronous and asynchronous correlations peaks, glutamic acid and gamma-aminobutyric acid (GABA), appear in Sc and DLG, respectively. In the 1730-1600cm^-1 range occur cross-peaks which appearance might be triggered by glial fibrillary acidic protein (GFAP) activation.

Raman Spectroscopy Applied to Parathyroid Tissues: A New Diagnostic Tool to Discriminate Normal Tissue from Adenoma

Primary hyperparathyroidism is an endocrine disorder characterized by autonomous production of parathyroid hormone. Patients with the symptomatic disease should be referred for parathyroidectomy. However, the distinction between the pathological condition and the benign one is very challenging in the surgical setting; therefore, accurate recognition is important to ensure success during minimally invasive surgery. At present, all intraoperative techniques significantly increase surgical time and, consequently, cost. In this proof-of-concept study, Raman microscopy was used to differentiate between healthy parathyroid tissue and parathyroid adenoma from 18 patients. The data showed different spectroscopic features for the two main tissue types of healthy and adenoma. Moreover, the parathyroid adenoma subtypes (chief cells and oxyphil cells) were characterized by their own Raman spectra. The partial least-squares discriminant analysis (PLS-DA) model built to discriminate healthy from adenomatous parathyroid tissue was able to correctly classify all samples in the calibration and validation data sets, providing 100% prediction accuracy. The PLS-DA model built to discriminate chief cell adenoma from oxyphil cell adenoma allowed us to correctly classify >99% of the spectra during calibration and cross-validation and to correctly predict 100% of oxyphil and 99.8% of chief cells in the external validation data set. The results clearly demonstrate the great potential of Raman spectroscopy. The final goal would be development of a Raman portable fiber probe device for intraoperative optical biopsy, both to improve the surgical success rate and reduce surgical cost.

Intraoperative Raman Spectroscopy

Surgical excision of brain tumors provides a means of cytoreduction and diagnosis while minimizing neurologic deficit and improving overall survival. Despite advances in functional and three-dimensional stereotactic navigation and intraoperative MRI, delineating tissue in real time with physiologic confirmation is challenging. Raman spectroscopy has potential to be an important modality in the intraoperative evaluation of tissue during surgical resection. In vitro experimental studies have shown that this technique can be used to differentiate normal brain tissue from tissue with infiltrating cancer cells and dense cancerous masses with high specificity, indicating the feasibility of this method for in vivo application.

Novel strategies of Raman imaging for brain tumor research

Raman diagnostics and imaging have been shown to be an effective tool for the analysis and discrimination of human brain tumors from normal structures. Raman spectroscopic methods have potential to be applied in clinical practice as they allow for identification of tumor margins during surgery. In this study, we investigate medulloblastoma (grade IV WHO) (n= 5), low-grade astrocytoma (grades I-II WHO) (n =4), ependymoma (n=3) and metastatic brain tumors (n= 1) and the tissue from the negative margins used as normal controls. We compare a high grade medulloblastoma, low grade astrocytoma and non-tumor samples from human central nervous system (CNS) tissue. Based on the properties of the Raman vibrational features and Raman images we provide a real–time feedback method that is label-free to monitor tumor metabolism that reveals reprogramming of biosynthesis of lipids, proteins, DNA and RNA. Our results indicate marked metabolic differences between low and high grade brain tumors. We discuss molecular mechanisms causing these metabolic changes, particularly lipid alterations in malignant medulloblastoma and low grade gliomas that may shed light on the mechanisms driving tumor recurrence thereby revealing new approaches for the treatment of malignant glioma. We have found that the high-grade tumors of central nervous system (medulloblastoma) exhibit enhanced level of β-sheet conformation and down-regulated level of α-helix conformation when comparing against normal tissue. We have found that almost all tumors studied in the paper have increased Raman signals of nucleic acids. This increase can be interpreted as increased DNA/RNA turnover in brain tumors. We have shown that the ratio of Raman intensities I₂₉₃₀/I₂₈₄₅ at 2930 and 2845 cm^-1 is a good source of information on the ratio of lipid and protein contents. We have found that the ratio reflects the different lipid and protein contents of cancerous brain tissue compared to the non-tumor tissue. We found that levels of the saturated fatty acids were significantly reduced in the high grade medulloblastoma samples compared with non-tumor brain samples and low grade astrocytoma. Differences were also noted in the n-6/n-3 polyunsaturated fatty acids (PUFA) content between medulloblastoma and non-tumor brain samples. The content of the oleic acid (OA) was significantly smaller in almost all brain high grade brain tumors than that observed in the control samples. It indicates that the fatty acid composition of human brain tumors differs from that found in non-tumor brain tissue. The iodine number N_I for the normal brain tissue is 60. For comparison OA has 87, docosahexaenoic acid (DHA) 464, α-linolenic acid (ALA) 274. The high grade tumors have the iodine numbers between that for palmitic acid, stearic acid, arachidic acid (N_I=0) and oleic acid (N_I=87). Most low grade tumors have N_I similar to that of OA. The iodine number for arachidonic acid (AA) (N_I=334) is much higher than those observed for all studied samples.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

Raman technologies in cancer diagnostics

Despite significant effort, cancer still remains a leading cause of death worldwide. In order to reduce its burden, the development and improvement of noninvasive strategies for early detection and diagnosis of cancer are urgently needed. Raman spectroscopy, an optical technique that relies on inelastic light scattering arising from molecular vibrations, is one such strategy, as it can noninvasively probe cancerous markers using only endogenous contrast. In this review, spontaneous, coherent and surface enhanced Raman spectroscopies and imaging, as well as the fundamental principles governing the successful use of these techniques, are discussed. Methods for spectral data analysis are also highlighted. Utilization of the discussed Raman techniques for the detection and diagnosis of cancer in vitro, ex vivo and in vivo is described. The review concludes with a discussion of the future directions of Raman technologies, with particular emphasis on their clinical translation.

Optical technologies for intraoperative neurosurgical guidance

Biomedical optics is a broadly interdisciplinary field at the interface of optical engineering, biophysics, computer science, medicine, biology, and chemistry, helping us understand light-tissue interactions to create applications with diagnostic and therapeutic value in medicine. Implementation of biomedical optics tools and principles has had a notable scientific and clinical resurgence in recent years in the neurosurgical community. This is in great part due to work in fluorescence-guided surgery of brain tumors leading to reports of significant improvement in maximizing the rates of gross-total resection. Multiple additional optical technologies have been implemented clinically, including diffuse reflectance spectroscopy and imaging, optical coherence tomography, Raman spectroscopy and imaging, and advanced quantitative methods, including quantitative fluorescence and lifetime imaging. Here we present a clinically relevant and technologically informed overview and discussion of some of the major clinical implementations of optical technologies as intraoperative guidance tools in neurosurgery.