Rise of Raman spectroscopy in neurosurgery: a review

Vibrational fiber photometry: label-free and reporter-free minimally invasive Raman spectroscopy deep in the mouse brain

Optical approaches to monitor neural activity are transforming neuroscience, owing to a fast-evolving palette of genetically encoded molecular reporters. However, the field still requires robust and label-free technologies to monitor the multifaceted biomolecular changes accompanying brain development, aging or disease. Here, we have developed vibrational fiber photometry as a low-invasive method for label-free monitoring of the biomolecular content of arbitrarily deep regions of the mouse brain in vivo through spontaneous Raman spectroscopy. Using a tapered fiber probe as thin as 1 µm at its tip, we elucidate the cytoarchitecture of the mouse brain, monitor molecular alterations caused by traumatic brain injury, as well as detect markers of brain metastasis with high accuracy. We view our approach, which introduces a deep learning algorithm to suppress probe background, as a promising complement to the existing palette of tools for the optical interrogation of neural function, with application to fundamental and preclinical investigations of the brain and other organs.

Artificial Intelligence-Assisted Stimulated Raman Histology: New Frontiers in Vibrational Tissue Imaging

Frozen section biopsy, introduced in the early 1900s, still remains the gold standard methodology for rapid histologic evaluations. Although a valuable tool, it is labor-, time-, and cost-intensive. Other challenges include visual and diagnostic variability, which may complicate interpretation and potentially compromise the quality of clinical decisions. Raman spectroscopy, with its high specificity and non-invasive nature, can be an effective tool for dependable and quick histopathology. The most promising modality in this context is stimulated Raman histology (SRH), a label-free, non-linear optical process which generates conventional H&E-like images in short time frames. SRH overcomes limitations of conventional Raman scattering by leveraging the qualities of stimulated Raman scattering (SRS), wherein the energy gets transferred from a high-power pump beam to a probe beam, resulting in high-energy, high-intensity scattering. SRH's high resolution and non-requirement of preprocessing steps make it particularly suitable when it comes to intrasurgical histology. Combining SRH with artificial intelligence (AI) can lead to greater precision and less reliance on manual interpretation, potentially easing the burden of the overburdened global histopathology workforce. We review the recent applications and advances in SRH and how it is tapping into AI to evolve as a revolutionary tool for rapid histologic analysis.

Current Applications of Raman Spectroscopy in Intraoperative Neurosurgery

BACKGROUND

Neurosurgery demands exceptional precision due to the brain's complex and delicate structures, necessitating precise targeting of pathological targets. Achieving optimal outcomes depends on the surgeon's ability to accurately differentiate between healthy and pathological tissues during operations. Raman spectroscopy (RS) has emerged as a promising innovation, offering real-time, in vivo non-invasive biochemical tissue characterization. This literature review evaluates the current research on RS applications in intraoperative neurosurgery, emphasizing its potential to enhance surgical precision and patient outcomes.

METHODS

Following PRISMA guidelines, a comprehensive systematic review was conducted using PubMed to extract relevant peer-reviewed articles. The inclusion criteria focused on original research discussing real-time RS applications with human tissue samples in or near the operating room, excluding retrospective studies, reviews, non-human research, and other non-relevant publications.

RESULTS

Our findings demonstrate that RS significantly improves tumor margin delineation, with handheld devices achieving high sensitivity and specificity. Stimulated Raman Histology (SRH) provides rapid, high-resolution tissue images comparable to traditional histopathology but with reduced time to diagnosis. Additionally, RS shows promise in identifying tumor types and grades, aiding precise surgical decision-making. RS techniques have been particularly beneficial in enhancing the accuracy of glioma surgeries, where distinguishing between tumor and healthy tissue is critical. By providing real-time molecular data, RS aids neurosurgeons in maximizing the extent of resection (EOR) while minimizing damage to normal brain tissue, potentially improving patient outcomes and reducing recurrence rates.

CONCLUSIONS

This review underscores the transformative potential of RS in neurosurgery, advocating for continued innovation and research to fully realize its benefits. Despite its substantial potential, further research is needed to validate RS's clinical utility and cost-effectiveness.

The application of vibrational spectroscopy in forensic analysis of biological evidence

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Optical Methods for Brain Tumor Detection: A Systematic Review

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.

Optical fiber biosensors toward in vivo detection

This review takes stock of the various optical fiber-based biosensors that could be used for in vivo applications. We discuss the characteristics that biosensors must have to be suitable for such applications and the corresponding transduction modes. In particular, we focus on optical fiber biosensors based on fluorescence, evanescent wave, plasmonics, interferometry, and Raman phenomenon. The operational principles, implemented solutions, and performances are described and debated. The different sensing configurations, such as the side- and tip-based fiber biosensors, are illustrated, and their adaptation for in vivo measurements is discussed. The required implementation of multiplexed biosensing on optical fibers is shown. In particular, the use of multi-fiber assemblies, one of the most optimal configurations for multiplexed detection, is discussed. Different possibilities for multiple localized functionalizations on optical fibers are presented. A final section is devoted to the practical in vivo use of fiber-based biosensors, covering regulatory, sterilization, and packaging aspects. Finally, the trends and required improvements in this promising and emerging field are analyzed and discussed.

Achieving Gross Total Resection in Neurosurgery: A Review of Intraoperative Techniques and Their Influence on Surgical Goals

The definition of complete resection in neurosurgery depends on tumor type, surgical aims, and postoperative investigations, directly guiding the choice of intraoperative tools. Most common tumor types present challenges in achieving complete resection due to their infiltrative nature and anatomical constraints. The development of adjuvant treatments has altered the balance between oncological aims and surgical risks. We review local recurrence associated with incomplete resection based on different definitions and emphasize the importance of achieving maximal safe resection in all tumor types. Intraoperative techniques that aid surgeons in identifying tumor boundaries are used in practice and in preclinical or clinical research settings. They encompass both conservative and invasive techniques. Among them, morphological tools include imaging modalities such as intraoperative magnetic resonance imaging, ultrasound, and optical coherence tomography. Fluorescence-guided surgery, mainly using 5-aminolevulinic acid, enhances gross total resection in glioblastomas. Nuclear methods, including positron emission tomography probes, provide tumor detection based on beta or gamma emission after a radiotracer injection. Mass spectrometry- and spectroscopy-based methods offer molecular insights. The adoption of these techniques depends on their relevance, effectiveness, and feasibility. With the emergence of positron emission tomography imaging for use in recurrence benchmarking, positron emission tomography probes raise particular interest among those tools. While all such tools provide valuable insights, their clinical benefits need further evaluation.

Vibrational spectroscopy and multiphoton microscopy for label-free visualization of nervous system degeneration and regeneration

Neurological disorders, including spinal cord injury, peripheral nerve injury, traumatic brain injury, and neurodegenerative diseases, pose significant challenges in terms of diagnosis, treatment, and understanding the underlying pathophysiological processes. Label-free multiphoton microscopy techniques, such as coherent Raman scattering, two-photon excited autofluorescence, and second and third harmonic generation microscopy, have emerged as powerful tools for visualizing nervous tissue with high resolution and without the need for exogenous labels. Coherent Raman scattering processes as well as third harmonic generation enable label-free visualization of myelin sheaths, while their combination with two-photon excited autofluorescence and second harmonic generation allows for a more comprehensive tissue visualization. They have shown promise in assessing the efficacy of therapeutic interventions and may have future applications in clinical diagnostics. In addition to multiphoton microscopy, vibrational spectroscopy methods such as infrared and Raman spectroscopy offer insights into the molecular signatures of injured nervous tissues and hold potential as diagnostic markers. This review summarizes the application of these label-free optical techniques in preclinical models and illustrates their potential in the diagnosis and treatment of neurological disorders with a special focus on injury, degeneration, and regeneration. Furthermore, it addresses current advancements and challenges for bridging the gap between research findings and their practical applications in a clinical setting.

Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases

Raman scattering is an inelastic light scattering that occurs in a manner reflective of the molecular vibrations of molecular structures and chemical conditions in a given sample of interest. Energy changes in the scattered light can be assessed to determine the vibration mode and associated molecular and chemical conditions within the sample, providing a molecular fingerprint suitable for sample identification and characterization. Raman spectroscopy represents a particularly promising approach to the molecular analysis of many diseases owing to clinical advantages including its instantaneous nature and associated high degree of stability, as well as its ability to yield signal outputs corresponding to a single molecule type without any interference from other molecules as a result of its narrow peak width. This technology is thus ideally suited to the simultaneous assessment of multiple analytes. Neurodegenerative diseases represent an increasingly significant threat to global public health owing to progressive population aging, imposing a severe physical and social burden on affected patients who tend to develop cognitive and/or motor deficits beginning between the ages of 50 and 70. Owing to a relatively limited understanding of the etiological basis for these diseases, treatments are lacking for the most common neurodegenerative diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. The present review was formulated with the goal of briefly explaining the principle of Raman spectroscopy and discussing its potential applications in the diagnosis and evaluation of neurodegenerative diseases, with a particular emphasis on the research prospects of this novel technological platform.

Early Detection of Oral Potentially Malignant Disorders: A Review on Prospective Screening Methods with Regard to Global Challenges

Oral cancer is a cancer type that is widely prevalent in low-and middle-income countries with a high mortality rate, and poor quality of life for patients after treatment. Early treatment of cancer increases patient survival, improves quality of life and results in less morbidity and a better prognosis. To reach this goal, early detection of malignancies using technologies that can be used in remote and low resource areas is desirable. Such technologies should be affordable, accurate, and easy to use and interpret. This review surveys different technologies that have the potentials of implementation in primary health and general dental practice, considering global perspectives and with a focus on the population in India, where oral cancer is highly prevalent. The technologies reviewed include both sample-based methods, such as saliva and blood analysis and brush biopsy, and more direct screening of the oral cavity including fluorescence, Raman techniques, and optical coherence tomography. Digitalisation, followed by automated artificial intelligence based analysis, are key elements in facilitating wide access to these technologies, to non-specialist personnel and in rural areas, increasing quality and objectivity of the analysis while simultaneously reducing the labour and need for highly trained specialists.

Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates

OBJECTIVES

Raman spectroscopy as a diagnostic tool for biofluid applications is limited by low inelastic scattering contributions compared to the fluorescence background from biomolecules. Surface-enhanced Raman spectroscopy (SERS) can increase Raman scattering signals, thereby offering the potential to reduce imaging times. We aimed to evaluate the enhancement related to the plasmonic effect and quantify the improvements in terms of spectral quality associated with SERS measurements in human saliva.

METHODS

Dried human saliva was characterized using spontaneous Raman spectroscopy and SERS. A fabrication protocol was implemented leading to the production of silver (Ag) nanopillar substrates by glancing angle deposition. Two different imaging systems were used to interrogate saliva from 161 healthy donors: a custom single-point macroscopic system and a Raman micro-spectroscopy instrument. Quantitative metrics were established to compare spontaneous RS and SERS measurements: the Raman spectroscopy quality factor (QF), the photonic count rate (PR), the signal-to-background ratio (SBR).

RESULTS

SERS measurements acquired with an excitation energy four times smaller than with spontaneous RS resulted in improved QF, PR values an order of magnitude larger and a SBR twice as large. The SERS enhancement reached 100×, depending on which Raman bands were considered.

CONCLUSIONS

Single-point measurement of dried saliva with silver nanopillars substrates led to reproducible SERS measurements, paving the way to real-time tools of diagnosis in human biofluids.

Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning

Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.

Revolutionizing Neurosurgery with GPT-4: A Leap Forward or Ethical Conundrum?

Neurosurgery, a highly specialized and sophisticated branch of medicine, is devoted to the surgical intervention of maladies impacting both the central and peripheral nervous systems. The intricate nature and meticulous precision demanded by neurosurgery has piqued the interest of artificial intelligence experts. In our comprehensive analysis, we encapsulate the prospective applications of the revolutionary GPT-4 technology within the sphere of neurosurgery, encompassing areas such as preoperative evaluation and preparation, tailored surgical simulations, postoperative care and rehabilitation, enriched patient communication, fostering collaboration and knowledge dissemination, as well as training and education. Furthermore, we plunge into the complex and intellectually stimulating conundrums that arise when integrating the cutting-edge GPT-4 technology into neurosurgery, taking into account the moral considerations and substantial hurdles intrinsic to its adoption. Our stance is that GPT-4 will not supplant neurosurgeons; on the contrary, it possesses the potential to serve as an invaluable instrument in augmenting the precision and effectiveness of neurosurgical procedures, ultimately enhancing patient outcomes and propelling the field forward.

Optimizing maximum resection of glioblastoma: Raman spectroscopy versus 5-aminolevulinic acid

OBJECTIVE

The objective of this study was to assess and compare the potential of 5-aminolevulinic acid (5-ALA) and Raman spectroscopy (RS) in detecting tumor-infiltrated brain in patients with glioblastoma (GBM).

METHODS

Between July 2020 and October 2021, the authors conducted a prospective clinical trial with 15 patients who underwent neurosurgical treatment of newly diagnosed and histologically verified GBM. A solid contrast-enhancing tumor core and peritumoral tissue were investigated intraoperatively for cancer cells by using 5-ALA and RS to achieve pathology-tailored maximum resection. In each case, a minimum of 10 biopsies were sampled from navigation-guided areas. Two neuropathologists examined the biopsies for the presence of neoplastic cells. The detection performance of 5-ALA and RS alone and in combination was assessed. Pre- and postoperative MRI, Karnofsky Performance Status (KPS), and National Institutes of Health Stroke Scale (NIHSS) scores were compared, and median progression-free survival (PFS) was evaluated.

RESULTS

A total of 185 biopsy samples were harvested from the contrast-enhancing tumor core (n = 19) and peritumoral tissue (n = 166). In the tumor core, 5-ALA and RS each showed a sensitivity of 100%. In the peritumoral tissue, 5-ALA was less sensitive than RS in detecting cancer (46% vs 69%) but showed higher specificity (81% vs 57%). When the two methods were combined, the accuracy of tumor detection was increased by about 10%. Pathology-tailored resection led to a 52% increase in resection volume comparing the volume of preoperative contrast enhancement with the postoperative resection cavity on MRI (p = 0.0123). Eloquent brain involvement prevented gross-total resection in 4 patients. Four weeks after surgery, mean KPS (p = 0.7637) and NIHSS scores (p = 0.3146) were not significantly different from preoperative values. Of the 13 patients who had received postoperative chemoradiotherapy, 4 did not show any progression after a median follow-up of 14 months. The remaining 9 patients had a median PFS of 8 months.

CONCLUSIONS

According to the study data, RS is capable of detecting tumor-infiltrated brain with higher sensitivity but lower specificity than the current standard of 5-ALA. With further technological and workflow advancements, RS in combination with protoporphyrin IX fluorescence may contribute to pathology-tailored glioma resection in the future.

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

SERS Sensing Using Graphene-Covered Silver Nanoparticles and Metamaterials for the Detection of Thiram in Soil

Multilayer hyperbolic metamaterial (HMM)-based SERS substrates have received special consideration because they accommodate various propagation modes such as surface plasmonic polaritons (SPP). However, the SPP modes are difficult to generate in HMM due to their weak electric field enhancement. In this article, we designed novel SERS substrates consisting of graphene-covered AgNPs and HMM. The graphene-covered AgNPs work as an external coupling structure for hyperbolic metamaterials due to this structure exhibiting significant plasmonic effects as well as unique optical features. The localized surface plasmonic resonance (LSPR) of the graphene-covered AgNPs excited the SPP and thus formed a strong hot spot zone in the nanogap area of the graphene. The Raman experiment was performed using rhodamine 6G (R6G) and crystal violet (CV), which showed high stability and a maximum enhancement factor of 2.12 × 10⁸. The COMSOL simulation further clarified that enhanced SERS performance was due to the presence of monolayer graphene and provided an atomically flat surface for organic molecules in a more controllable manner. Interestingly, the proposed SERS structure carries out quantitative detection of thiram in soil and can satisfy the basic environmental need for pesticide residue in the soil.

Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications

Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.

Innovation in Neurosurgery: Lessons Learned, Obstacles, and Potential Funding Sources

Innovation is central to neurosurgery and has dramatically increased over the last twenty years. Although the specialty innovates as a whole, only 3-4.7% of practicing neurosurgeons hold patents. Various roadblocks to innovation impede this process such as lack of understanding, increasing regulatory complexity, and lack of funding. Newly emerging technologies allow us to understand how to innovate and how to learn from other medical specialties. By further understanding the process of innovation, and the funding that supports it, Neurosurgery can continue to hold innovation as one of its's central tenets.

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.

Confocal Raman Micro-Spectroscopy for Discrimination of Glycerol Diffusivity in Ex Vivo Porcine Dura Mater

Dura mater (DM) is a connective tissue with dense collagen, which is a protective membrane surrounding the human brain. The optical clearing (OC) method was used to make DM more transparent, thereby allowing to increase in-depth investigation by confocal Raman micro-spectroscopy and estimate the diffusivity of 50% glycerol and water migration. Glycerol concentration was obtained, and the diffusion coefficient was calculated, which ranged from 9.6 × 10-6 to 3.0 × 10-5 cm2/s. Collagen-related Raman band intensities were significantly increased for all depths from 50 to 200 µm after treatment. In addition, the changes in water content during OC showed that 50% glycerol induces tissue dehydration. Weakly and strongly bound water types were found to be most concentrated, playing a major role in the glycerol-induced water flux and OC. Results show that OC is an efficient method for controlling the DM optical properties, thereby enhancing the in-depth probing for laser therapy and diagnostics of the brain. DM is a comparable to various collagen-containing tissues and organs, such as sclera of eyes and skin dermis.

Differentiation of glioblastoma tissues using spontaneous Raman scattering with dimensionality reduction and data classification

The neurosurgery of intracranial tumors is often complicated by the difficulty of distinguishing tumor center, infiltration area, and normal tissue. The current standard for intraoperative navigation is fluorescent diagnostics with a fluorescent agent. This approach can be further enhanced by measuring the Raman spectrum of the tissue, which would provide additional information on its composition even in the absence of fluorescence. However, for the Raman spectra to be immediately helpful for a neurosurgeon, they must be additionally processed. In this work, we analyzed the Raman spectra of human brain glioblastoma multiforme tissue samples obtained during the surgery and investigated several approaches to dimensionality reduction and data classificatin to distinguish different types of tissues. In our study two approaches to Raman spectra dimensionality reduction were approbated and as a result we formulated new technique combining both of them: feature filtering based on the selection of those shifts which correspond to the biochemical components providing the statistically significant differences between groups of examined tissues (center of glioblastoma multiforme, tissues from infiltration area and normally appeared white matter) and principal component analysis. We applied the support vector machine to classify tissues after dimensionality reduction of registered Raman spectra. The accuracy of the classification of malignant tissues (tumor edge and center) and normal ones using the principal component analysis alone was 83% with sensitivity of 96% and specificity of 44%. With a combined technique of dimensionality reduction we obtained 83% accuracy with 77% sensitivity and 92% specificity of tumor tissues classification.

Featuring few essential Raman spectroscopic signatures between heterogeneous cells

Here we demonstrate it is instructive to quantify cell Raman spectroscopy by sparse regularization. To be able to extract the specific spectral differences in a heterogeneous cell system with great spectroscopic similarities derived from many common biomolecular components, the maximum information entropy probability was proposed and exemplified by identifying normal lymphocytes from leukemia cells. The essential spectroscopic features were observed to locate at three Raman peaks whose spectral signatures were commensurate. The applicability of the extracted features was acknowledged by that the predicted identification accuracy of up to 93% was still achieved when only two peaks were loaded into decision tree model, which may provide the possibility of a clinically rapid hematological malignancy detection.

Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2–3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery.

Raman Spectroscopy as a Neuromonitoring Tool in Traumatic Brain Injury: A Systematic Review and Clinical Perspectives

Traumatic brain injury (TBI) is a significant global health problem, for which no disease-modifying therapeutics are currently available to improve survival and outcomes. Current neuromonitoring modalities are unable to reflect the complex and changing pathophysiological processes of the acute changes that occur after TBI. Raman spectroscopy (RS) is a powerful, label-free, optical tool which can provide detailed biochemical data in vivo. A systematic review of the literature is presented of available evidence for the use of RS in TBI. Seven research studies met the inclusion/exclusion criteria with all studies being performed in pre-clinical models. None of the studies reported the in vivo application of RS, with spectral acquisition performed ex vivo and one performed in vitro. Four further studies were included that related to the use of RS in analogous brain injury models, and a further five utilised RS in ex vivo biofluid studies for diagnosis or monitoring of TBI. RS is identified as a potential means to identify injury severity and metabolic dysfunction which may hold translational value. In relation to the available evidence, the translational potentials and barriers are discussed. This systematic review supports the further translational development of RS in TBI to fully ascertain its potential for enhancing patient care.

Application and Progress of Raman Spectroscopy in Male Reproductive System

Raman spectroscopy is a fast-developing, unmarked, non-invasive, non-destructive technique which allows for real-time scanning and sampling of biological samples in situ, reflecting the subtle biochemical composition alterations of tissues and cells through the variations of spectra. It has great potential to identify pathological tissue and provide intraoperative assistance in clinic. Raman spectroscopy has made many exciting achievements in the study of male reproductive system. In this review, we summarized literatures about the application and progress of Raman spectroscopy in male reproductive system from PubMed and Ovid databases, using MeSH terms associated to Raman spectroscopy, prostate, testis, seminal plasma and sperm. The existing challenges and development opportunities were also discussed and prospected.

Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae

We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.

Lock-in detector for accelerated nonlinear imaging

We show that accelerated nonlinear imaging, such as stimulated Raman scattering and pump-probe imaging, is enabled by an order of magnitude reduction of data acquisition time when replacing the exponentially-weighted-moving-average low-pass filter in a lock-in amplifier with a simple-moving-average filter. We show that this simple-moving-average (box) lock-in yields a superior signal-to-noise ratio and suppression of extraneous modulations with short pixel dwell times, if one condition for the relation between the lock-in time constant and modulation frequencies is met. Our results, both theoretical and experimental, indicate that for nonlinear imaging applications, the box lock-in significantly outperforms conventional lock-in detection. These results facilitate the application of ultrafast and nonlinear imaging as a new standard for material characterization.

Label-free detection of brain tumors in a 9L gliosarcoma rat model using stimulated Raman scattering-spectroscopic optical coherence tomography

SIGNIFICANCE

In neurosurgery, it is essential to differentiate between tumor and healthy brain regions to maximize tumor resection while minimizing damage to vital healthy brain tissue. However, conventional intraoperative imaging tools used to guide neurosurgery are often unable to distinguish tumor margins, particularly in infiltrative tumor regions and low-grade gliomas.

AIM

The aim of this work is to assess the feasibility of a label-free molecular imaging tool called stimulated Raman scattering-spectroscopic optical coherence tomography (SRS-SOCT) to differentiate between healthy brain tissue and tumor based on (1) structural biomarkers derived from the decay rate of signals as a function of depth and (2) molecular biomarkers based on relative differences in lipid and protein composition extracted from the SRS signals.

APPROACH

SRS-SOCT combines the molecular sensitivity of SRS (based on vibrational spectroscopy) with the spatial and spectral multiplexing capabilities of SOCT to enable fast, spatially and spectrally resolved molecular imaging. SRS-SOCT is applied to image a 9L gliosarcoma rat tumor model, a well-characterized model that recapitulates human high-grade gliomas, including high proliferative capability, high vascularization, and infiltration at the margin. Structural and biochemical signatures acquired from SRS-SOCT are extracted to identify healthy and tumor tissues.

RESULTS

Data show that SRS-SOCT provides light-scattering-based signatures that correlate with the presence of tumors, similar to conventional OCT. Further, nonlinear phase changes from the SRS interaction, as measured with SRS-SOCT, provide an additional measure to clearly separate tumor tissue from healthy brain regions. We also show that the nonlinear phase signals in SRS-SOCT provide a signal-to-noise advantage over the nonlinear amplitude signals for identifying tumors.

CONCLUSIONS

SRS-SOCT can distinguish both spatial and spectral features that identify tumor regions in the 9L gliosarcoma rat model. This tool provides fast, label-free, nondestructive, and spatially resolved molecular information that, with future development, can potentially assist in identifying tumor margins in neurosurgery.

Coherent Raman scattering microscopy: capable solution in search of a larger audience

SIGNIFICANCE

Coherent Raman scattering (CRS) microscopy is an optical imaging technique with capabilities that could benefit a broad range of biomedical research studies.

AIM

We reflect on the birth, rapid rise, and inescapable growing pains of the technique and look back on nearly four decades of developments to examine where the CRS imaging approach might be headed in the next decade to come.

APPROACH

We provide a brief historical account of CRS microscopy, followed by a discussion of the challenges to disseminate the technique to a larger audience. We then highlight recent progress in expanding the capabilities of the CRS microscope and assess its current appeal as a practical imaging tool.

RESULTS

New developments in Raman tagging have improved the specificity and sensitivity of the CRS technique. In addition, technical advances have led to CRS microscopes that can capture hyperspectral data cubes at practical acquisition times. These improvements have broadened the application space of the technique.

CONCLUSION

The technical performance of the CRS microscope has improved dramatically since its inception, but these advances have not yet translated into a substantial user base beyond a strong core of enthusiasts. Nonetheless, new developments are poised to move the unique capabilities of the technique into the hands of more users.

4D surface shape measurement system with high spectral resolution and great depth accuracy

A 4D surface shape measurement system that combines spectral detection and 3D surface morphology measurements is proposed, which can realize high spectral resolution and great depth accuracy (HSDA system). A starring hyperspectral imager system based on a grating generates precise spectral data, while a structured light stereovision system reconstructs target morphology as a 3D point cloud. The systems are coupled using a double light path module, which realize point-to-point correspondence of the systems' image planes. The spectral and 3D coordinate data are fused and transformed into a 4D data set. The HSDA system has excellent performance with a spectral resolution of 3 nm and depth accuracy of 27.5 μm. A range of 4D imaging experiments are presented to demonstrate the capabilities and versatility of the HSDA system, which show that it can be used in broad range of application areas, such as fluorescence detection, face anti-spoofing, physical health state assessment and green plant growth condition monitoring.

Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma

BACKGROUND

Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" that could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS.

METHODS

To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up an SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort.

RESULTS

Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on the spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal-appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal-appearing brain tissue can be detected.

CONCLUSION

These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning

A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm, respectively. We performed hyperspectral imaging (HSI) of three species of microalgae to verify their absorption characteristics. Transmission spectra were analyzed using principal component analysis (PCA) and peak ratio algorithms for dimensionality reduction and feature extraction, and a support vector machine (SVM) model was used for classification. The average accuracy, sensitivity and specificity to distinguish one species from the other two species were found to be 94.4%, 94.4% and 97.2%, respectively. A species identification experiment for a group of mixed microalgae in solution demonstrates the usability of the classification method. Using a random forest (RF) model, the growth stage in a phaeocystis growth cycle cultivated under laboratory conditions was predicted with an accuracy of 98.1%, indicating the feasibility to evaluate the growth state of microalgae through their transmission spectra. Experimental results show that the THMI system has the capability for classification, identification and growth stage estimation of microalgae, with strong potential for in-situ marine environmental monitoring and early warning detection applications.

Discrimination of Benign and Malignant Lesions in Canine Mammary Tissue Samples Using Raman Spectroscopy: A Pilot Study

Breast cancer is a health problem that affects individual life quality and the family system. It is the most frequent type of cancer in women, but men are also affected. As an integrative approach, comparative oncology offers an opportunity to learn more about natural cancers in different species. Methods based on Raman spectroscopy have shown significant potential in the study of the human breast through the fingerprinting of biological tissue, which provides valuable information that can be used to identify, characterize and discriminate structures in breast tissue, in both healthy and carcinogenic environments. One of the most important applications of Raman spectroscopy in medical diagnosis is the characterization of microcalcifications, which are highly important diagnostic indicators of breast tissue diseases. Raman spectroscopy has been used to analyze the chemical composition of microcalcifications. These occur in benign and malignant lesions in the human breast, and Raman helps to discriminate microcalcifications as type I and type II according to their composition. This paper demonstrates the recent progress in understanding how this vibrational technique can discriminate through the fingerprint regions of lesions in unstained histology sections from canine mammary glands.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.