• Artificial intelligence
• Cervical cancer
• Coherent anti-Stokes Raman spectroscopy
• ConvNeXt network
• Raman spectroscopy

• Spectrum Analysis, Raman/methods
• Uterine Cervical Neoplasms/diagnosis
• Uterine Cervical Neoplasms/pathology
• Female
• Humans
• Artificial Intelligence
• Carcinoma, Squamous Cell/diagnosis
• Carcinoma, Squamous Cell/pathology
• Lipids/analysis
• Lipids/chemistry

• Brain tumour
• Electrocorticography
• Glioma
• Impedance
• Resistivity

• Humans
• Brain Neoplasms/surgery
• Electrocorticography/methods
• Electric Impedance
• Neurosurgical Procedures/methods

• Diagnostics
• Glioma
• Raman spectroscopy

• Spectrum Analysis, Raman/methods
• Humans
• Glioma/surgery
• Glioma/diagnostic imaging
• Machine Learning
• Brain Neoplasms/diagnostic imaging
• Brain Neoplasms/surgery
• Brain Neoplasms/pathology
• Sensitivity and Specificity

• Cytochrome-c
• ECM stiffness
• Mechanoresponse
• Mitochondria
• PC-LDA
• Raman spectroscopy

• Humans
• Female
• Breast Neoplasms/metabolism
• Extracellular Matrix/chemistry
• Extracellular Matrix/metabolism
• Collagen/analysis

• Differentiation
• FT-Raman
• Glioblastoma G4
• Machine learning
• Meningiomas
• Multivariate analyses

• Humans
• Meningioma/diagnosis
• Meningioma/pathology
• Glioblastoma/diagnosis
• Brain Neoplasms/diagnosis
• Brain Neoplasms/pathology
• Multivariate Analysis
• Spectrum Analysis, Raman/methods
• Principal Component Analysis
• Meningeal Neoplasms/pathology

• Au nanospheres
• Gliomas
• Nanomaterials
• Raman spectroscopy
• SERS
• ZrO(2)

• Humans
• Spectrum Analysis, Raman/methods
• Silver/chemistry
• Nanospheres/chemistry
• Glioma
• Metal Nanoparticles/chemistry
• Gold/chemistry

• Animals
• Humans
• Swine
• Huntington Disease/diagnosis
• Swine, Miniature
• Spectrum Analysis, Raman
• Brain
• Neurodegenerative Diseases

• Animal models
• Fourier transform infrared microspectroscopy (FTIR)
• Glioblastoma multiforme (GBM)
• Principal component analysis (PCA)
• Raman microscopy
• Vibrational spectroscopy
• X-ray fluorescence (XRF) spectrometry

• Rats
• Animals
• Glioblastoma/chemistry
• Spectroscopy, Fourier Transform Infrared/methods
• Proteins
• Brain/pathology
• Models, Animal

• Science Foundation Ireland
• Irish Research Council

• artificial intelligence
• automated tissue characterization
• brain tumor imaging
• optical coherance tomography
• residual tumor detection
• tissue classification
• tumor border detection
• visual image analysis

• cardiology
• diagnosis
• neurosurgery
• orthopedic
• pathology analysis

• R25 NS108939 NINDS NIH HHS

• Raman spectroscopy
• metabolic dysfunction
• neuroinflammation
• neuromonitoring
• oxidative stress
• point-of-care diagnostics
• traumatic brain injury

• Brain Injuries
• Brain Injuries, Traumatic/diagnosis
• Humans
• Spectrum Analysis, Raman

• Department of Health

• Biomedical imaging
• Cell culture
• Colon cancer
• Mevastatin
• Raman Spectroscopy
• Raman imaging
• Statins

• Caco-2 Cells
• Colonic Neoplasms/drug therapy
• Humans
• Hydroxymethylglutaryl-CoA Reductase Inhibitors
• Lovastatin/analogs & derivatives

One of the major challenges during glioblastoma surgery is balancing between maximizing extent of resection and preventing neurological deficits. Several surgical techniques and adjuncts have been developed to help identify eloquent areas both preoperatively (fMRI, nTMS, MEG, DTI) and intraoperatively (imaging (ultrasound, iMRI), electrostimulation (mapping), cerebral perfusion measurements (fUS)), and visualization (5-ALA, fluoresceine)). In this review, we give an update of the state-of-the-art management of both primary and recurrent glioblastomas. We will review the latest surgical advances, challenges, and approaches that define the onco-neurosurgical practice in a contemporary setting and give an overview of the current prospective scientific efforts.

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.

• Raman spectroscopy
• data integration
• glioblastoma

• Adult
• Aged
• Brain Neoplasms/genetics
• Brain Neoplasms/pathology
• Female
• Glioblastoma/genetics
• Glioblastoma/pathology
• Humans
• Male
• Middle Aged
• Spectrum Analysis, Raman/methods
• Transcriptome/genetics

• Institut National Du Cancer

• T32 EB014836 NIBIB NIH HHS
• UL1 TR001420 NCATS NIH HHS

• Cell discrimination
• Chemometrics
• Glioblastoma
• Monocytes
• Raman spectroscopy
• T-cells

• Discriminant Analysis
• Glioblastoma
• Humans
• Principal Component Analysis
• Spectrum Analysis, Raman
• Support Vector Machine

• Animals
• Disease Models, Animal
• Female
• Mice
• Mice, Inbred C57BL
• Ovarian Neoplasms/chemistry
• Spectrum Analysis/methods
• Trace Elements/analysis

• FFPE
• Raman spectroscopy
• glioblastoma
• machine learning
• pathology

• Fondation Cancer Luxemburg
• Luxembourg National Research Fund

• Raman
• fluorescence
• glioblastoma
• in-vivo
• reflectance
• spectroscopy

• Raman imaging
• Raman spectroscopy
• colon cancer
• photochemistry
• photosensitizer

• Cells, Cultured
• Colon/metabolism
• Colonic Neoplasms/metabolism
• Humans
• Indoles/chemistry
• Indoles/metabolism
• Isoindoles
• Optical Imaging
• Organometallic Compounds/chemistry
• Organometallic Compounds/metabolism
• Photochemistry
• Single-Cell Analysis
• Spectrometry, Fluorescence/methods
• Spectrum Analysis, Raman/methods
• Zinc Compounds

• UMO-2017/25/B/ST4/01788 The National Science Centre Poland

• Raman
• coherent anti-Stokes Raman scattering
• neurosurgery
• stimulated Raman scattering

• Brain/diagnostic imaging
• Brain/surgery
• Neurosurgery
• Neurosurgical Procedures
• Spectrum Analysis, Raman

• CIHR

• Brain tumours
• Principal component analysis
• Surface-enhanced Raman spectroscopy

• Astrocytoma/chemistry
• Astrocytoma/diagnosis
• Brain Chemistry
• Brain Neoplasms/chemistry
• Brain Neoplasms/diagnosis
• Brain Neoplasms/pathology
• Glioblastoma/chemistry
• Glioblastoma/diagnosis
• Humans
• Principal Component Analysis
• Spectrum Analysis, Raman/methods

• Raman spectroscopy
• autofluorescence
• carbon nanoparticles
• dynamic optical contrast imaging
• imaging techniques
• indocyanine green
• laser speckle contrast imaging
• parathyroid adenoma
• parathyroid gland
• shear wave elastography

• Clinical Decision-Making
• Disease Management
• Humans
• Intraoperative Care/methods
• Multimodal Imaging/methods
• Parathyroid Glands/diagnostic imaging
• Parathyroid Glands/pathology
• Parathyroid Glands/surgery
• Parathyroid Neoplasms/diagnosis
• Parathyroid Neoplasms/surgery
• Preoperative Care/methods
• Radiopharmaceuticals
• Spectrum Analysis, Raman
• Technetium Tc 99m Sestamibi
• Theranostic Nanomedicine

• Raman spectroscopy
• fluorescence
• machine learning
• signal processing
• surgery
• tissue optics

• Brain Neoplasms/diagnostic imaging
• Brain Neoplasms/surgery
• Humans
• Sensitivity and Specificity
• Spectrum Analysis, Raman

• CIHR

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.

• atypical meningioma
• brain tumor
• craniotomy
• neuro-oncology
• neurosurgery
• oncology
• pathology
• skull base
• stimulated raman histology
• tumor margins

• PI3K/Akt signaling pathway
• glioma
• lipid biomarkers
• matrix assisted laser desorption/ionization-imaging
• sphingomyelin

Raman spectroscopy and imaging are highly structure-sensitive methods that allow the characterization of biological samples with minimal impact. In this paper, Raman spectra and imaging of noncancerous and cancerous human colon tissue samples were measured at different excitation wavelengths: 355, 532, and 785 nm. Intra-patient variability in the analyzed spectra showed colon sample heterogeneity for both noncancerous and cancerous human sample types. The lowest inter-patient variability of Raman spectra was observed for the fingerprint region of noncancerous samples for the 532 nm excitation laser line. The bands of principal biochemical constituents (proteins, lipids, nucleic acids) predominate in VIS and NIR-Raman spectra (excitation: 532, 785 nm), with the special role of the bands of intrinsic tissue chromophores—carotenoids for VIS excitation due to resonance enhancement. At 355 nm excitation, high autofluorescence of colon tissues were observed. Our studies proved high potential of Raman spectroscopy and Raman imaging in differentiation of noncancerous and cancerous human colon tissues and that the wavelengths 532 and 785 nm offer wide possibilities for the detection of human colon tissue pathology for ex vivo and in vivo measurements and prevail over 355 nm excitation.

Raman spectroscopy and imaging are highly structure-sensitive methods that allow the characterization of biological samples with minimal impact.

Introduction: Glioma surgery is aimed at obtaining maximal safe tumor resection while preserving or improving patient's neurological status. For this reason, there is growing interest for intra-operative imaging in neuro-oncological surgery. Intra-operative ultrasound (ioUS) provides the surgeon with real-time, anatomical and functional information. Despite this, in neurosurgery ioUS mainly relies only on gray-scale brightness mode (B-mode). Many other ultrasound imaging modalities, such as Fusion Imaging with pre-operative acquired magnetic resonance imaging (MRI), Doppler modes, Contrast Enhanced Ultrasound (CEUS), and elastosonography have been developed and have been extensively used in other organs. Although these modalities offer valuable real-time intra-operative information, so far their usage during neurosurgical procedures is still limited.

Purpose: To present an US-based multimodal approach for image-guidance in glioma surgery, highlighting the different features of advanced US modalities: fusion imaging with pre-operative acquired MRI for Virtual Navigation, B-mode, Doppler (power-, color-, spectral-), CEUS, and elastosonography.

Methods: We describe, in a step-by-step fashion, the applications of the most relevant advanced US modalities during different stages of surgery and their implications for surgical decision-making. Each US modality is illustrated from a technical standpoint and its application during glioma surgery is discussed.

Results: B-mode offers dynamic morphological information, which can be further implemented with fusion imaging to improve image understanding and orientation. Doppler imaging permits to evaluate anatomy and function of the vascular tree. CEUS allows to perform a real-time angiosonography, providing valuable information in regards of parenchyma and tumor vascularization and perfusion. This facilitates tumor detection and surgical strategy, also allowing to characterize tumor grade and to identify residual tumor. Elastosonography is a promising tool able to better define tumor margins, parenchymal infiltration, tumor consistency and permitting differentiation of high grade and low grade lesions.

Conclusions: Multimodal ioUS represents a valuable tool for glioma surgery being highly informative, rapid, repeatable, and real-time. It is able to differentiate low grade from high grade tumors and to provide the surgeon with relevant information for surgical decision-making. ioUS could be integrated with other intra-operative imaging and functional approaches in a synergistic manner to offer the best image guidance for each patient.

• B-mode
• Doppler
• Glioma
• contrast enhanced ultrasound
• elastography
• fusion imaging
• intra-operative ultrasound
• navigated ultrasound

To investigate the possibility of diagnosing gastric cancer from an unstained pathological tissue using Raman spectroscopy, and to compare the findings to those obtained with conventional histopathology.

We produced two consecutive tissue specimens from areas with and without cancer lesions in the surgically resected stomach of a patient with gastric cancer. One of the two tissue specimens was stained with hematoxylin and eosin and used as a reference for laser irradiation positioning by the spectroscopic method. The other specimen was left unstained and used for Raman spectroscopy analysis.

A significant Raman scattering spectrum could be obtained at all measurement points. Raman scattering spectrum intensities of 725 cm^-1 and 782 cm^-1, are associated with the nucleotides adenine and cytosine, respectively. The Raman scattering spectrum intensity ratios of 782 cm^-1/620 cm^-1, 782 cm^-1/756 cm^-1, 782 cm^-1/1250 cm^-1, and 782 ​​cm^-1/1263 cm^-1 in the gastric adenocarcinoma tissue were significantly higher than those in the normal stomach tissue.

The results of this preliminary experiment suggest the feasibility of our spectroscopic method as a diagnostic tool for gastric cancer using unstained pathological specimens.

• Label-free analysis
• Raman spectroscopy
• Histopathological examination
• Gastric cancer

• Raman spectroscopy
• in vivo
• intraoperative
• label-free

• Algorithms
• Diagnostic Imaging
• Equipment Design
• Humans
• Phantoms, Imaging
• Spectrum Analysis, Raman