Intraoperative Raman Spectroscopy

Open-sourced Raman spectroscopy data processing package implementing a baseline removal algorithm validated from multiple datasets acquired in human tissue and biofluids

SIGNIFICANCE

Standardized data processing approaches are required in the field of bio-Raman spectroscopy to ensure information associated with spectral data acquired by different research groups, and with different systems, can be compared on an equal footing.

AIM

An open-sourced data processing software package was developed, implementing algorithms associated with all steps required to isolate the inelastic scattering component from signals acquired using Raman spectroscopy devices. The package includes a novel morphological baseline removal technique (BubbleFill) that provides increased adaptability to complex baseline shapes compared to current gold standard techniques. Also incorporated in the package is a versatile tool simulating spectroscopic data with varying levels of Raman signal-to-background ratios, baselines with different morphologies, and varying levels of stochastic noise.

RESULTS

Application of the BubbleFill technique to simulated data demonstrated superior baseline removal performance compared to standard algorithms, including iModPoly and MorphBR. The data processing workflow of the open-sourced package was validated in four independent in-human datasets, demonstrating it leads to inter-systems data compatibility.

CONCLUSIONS

A new open-sourced spectroscopic data pre-processing package was validated on simulated and real-world in-human data and is now available to researchers and clinicians for the development of new clinical applications using Raman spectroscopy.

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.

Ultra-rapid somatic variant detection via real-time targeted amplicon sequencing

Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool-LAMPrey-to process sequenced LAMP product. LAMPrey's concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.

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.

Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies

Simple Summary

Isocitrate dehydrogenase (IDH) mutation is one of the most important prognostic markers in glioma tumors. Raman spectroscopy (RS) is an optical technique with great potential in intraoperative molecular diagnosis and surgical guidance. We analyzed RS’s ability to detect the IDH mutation onto unprocessed glioma biopsies. A total of 2073 Raman spectra were extracted from 38 tumor specimens. From the 103 Raman shifts screened, we identified 52 shifts (related to lipids, collagen, DNA and cholesterol/phospholipids) with the highest performance in the distinction of the two groups. We described 18 shifts never used before for IDH detection with RS in fresh or frozen samples. We were able to distinguish between IDH-mutated and IDH-wild-type tumors with an accuracy and precision of 87%. RS showed optimal accuracy and precision in discriminating IDH-mutated glioma from IDH-wild-type tumors ex-vivo onto fresh surgical specimens.

Abstract

Isocitrate dehydrogenase (IDH) mutational status is pivotal in the management of gliomas. Patients with IDH-mutated (IDH-MUT) tumors have a better prognosis and benefit more from extended surgical resection than IDH wild-type (IDH-WT). Raman spectroscopy (RS) is a minimally invasive optical technique with great potential for intraoperative diagnosis. We evaluated the RS’s ability to characterize the IDH mutational status onto unprocessed glioma biopsies. We extracted 2073 Raman spectra from thirty-eight unprocessed samples. The classification performance was assessed using the eXtreme Gradient Boosted trees (XGB) and Support Vector Machine with Radial Basis Function kernel (RBF-SVM). Measured Raman spectra displayed differences between IDH-MUT and IDH-WT tumor tissue. From the 103 Raman shifts screened as input features, the cross-validation loop identified 52 shifts with the highest performance in the distinction of the two groups. Raman analysis showed differences in spectral features of lipids, collagen, DNA and cholesterol/phospholipids. We were able to distinguish between IDH-MUT and IDH-WT tumors with an accuracy and precision of 87%. RS is a valuable and accurate tool for characterizing the mutational status of IDH mutation in unprocessed glioma samples. This study improves RS knowledge for future personalized surgical strategy or in situ target therapies for glioma tumors.

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.

Accurate identification of breast cancer margins in microenvironments of ex-vivo basal and luminal breast cancer tissues using Raman spectroscopy

Better knowledge of the breast tumor microenvironment is required for surgical resection and understanding the processes of tumor development. Raman spectroscopy is a promising tool that can assist in uncovering the molecular basis of disease and provide quantifiable molecular information for diagnosis and treatment evaluation. In this work, eighty-eight frozen breast tissue sections, including forty-four normal and forty-four tumor sections, were mapped in their entirety using a 250-μm-square measurement grid. Two or more smaller regions of interest within each tissue were additionally mapped using a 25 μm-square step size. A deep learning algorithm, convolutional neural network (CNN), was developed to distinguish histopathologic features with-in individual and across multiple tissue sections. Cancerous breast tissue were discriminated from normal breast tissue with 90 % accuracy, 88.8 % sensitivity and 90.8 % specificity with an excellent Area Under the Receiver Operator Curve (AUROC) of 0.96. Features that contributed significantly to the model were identified and used to generate RGB images of the tissue sections. For each grid point (pixel) on a Raman map, color was assigned to intensities at frequencies of 1002 cm-1 (Phenylalanine), 869 cm-1 (Proline, CC stretching of hydroxyproline-collagen assignment, single bond stretching vibrations for the amino acids proline, valine and polysaccharides) and 1309 cm-1 (CH3/CH2 twisting or bending mode of lipids). The Raman images clearly associate with hematoxylin and eosin stained tissue sections and allow clear visualization of boundaries between normal adipose, connective tissue and tumor. We demonstrated that this simple imaging technique allows high-resolution, straightforward molecular interpretation of Raman images. Raman spectroscopy provides rapid, label-free imaging of microscopic features with high accuracy. This method has application as laboratory tool and can assist with intraoperative tissue assessment during Breast Conserving surgery.

A "New Radiation"-shining light on cancer

There is renewed interest in novel spectroscopic techniques for molecular interrogation. Inelastic light scattering techniques can provide real-time phenotypic identification of tissue and cellular state. Here we review Raman spectroscopy as a powerful technique for the identification of cancerous tissue and tumor boundaries.

Spontaneous Raman spectroscopy for intracranial tumors diagnostics ex vivo

Neurosurgery of intracranial tumors, especially of glial origin, is a non-trivial task due to their infiltrative growth. In recent years, optical methods of intraoperative navigation have been actively used in neurosurgery. However, one of the most widely used approaches based on the selective accumulation of fluorescent contrast medium (5-ALA-induced protoporphyrin IX) by the tumor cannot be applied to a significant number of tumors due to its low accumulation. On the contrary, Raman spectroscopy, which allows analyzing the molecular composition of tissues while preserving all the advantages of the method of fluorescence spectroscopy, does not require the use of an exogenous dye and may become a method of choice when composing a system for intraoperative navigation or optical biopsy. This work presents the first results of using the principal component method to classify Raman spectra of human glioblastoma with intermediate processing of spectra to minimize possible errors from the fluorescence of both endogenous fluorophores and photosensitizers used in fluorescence navigation. As a result, differences were found in the principal component space, corresponding to tissue samples with microcystic components, extensive areas of necrosis, and foci of fresh hemorrhages. It is shown that this approach can serve as the basis for constructing a system for automatic intraoperative tissue classification based on the analysis of Raman spectra.

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.

Recalcitrant Issues and New Frontiers in Nano-Pharmacology

Packaging of old pharma drugs into new packaging "nanoparticles" is called nano-pharmacology and the products are called nano-based drugs. The inception of nano-pharmacology research and development (R&D) is marked by the approval of the first nano-based drug Doxil® in 1995 by the Food and Drug Administration. However, even after more than two decades, today, there are only ∼20 nano-based drugs in the market to treat cancers and brain diseases. In this article we share the perspectives of nanotechnology scientists, engineers, and clinicians on the roadblocks in nano-pharmacology R&D. Also, we share our opinion on new frontiers in the field of nano-pharmacology R&D that may allow rapid and efficient transfer of nano-pharma technologies from R&D to market.

Intraoperative fluorescence diagnosis in the brain: a systematic review and suggestions for future standards on reporting diagnostic accuracy and clinical utility

Background

Surgery for gliomas is often confounded by difficulties in distinguishing tumor from surrounding normal brain. For better discrimination, intraoperative optical imaging methods using fluorescent dyes are currently being explored. Understandably, such methods require the demonstration of a high degree of diagnostic accuracy and clinical benefit. Currently, clinical utility is determined by tissue biopsies which are correlated to optical signals, and quantified using measures such as sensitivity, specificity, positive predictive values, and negative predictive values. In addition, surgical outcomes, such as extent of resection rates and/or survival (progression-free survival (PFS) and overall survival (OS)) have been measured. These assessments, however, potentially involve multiple biases and confounders, which have to be minimized to ensure reproducibility, generalizability and comparability of test results. Test should aim at having a high internal and external validity. The objective of this article is to analyze how diagnostic accuracy and outcomes are utilized in available studies describing intraoperative imaging and furthermore, to derive recommendations for reliable and reproducible evaluations.

Methods

A review of the literature was performed for assessing the use of measures of diagnostic accuracy and outcomes of intraoperative optical imaging methods. From these data, we derive recommendations for designing and reporting future studies.

Results

Available literature indicates that potential confounders and biases for reporting the diagnostic accuracy and usefulness of intraoperative optical imaging methods are seldom accounted for. Furthermore, methods for bias reduction are rarely used nor reported.

Conclusions

Detailed, transparent, and uniform reporting on diagnostic accuracy of intraoperative imaging methods is necessary. In the absence of such reporting, studies will not be comparable or reproducible. Future studies should consider some of the recommendations given here.

Electronic supplementary material

The online version of this article (10.1007/s00701-019-04007-y) contains supplementary material, which is available to authorized users.

Development and first in-human use of a Raman spectroscopy guidance system integrated with a brain biopsy needle

Navigation-guided brain biopsies are the standard of care for diagnosis of several brain pathologies. However, imprecise targeting and tissue heterogeneity often hinder obtaining high-quality tissue samples, resulting in poor diagnostic yield. We report the development and first clinical testing of a navigation-guided fiberoptic Raman probe that allows surgeons to interrogate brain tissue in situ at the tip of the biopsy needle prior to tissue removal. The 900 μm diameter probe can detect high spectral quality Raman signals in both the fingerprint and high wavenumber spectral regions with minimal disruption to the neurosurgical workflow. The probe was tested in three brain tumor patients, and the acquired spectra in both normal brain and tumor tissue demonstrated the expected spectral features, indicating the quality of the data. As a proof-of-concept, we also demonstrate the consistency of the acquired Raman signal with different systems and experimental settings. Additional clinical development is planned to further evaluate the performance of the system and develop a statistical model for real-time tissue classification during the biopsy procedure.

Challenges in application of Raman spectroscopy to biology and materials

Raman spectroscopy has become an essential tool for chemists, physicists, biologists and materials scientists. In this article, we present the challenges in unravelling the molecule-specific Raman spectral signatures of different biomolecules like proteins, nucleic acids, lipids and carbohydrates based on the review of our work and the current trends in these areas. We also show how Raman spectroscopy can be used to probe the secondary and tertiary structural changes occurring during thermal denaturation of protein and lysozyme as well as more complex biological systems like bacteria. Complex biological systems like tissues, cells, blood serum etc. are also made up of such biomolecules. Using mice liver and blood serum, it is shown that different tissues yield their unique signature Raman spectra, owing to a difference in the relative composition of the biomolecules. Additionally, recent progress in Raman spectroscopy for diagnosing a multitude of diseases ranging from cancer to infection is also presented. The second part of this article focuses on applications of Raman spectroscopy to materials. As a first example, Raman spectroscopy of a melt cast explosives formulation was carried out to monitor the changes in the peaks which indicates the potential of this technique for remote process monitoring. The second example presents various modern methods of Raman spectroscopy such as spatially offset Raman spectroscopy (SORS), reflection, transmission and universal multiple angle Raman spectroscopy (UMARS) to study layered materials. Studies on chemicals/layered materials hidden in non-metallic containers using the above variants are presented. Using suitable examples, it is shown how a specific excitation or collection geometry can yield different information about the location of materials. Additionally, it is shown that UMARS imaging can also be used as an effective tool to obtain layer specific information of materials located at depths beyond a few centimeters.

This paper reviews various facets of Raman spectroscopy. This encompasses biomolecule fingerprinting and conformational analysis, discrimination of healthy vs. diseased states, depth-specific information of materials and 3D Raman imaging.