Raman spectroscopy to differentiate between fresh tissue samples of glioma and normal brain: a comparison with 5-ALA-induced fluorescence-guided surgery

An interpretable multi-scale convolutional attention residual neural network for glioma grading with Raman spectroscopy

Since the malignancy of gliomas varies with their grade, classifying gliomas of different grades can assist doctors in developing personalized surgical plans during surgery, thereby improving the prognosis. Raman spectroscopy is an optical method for real-time glioma diagnosis. However, high-grade glioma (HGG, WHO grades III and IV), low-grade glioma (LGG, WHO grades I and II) and normal tissue have similar biochemical components, leading to similar spectral characteristics. This similarity reduces classification accuracy when using traditional machine learning methods. In contrast, deep learning offers enhanced feature extraction capabilities without the need for extensive feature engineering. Nevertheless, the diversity in the scale of spectral features presents challenges in designing a neural network that effectively adapts to these characteristics. To address these issues, this paper proposes a Multi-Scale Convolutional Attention Residual Network (M-SCA ResNet), which incorporates multi-scale channel and spatial attention mechanisms along with residual structures to improve the model's feature extraction capabilities. The algorithm presented in this study, was employed to classify HGG, LGG, and healthy tissue and was compared with conventional machine learning and neural networks. The results indicate that the M-SCA ResNet achieved an identification accuracy exceeding 85% for all three tissue types, along with the highest weighted F1-score. Furthermore, to enhance the interpretability of deep learning models, Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to extract and visualize key Raman shifts that significantly contribute to classification. Most of the extracted Raman shifts correspond to characteristic peaks of brain tissue which have been demonstrated to be effective in distinguishing between glioma of different grades and normal tissue in previous studies. This finding proves the strong correlation between the feature extraction capabilities of the M-SCA ResNet and the biomolecular characteristics of various tissues. The experiments conducted in this study validate the feasibility of using the M-SCA ResNet for glioma grading and provide valuable support for formulating subsequent surgical and treatment plans, indicating its promising application in in vivo and in situ spectral diagnosis of glioma grading.

Accuracy of Raman spectroscopy in discriminating normal brain tissue from brain tumor: A systematic review and meta-analysis

IMPORTANCE

There are several methods of intraoperative tumor border identification, but none of them is perfect. There is a need of a new tool.

OBJECTIVE

Raman spectroscopy, being a noninvasive, requiring no tissue preparation, quick technique of substance structure identification, is a potential tool for intraoperative identification of brain tumor. This meta-analysis aimed to assess the accuracy of Raman spectroscopy in differentiation of normal brain tissue from brain tumor.

DATA SOURCES

PubMed, Google Scholar, Scopus and Web of Science databases were searched until October 1, 2024.

STUDY SELECTION

All English-language articles reporting efficacy and accuracy of Raman spectroscopy for brain tumor differentiation were analyzed, sufficient data to construct 2x2 table was extracted.

EXCLUSION CRITERIA

studies using data from national databases; reviews, conference abstracts, case studies, letters to the editor; studies with irrelevant or not sufficient data; not human tissue used in the experiment. 6112 records were found; after exclusion, the suitability of 64 full-text articles was evaluated. 18 studies were reviewed and included into the meta-analysis.

DATA EXTRACTION AND SYNTHESIS

The meta-analysis was performed in accordance with PRISMA guidelines and recommendations. Methodological quality was assessed according to the QUADAS-2 guidelines. Data were extracted by multiple observers and any discrepancies were resolved by discussion and consensus. Data were pooled using a random-effects model.

MAIN OUTCOME(S) AND MEASURE(S)

The primary outcome was pooled sensitivity, specificity and diagnostic odds ratio (DOR) for Raman spectroscopy.

RESULTS

The manuscript presents 18 studies which were used to calculate pooled values. The pooled sensitivity, specificity and pooled diagnostic odds ratio (DOR) of RS for discriminating glioma and normal brain tissues were 0,965, 0,738 and 61,305 respectively. For GBM the results were 0,948, 0,506 and 78,420 respectively. For meningioma pooled values were 0,896, 0,913, and 149,59. For metastases pooled values were 0,946, 0,862 and 133,90 respectively.

CONCLUSIONS AND RELEVANCE

Raman spectroscopy has a potential to serve as a tool for differentiation of brain tumor from normal brain tissue. Not only could it be helpful in distinguishing malignant lesion from benign with high sensitivity and specificity, but also indicate type of tumor. There is a need for more studies examining the accuracy of spectroscopy in differentiating brain tumors from healthy tissues, especially in vivo and in differentiation of brain tumor subtypes.

A Phase 2 Sensitivity and Selectivity Study of High-Dose 5-Aminolevulinic Acid in Adult Patients Undergoing Resection of a Newly Diagnosed or Recurrent Glioblastoma

BACKGROUND AND OBJECTIVES

The utility of oral 5-aminolevulinic acid (5-ALA)/protoporphyrin fluorescence for the resection of high-grade gliomas is well documented, but the problem of false-negative observations remains. This study compares high-grade glioma visualization with low/standard dose 5-ALA (<30 mg/kg) to high-dose 5-ALA (>40 mg/kg) to see if by using this higher dose, it is possible to reduce the rate of false-negative observations without increasing the rate of false-positive (FP) observations and therefore increase the sensitivity.

METHODS

This is a prospective study of consecutive patients with radiological evidence of presumed high-grade glioma. We reviewed the data from patients who received preoperative low/standard doses and patients who received a preoperative high dose of 5-ALA. Adverse events, dose to observation time, intensity of tumor fluorescence, and results of biopsies in areas of tumor and tumor bed under deep blue light were recorded.

RESULTS

A total of 22 patients with high-grade glioma received a dose >40 mg/kg (high-dose) and 9 patients received <30 mg/kg (low/standard dose). There were no serious adverse events related to 5-ALA in any subject. There was a very high sensitivity and specificity of 5-ALA for the presence of tumor in both groups. There were no FP observations (fluorescence with no tumor) in either group. The specificity and the positive predictive value were 100% in both groups. The sensitivity and the negative predictive value were 53.3% and 30.0% in the low/standard dose group and 59.5% and 31.8% in the high-dose group, respectively.

CONCLUSION

High-dose oral 5-aminolevulinic/protoporphyrin fluorescence is a safe and effective aid to the intraoperative detection of high-grade gliomas with high sensitivity and specificity. False-negative observations with a high dose do not seem to be less than that with a low/standard dose. The rate of FP observations with both groups remains very low.

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.

Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy

Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution in clinical specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. These biochemical metabolites identified by Raman spectroscopy are further confirmed by spatial metabolomics. Based on differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label-free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. Most importantly, the cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm2. Raman spectroscopy enables the detection of previously undetectable diffusely infiltrative cancer cells, which holds potential value in guiding complete tumor resection in GBM patients.

DHM/SERS reveals cellular morphology and molecular changes during iPSCs-derived activation of astrocytes

The activation of astrocytes derived from induced pluripotent stem cells (iPSCs) is of great significance in neuroscience research, and it is crucial to obtain both cellular morphology and biomolecular information non-destructively in situ, which is still complicated by the traditional optical microscopy and biochemical methods such as immunofluorescence and western blot. In this study, we combined digital holographic microscopy (DHM) and surface-enhanced Raman scattering (SERS) to investigate the activation characteristics of iPSCs-derived astrocytes. It was found that the projected area of activated astrocytes decreased by 67%, while the cell dry mass increased by 23%, and the cells changed from a flat polygonal shape to an elongated star-shaped morphology. SERS analysis further revealed an increase in the intensities of protein spectral peaks (phenylalanine 1001 cm-1, proline 1043 cm-1, etc.) and lipid-related peaks (phosphatidylserine 524 cm-1, triglycerides 1264 cm-1, etc.) decreased in intensity. Principal component analysis-linear discriminant analysis (PCA-LDA) modeling based on spectral data distinguished resting and reactive astrocytes with a high accuracy of 96.5%. The increase in dry mass correlated with the increase in protein content, while the decrease in projected area indicated the adjustment of lipid composition and cell membrane remodeling. Importantly, the results not only reveal the cellular morphology and molecular changes during iPSCs-derived astrocytes activation but also reflect their mapping relationship, thereby providing new insights into diagnosing and treating neurodegenerative diseases.

Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy

Molecular genetics is highly related with prognosis of high-grade glioma. Accordingly, the latest WHO guideline recommends that molecular subgroups of the genes, including IDH, 1p/19q, MGMT, TERT, EGFR, Chromosome 7/10, CDKN2A/B, need to be detected to better classify glioma and guide surgery and treatment. Unfortunately, there is no preoperative or intraoperative technology available for accurate and comprehensive molecular subgrouping of glioma. Here, we develop a deep learning-assisted fiber-optic Raman diagnostic platform for accurate and rapid molecular subgrouping of high-grade glioma. Specifically, a total of 2,354 fingerprint Raman spectra was obtained from 743 tissue sites (astrocytoma: 151; oligodendroglioma: 150; glioblastoma (GBM): 442) of 44 high-grade glioma patients. The convolutional neural networks (ResNet) model was then established and optimized for molecular subgrouping. The mean area under receiver operating characteristic curves (AUC) for identifying the molecular subgroups of high-grade glioma reached 0.904, with mean sensitivity of 83.3%, mean specificity of 85.0%, mean accuracy of 83.3%, and mean time expense of 10.6 s. The diagnosis performance using ResNet model was shown to be superior to PCA-SVM and UMAP models, suggesting that high dimensional information from Raman spectra would be helpful. In addition, for the molecular subgroups of GBM, the mean AUC reached 0.932, with mean sensitivity of 87.8%, mean specificity of 83.6%, and mean accuracy of 84.1%. Furthermore, according to saliency maps, the specific Raman features corresponding to tumor-associated biomolecules (e.g. nucleic acid, tyrosine, tryptophan, cholesteryl ester, fatty acid, and collagen) were found to contribute to the accurate molecular subgrouping. Collectively, this study opens up new opportunities for accurate and rapid molecular subgrouping of high-grade glioma, which would assist optimal surgical resection and instant post-operative decision-making.

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.

Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer

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

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.

A new approach for clinical translation of infrared spectroscopy: exploitation of the signature of glioblastoma for general brain tumor recognition

PURPOSE

Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition.

METHODS

Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors.

RESULTS

The model correctly classified 82% (517/628) of specimens as "tumor" or "non-tumor", respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified.

CONCLUSION

The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.

The complementary value of intraoperative fluorescence imaging and Raman spectroscopy for cancer surgery: combining the incompatibles

A clear margin is an important prognostic factor for most solid tumours treated by surgery. Intraoperative fluorescence imaging using exogenous tumour-specific fluorescent agents has shown particular benefit in improving complete resection of tumour tissue. However, signal processing for fluorescence imaging is complex, and fluorescence signal intensity does not always perfectly correlate with tumour location. Raman spectroscopy has the capacity to accurately differentiate between malignant and healthy tissue based on their molecular composition. In Raman spectroscopy, specificity is uniquely high, but signal intensity is weak and Raman measurements are mainly performed in a point-wise manner on microscopic tissue volumes, making whole-field assessment temporally unfeasible. In this review, we describe the state-of-the-art of both optical techniques, paying special attention to the combined intraoperative application of fluorescence imaging and Raman spectroscopy in current clinical research. We demonstrate how these techniques are complementary and address the technical challenges that have traditionally led them to be considered mutually exclusive for clinical implementation. Finally, we present a novel strategy that exploits the optimal characteristics of both modalities to facilitate resection with clear surgical margins.

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.

Surgical Neuro-Oncology

Gliomas are the most common intrinsic brain tumor in adults. Although maximal tumor resection improves survival, this must be balanced with preservation of neurologic function. Technological advancements have greatly expanded our ability to safely maximize tumor resection and design innovative therapeutic trials that take advantage of intracavitary delivery of therapeutic agents after resection. In this article, we review the role of surgical intervention for both low-grade and high-grade gliomas and the innovations that are driving and expanding the role of surgery in this therapeutically challenging group of malignancies.

DDRugging glioblastoma: understanding and targeting the DNA damage response to improve future therapies

Glioblastoma is the most frequently diagnosed type of primary brain tumour in adults. These aggressive tumours are characterised by inherent treatment resistance and disease progression, contributing to ~ 190 000 brain tumour-related deaths globally each year. Current therapeutic interventions consist of surgical resection followed by radiotherapy and temozolomide chemotherapy, but average survival is typically around 1 year, with < 10% of patients surviving more than 5 years. Recently, a fourth treatment modality of intermediate-frequency low-intensity electric fields [called tumour-treating fields (TTFields)] was clinically approved for glioblastoma in some countries after it was found to increase median overall survival rates by ~ 5 months in a phase III randomised clinical trial. However, beyond these treatments, attempts to establish more effective therapies have yielded little improvement in survival for patients over the last 50 years. This is in contrast to many other types of cancer and highlights glioblastoma as a recognised tumour of unmet clinical need. Previous work has revealed that glioblastomas contain stem cell-like subpopulations that exhibit heightened expression of DNA damage response (DDR) factors, contributing to therapy resistance and disease relapse. Given that radiotherapy, chemotherapy and TTFields-based therapies all impact DDR mechanisms, this Review will focus on our current knowledge of the role of the DDR in glioblastoma biology and treatment. We also discuss the potential of effective multimodal targeting of the DDR combined with standard-of-care therapies, as well as emerging therapeutic targets, in providing much-needed improvements in survival rates for patients.

Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia

Applications of machine learning (ML) in translational medicine include therapeutic drug creation, diagnostic development, surgical planning, outcome prediction, and intraoperative assistance. Opportunities in the neurosciences are rich given advancement in our understanding of the brain, expanding indications for intervention, and diagnostic challenges often characterized by multiple clinical and environmental factors. We present a review of ML in neuro-oncology, epilepsy, Alzheimer's disease, and schizophrenia to highlight recent progression in these field, optimizing machine learning capabilities in their current forms. Supervised learning models appear to be the most commonly incorporated algorithm models for machine learning across the reviewed neuroscience disciplines with primary aim of diagnosis. Accuracy ranges are high from 63% to 99% across all algorithms investigated. Machine learning contributions to neurosurgery, neurology, psychiatry, and the clinical and basic science neurosciences may enhance current medical best practices while also broadening our understanding of dynamic neural networks and the brain.

How molecular imaging will enable robotic precision surgery : The role of artificial intelligence, augmented reality, and navigation

Molecular imaging is one of the pillars of precision surgery. Its applications range from early diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In particular, molecular imaging solutions are in high demand in minimally invasive surgical strategies, such as the substantially increasing field of robotic surgery. This review aims at connecting the molecular imaging and nuclear medicine community to the rapidly expanding armory of surgical medical devices. Such devices entail technologies ranging from artificial intelligence and computer-aided visualization technologies (software) to innovative molecular imaging modalities and surgical navigation (hardware). We discuss technologies based on their role at different steps of the surgical workflow, i.e., from surgical decision and planning, over to target localization and excision guidance, all the way to (back table) surgical verification. This provides a glimpse of how innovations from the technology fields can realize an exciting future for the molecular imaging and surgery communities.

Raman Imaging and Fluorescence Lifetime Imaging Microscopy for Diagnosis of Cancer State and Metabolic Monitoring

Hurdles for effective tumor therapy are delayed detection and limited effectiveness of systemic drug therapies by patient-specific multidrug resistance. Non-invasive bioimaging tools such as fluorescence lifetime imaging microscopy (FLIM) and Raman-microspectroscopy have evolved over the last decade, providing the potential to be translated into clinics for early-stage disease detection, in vitro drug screening, and drug efficacy studies in personalized medicine. Accessing tissue- and cell-specific spectral signatures, Raman microspectroscopy has emerged as a diagnostic tool to identify precancerous lesions, cancer stages, or cell malignancy. In vivo Raman measurements have been enabled by recent technological advances in Raman endoscopy and signal-enhancing setups such as coherent anti-stokes Raman spectroscopy or surface-enhanced Raman spectroscopy. FLIM enables in situ investigations of metabolic processes such as glycolysis, oxidative stress, or mitochondrial activity by using the autofluorescence of co-enzymes NADH and FAD, which are associated with intrinsic proteins as a direct measure of tumor metabolism, cell death stages and drug efficacy. The combination of non-invasive and molecular-sensitive in situ techniques and advanced 3D tumor models such as patient-derived organoids or microtumors allows the recapitulation of tumor physiology and metabolism in vitro and facilitates the screening for patient-individualized drug treatment options.

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.

Rationale and Clinical Implications of Fluorescein-Guided Supramarginal Resection in Newly Diagnosed High-Grade Glioma

Current standard of care for glioblastoma is surgical resection followed by temozolomide chemotherapy and radiation. Recent studies have demonstrated that >95% extent of resection is associated with better outcomes, including prolonged progression-free and overall survival. The diffusely infiltrative pattern of growth in gliomas results in microscopic extension of tumor cells into surrounding brain parenchyma that makes complete resection unattainable. The historical goal of surgical management has therefore been maximal safe resection, traditionally guided by MRI and defined as removal of all contrast-enhancing tumor. Optimization of surgical resection has led to the concept of supramarginal resection, or removal beyond the contrast-enhancing region on MRI. This strategy of extending the cytoreductive goal targets a tumor region thought to be important in the recurrence or progression of disease as well as resistance to systemic and local treatment. This approach must be balanced against the risk of impacting eloquent regions of brain and causing permanent neurologic deficit, an important factor affecting overall survival. Over the years, fluorescent agents such as fluorescein sodium have been explored as a means of more reliably delineating the boundary between tumor core, tumor-infiltrated brain, and surrounding cortex. Here we examine the rationale behind extending resection into the infiltrative tumor margins, review the current literature surrounding the use of fluorescein in supramarginal resection of gliomas, discuss the experience of our own institution in utilizing fluorescein to maximize glioma extent of resection, and assess the clinical implications of this treatment strategy.

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.