Stimulated Raman histology in the neurosurgical workflow of a major European neurosurgical center - part A

Toward cancer detection by label-free microscopic imaging in oncological surgery: Techniques, instrumentation and applications

This review examines the clinical application of label-free microscopy and spectroscopy, which are based on optical signals emitted by tissue components. Over the past three decades, a variety of techniques have been investigated with the aim of developing an in situ histopathology method that can rapidly and accurately identify tumor margins during surgical procedures. These techniques can be divided into two groups. One group encompasses techniques exploiting linear optical signals, and includes infrared and Raman microspectroscopy, and autofluorescence microscopy. The second group includes techniques based on nonlinear optical signals, including harmonic generation, coherent Raman scattering, and multiphoton autofluorescence microscopy. Some of these methods provide comparable information, while others are complementary. However, all of them have distinct advantages and disadvantages due to their inherent nature. The first part of the review provides an explanation of the underlying physics of the excitation mechanisms and a description of the instrumentation. It also covers endomicroscopy and data analysis, which are important for understanding the current limitations in implementing label-free techniques in clinical settings. The second part of the review describes the application of label-free microscopy imaging to improve oncological surgery with focus on brain tumors and selected gastrointestinal cancers, and provides a critical assessment of the current state of translation of these methods into clinical practice. Finally, the potential of confocal laser endomicroscopy for the acquisition of autofluorescence is discussed in the context of immediate clinical applications.

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.

Intraoperative classification of CNS lymphoma and glioblastoma by AI-based analysis of Stimulated Raman Histology (SRH)

Introduction

Early diagnosis is important to differentiate central nervous system lymphomas (CNSL) from the main differential diagnosis, glioblastoma (GBM), because of different primary treatment modalities for these entities. Due to neurological deficits, diagnostic stereotactic biopsies often need to be performed urgently. In this setting the availability of an intraoperative neuropathological assessment is limited.

Research question

This study uses AI-based analysis of Stimulated Raman Histology (SRH) to establish a classifier distinguishing CNSL from glioblastoma in an intraoperative setting.

Material and methods

We collected 126 intraoperative SRH images from 40 patients diagnosed with CNSL. These SRH images were divided into patches, measuring 224 x 224 pixels each. Additionally, we used a comparative dataset of 87 SRH images from 31 patients with GBM as a control group to train and validate a neural network based on the CTransPath architecture. Two distinct diagnostic categories were established: "Lymphoma" and "Glioblastoma".

Results

Our model demonstrated an accuracy rate of 92.5% in distinguishing between lymphoma and glioblastoma. Analysis of our test dataset showed a sensitivity of 84.2% and a specificity of 100% in the detection of CNSL, demonstrating performance comparable to standard intraoperative histopathological analysis.

Discussion and conclusion

The use of AI-driven analysis of SRH images holds promise for intraoperative tissue examination of stereotactic biopsies with suspected CNSL en par with the current gold standard. This study could improve the management of these cases especially in the emergency setting when conventional intraoperative neuropathological evaluation is unavailable.

Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing

Glioblastomas are invasive brain tumors with high therapeutic resistance. Neuron-to-glioma synapses have been shown to promote glioblastoma progression. However, a characterization of tumor-connected neurons has been hampered by a lack of technologies. Here, we adapted retrograde tracing using rabies viruses to investigate and manipulate neuron-tumor networks. Glioblastoma rapidly integrated into neural circuits across the brain, engaging in widespread functional communication, with cholinergic neurons driving glioblastoma invasion. We uncovered patient-specific and tumor-cell-state-dependent differences in synaptogenic gene expression associated with neuron-tumor connectivity and subsequent invasiveness. Importantly, radiotherapy enhanced neuron-tumor connectivity by increased neuronal activity. In turn, simultaneous neuronal activity inhibition and radiotherapy showed increased therapeutic effects, indicative of a role for neuron-to-glioma synapses in contributing to therapeutic resistance. Lastly, rabies-mediated genetic ablation of tumor-connected neurons halted glioblastoma progression, offering a viral strategy to tackle glioblastoma. Together, this study provides a framework to comprehensively characterize neuron-tumor networks and target glioblastoma.

Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples

BACKGROUND

Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples.

METHODS

In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at -80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs.

RESULTS

The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1-5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53).

CONCLUSIONS

Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools.

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.

Advancements in Neurosurgical Intraoperative Histology

Despite their relatively low incidence globally, central nervous system (CNS) tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and the identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and the rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes.

[Intraoperative stimulated Raman histology for personalized brain tumor surgery]

BACKGROUND

In brain tumor surgery a personalized surgical approach is crucial to achieve a maximum safe tumor resection. The extent of resection decisively depends on the histological diagnosis. Stimulated Raman histology (SRH), a fiber laser-based optical imaging method, offers the possibility for evaluation of an intraoperative diagnosis in a few minutes.

OBJECTIVE

To provide an overview on the applications of SRH in neurosurgery and transference of the technique to other surgical disciplines.

METHODS

Description of the technique and review of the current literature on SRH.

RESULTS

The SRH technique was successfully used in multiple neuro-oncological tumor entities. Initial pilot projects showed the potential for analysis of extracranial tumors.

CONCLUSION

The use of SRH provides a near real-time diagnosis with high diagnostic accuracy and provides further developmental potential to improve personalized tumor surgery.

Isolation and profiling of viable tumor cells from human ex vivo glioblastoma cultures through single-cell transcriptomics

Single-cell RNA-sequencing (scRNA-seq) is becoming a ubiquitous method in profiling the cellular transcriptomes of both malignant and non-malignant cells from the human brain. Here, we present a protocol to isolate viable tumor cells from human ex vivo glioblastoma cultures for single-cell transcriptomic analysis. We describe steps including surgical tissue collection, sectioning, culturing, primary tumor cells inoculation, growth tracking, fluorescence-based cell sorting, and population-enriched scRNA-seq. This comprehensive methodology empowers in-depth understanding of brain tumor biology at the single-cell level. For complete details on the use and execution of this protocol, please refer to Ravi et al.1.

Clinical Validation of Stimulated Raman Histology for Rapid Intraoperative Diagnosis of Central Nervous System Tumors

Stimulated Raman histology (SRH) is an ex vivo optical imaging method that enables microscopic examination of fresh tissue intraoperatively. The conventional intraoperative method uses frozen section analysis, which is labor and time intensive, introduces artifacts that limit diagnostic accuracy, and consumes tissue. SRH imaging allows rapid microscopic imaging of fresh tissue, avoids tissue loss, and enables remote telepathology review. This improves access to expert neuropathology consultation in both low- and high-resource practices. We clinically validated SRH by performing a blinded, retrospective two-arm telepathology study to clinically validate SRH for telepathology at our institution. Using surgical specimens from 47 subjects, we generated a data set composed of 47 SRH images and 47 matched whole slide images (WSIs) of formalin-fixed, paraffin-embedded tissue stained with hematoxylin and eosin, with associated intraoperative clinicoradiologic information and structured diagnostic questions. We compared diagnostic concordance between WSI and SRH-rendered diagnoses. Also, we compared the 1-year median turnaround time (TAT) of intraoperative conventional neuropathology frozen sections with prospectively rendered SRH-telepathology TAT. All SRH images were of sufficient quality for diagnostic review. A review of SRH images showed high accuracy in distinguishing glial from nonglial tumors (96.5% SRH vs 98% WSIs) and predicting final diagnosis (85.9% SRH vs 93.1% WSIs). SRH-based diagnosis and WSI-permanent section diagnosis had high concordance (κ = 0.76). The median TAT for prospectively SRH-rendered diagnosis was 3.7 minutes, approximately 10-fold shorter than the median frozen section TAT (31 minutes). The SRH-imaging procedure did not affect ancillary studies. SRH generates diagnostic virtual histologic images with accuracy comparable to conventional hematoxylin and eosin-based methods in a rapid manner. Our study represents the largest and most rigorous clinical validation of SRH to date. It supports the feasibility of implementing SRH as a rapid method for intraoperative diagnosis complementary to conventional pathology laboratory methods.

Stimulated Raman histology for histological evaluation of oral squamous cell carcinoma

OBJECTIVES

To investigate whether in patients undergoing surgery for oral squamous cell carcinoma, stimulated Raman histology (SRH), in comparison with H&E-stained frozen sections, can provide accurate diagnoses regarding neoplastic tissue and sub-classification of non-neoplastic tissues.

MATERIALS AND METHODS

SRH, a technology based on Raman scattering, was applied to generate digital histopathologic images of 80 tissue samples obtained from 8 oral squamous cell carcinoma (OSCC) patients. Conventional H&E-stained frozen sections were then obtained from all 80 samples. All images/sections (SRH and H&E) were analyzed for squamous cell carcinoma, normal mucosa, connective tissue, muscle tissue, adipose tissue, salivary gland tissue, lymphatic tissue, and inflammatory cells. Agreement between SRH and H&E was evaluated by calculating Cohen's kappa. Accuracy of SRH compared to H&E was quantified by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as well as area under the receiver operating characteristic curve (AUC).

RESULTS

Thirty-six of 80 samples were classified as OSCC by H&E-based diagnosis. Regarding the differentiation between neoplastic and non-neoplastic tissue, high agreement between H&E and SRH (kappa: 0.880) and high accuracy of SRH (sensitivity: 100%; specificity: 90.91%; PPV: 90.00%, NPV: 100%; AUC: 0.954) were demonstrated. For sub-classification of non-neoplastic tissues, SRH performance was dependent on the type of tissue, with high agreement and accuracy for normal mucosa, muscle tissue, and salivary glands.

CONCLUSION

SRH provides high accuracy in discriminating neoplastic and non-neoplastic tissues. Regarding sub-classification of non-neoplastic tissues in OSCC patients, accuracy varies depending on the type of tissue examined.

CLINICAL RELEVANCE

This study demonstrates the potential of SRH for intraoperative imaging of fresh, unprocessed tissue specimens from OSCC patients without the need for sectioning or staining.

Intraoperative microscopic autofluorescence detection and characterization in brain tumors using stimulated Raman histology and two-photon fluorescence

Introduction

The intrinsic autofluorescence of biological tissues interferes with the detection of fluorophores administered for fluorescence guidance, an emerging auxiliary technique in oncological surgery. Yet, autofluorescence of the human brain and its neoplasia is sparsely examined. This study aims to assess autofluorescence of the brain and its neoplasia on a microscopic level by stimulated Raman histology (SRH) combined with two-photon fluorescence.

Methods

With this experimentally established label-free microscopy technique unprocessed tissue can be imaged and analyzed within minutes and the process is easily incorporated in the surgical workflow. In a prospective observational study, we analyzed 397 SRH and corresponding autofluorescence images of 162 samples from 81 consecutive patients that underwent brain tumor surgery. Small tissue samples were squashed on a slide for imaging. SRH and fluorescence images were acquired with a dual wavelength laser (790 nm and 1020 nm) for excitation. In these images tumor and non-tumor regions were identified by a convolutional neural network that reliably differentiates between tumor, healthy brain tissue and low quality SRH images. The identified areas were used to define regions.of- interests (ROIs) and the mean fluorescence intensity was measured.

Results

In healthy brain tissue, we found an increased mean autofluorescence signal in the gray (11.86, SD 2.61, n=29) compared to the white matter (5.99, SD 5.14, n=11, p<0.01) and in the cerebrum (11.83, SD 3.29, n=33) versus the cerebellum (2.82, SD 0.93, n=7, p<0.001), respectively. The signal of carcinoma metastases, meningiomas, gliomas and pituitary adenomas was significantly lower (each p<0.05) compared to the autofluorescence in the cerebrum and dura, and significantly higher (each p<0.05) compared to the cerebellum. Melanoma metastases were found to have a higher fluorescent signal (p<0.01) compared to cerebrum and cerebellum.

Discussion

In conclusion we found that autofluorescence in the brain varies depending on the tissue type and localization and differs significantly among various brain tumors. This needs to be considered for interpreting photon signal during fluorescence-guided brain tumor surgery.

Technical report: surgical preparation of human brain tissue for clinical and basic research

BACKGROUND

The study of the distinct structure and function of the human central nervous system, both in healthy and diseased states, is becoming increasingly significant in the field of neuroscience. Typically, cortical and subcortical tissue is discarded during surgeries for tumors and epilepsy. Yet, there is a strong encouragement to utilize this tissue for clinical and basic research in humans. Here, we describe the technical aspects of the microdissection and immediate handling of viable human cortical access tissue for basic and clinical research, highlighting the measures needed to be taken in the operating room to ensure standardized procedures and optimal experimental results.

METHODS

In multiple rounds of experiments (n = 36), we developed and refined surgical principles for the removal of cortical access tissue. The specimens were immediately immersed in cold carbogenated N-methyl-D-glucamine-based artificial cerebrospinal fluid for electrophysiology and electron microscopy experiments or specialized hibernation medium for organotypic slice cultures.

RESULTS

The surgical principles of brain tissue microdissection were (1) rapid preparation (<1 min), (2) maintenance of the cortical axis, (3) minimization of mechanical trauma to sample, (4) use of pointed scalpel blade, (5) avoidance of cauterization and blunt preparation, (6) constant irrigation, and (7) retrieval of the sample without the use of forceps or suction. After a single round of introduction to these principles, multiple surgeons adopted the technique for samples with a minimal dimension of 5 mm spanning all cortical layers and subcortical white matter. Small samples (5-7 mm) were ideal for acute slice preparation and electrophysiology. No adverse events from sample resection were observed.

CONCLUSION

The microdissection technique of human cortical access tissue is safe and easily adoptable into the routine of neurosurgical procedures. The standardized and reliable surgical extraction of human brain tissue lays the foundation for human-to-human translational research on human brain tissue.

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.

Advances, technological innovations, and future prospects in stereotactic brain biopsies

Stereotactic brain biopsy is one of the most frequently performed brain surgeries. This review aimed to expose the latest cutting-edge and updated technologies and innovations available to neurosurgeons to safely perform stereotactic brain biopsy by minimizing the risks of complications and ensuring that the procedure is successful, leading to a histological diagnosis. We also examined methods for improving preoperative, intraoperative, and postoperative workflows. We performed a comprehensive state-of-the-art literature review. Intraoperative histology, fluorescence, and imaging techniques appear as smart tools to improve the diagnostic yield of biopsy. Constant innovations such as optical methods and augmented reality are also being made to increase patient safety. Robotics and integrated imaging techniques provide an enhanced intraoperative workflow. Patients' management algorithms based on early discharge after biopsy optimize the patient's personal experience and make the most efficient possible use of the available hospital resources. Many new trends are emerging, constantly improving patient care and safety, as well as surgical workflow. A parameter that must be considered is the cost-effectiveness of these devices and the possibility of using them on a daily basis. The decision to implement a new instrument in the surgical workflow should also be dependent on the number of procedures per year, the existing stereotactic equipment, and the experience of each center. Research on patients' postbiopsy management is another mandatory approach to enhance the safety profile of stereotactic brain biopsy and patient satisfaction, as well as to reduce healthcare costs.

Surgical Treatment of Glioblastoma: State-of-the-Art and Future Trends

Glioblastoma (GBM) is a highly aggressive disease and is associated with poor prognosis despite treatment advances in recent years. Surgical resection of tumor remains the main therapeutic option when approaching these patients, especially when combined with adjuvant radiochemotherapy. In the present study, we conducted a comprehensive literature review on the state-of-the-art and future trends of the surgical treatment of GBM, emphasizing topics that have been the object of recent study.

From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives

In recent years, Raman spectroscopy has been more and more frequently applied to address research questions in neuroscience. As a non-destructive technique based on inelastic scattering of photons, it can be used for a wide spectrum of applications including neurooncological tumor diagnostics or analysis of misfolded protein aggregates involved in neurodegenerative diseases. Progress in the technical development of this method allows for an increasingly detailed analysis of biological samples and may therefore open new fields of applications. The goal of our review is to provide an introduction into Raman scattering, its practical usage and also commonly associated pitfalls. Furthermore, intraoperative assessment of tumor recurrence using Raman based histology images as well as the search for non-invasive ways of diagnosis in neurodegenerative diseases are discussed. Some of the applications mentioned here may serve as a basis and possibly set the course for a future use of the technique in clinical practice. Covering a broad range of content, this overview can serve not only as a quick and accessible reference tool but also provide more in-depth information on a specific subtopic of interest.

Neuropathological interpretation of stimulated Raman histology images of brain and spine tumors: part B

Intraoperative histopathological examinations are routinely performed to provide neurosurgeons with information about the entity of tumor tissue. Here, we quantified the neuropathological interpretability of stimulated Raman histology (SRH) acquired using a Raman laser imaging system in a routine clinical setting without any specialized training or prior experience. Stimulated Raman scattering microscopy was performed on 117 samples of pathological tissue from 73 cases of brain and spine tumor surgeries. A board-certified neuropathologist — novice in the interpretation of SRH — assessed image quality by scoring subjective tumor infiltration and stated a diagnosis based on the SRH images. The diagnostic accuracy was determined by comparison to frozen hematoxylin–eosin (H&E)-stained sections and the ground truth defined as the definitive neuropathological diagnosis. The overall SRH imaging quality was rated high with the detection of tumor cells classified as inconclusive in only 4.2% of all images. The accuracy of neuropathological diagnosis based on SRH images was 87.7% and was non-inferior to the current standard of fast frozen H&E-stained sections (87.3 vs. 88.9%, p = 0.783). We found a substantial diagnostic correlation between SRH-based neuropathological diagnosis and H&E-stained frozen sections (κ = 0.8). The interpretability of intraoperative SRH imaging was demonstrated to be equivalent to the current standard method of H&E-stained frozen sections. Further research using this label-free innovative alternative vs. conventional staining is required to determine to which extent SRH-based intraoperative decision-making can be streamlined in order to facilitate the advancement of surgical neurooncology.