Faculty Bibliography

BACKGROUND/UNASSIGNED:Hyperglycemia has been associated with worse survival in glioblastoma. Attempts to lower glucose yielded mixed responses which could be due to molecularly distinct GBM subclasses. METHODS/UNASSIGNED:Clinical, laboratory, and molecular data on 89 IDH-wt GBMs profiled by clinical next-generation sequencing and treated with Stupp protocol were reviewed. IDH-wt GBMs were sub-classified into RTK I (Proneural), RTK II (Classical) and Mesenchymal subtypes using whole-genome DNA methylation. Average glucose was calculated by time-weighting glucose measurements between diagnosis and last follow-up. RESULTS/UNASSIGNED:= .02). Methylation clustering did not identify unique signatures associated with high or low glucose levels. Metabolomic analysis of 23 tumors showed minimal variation across metabolites without differences between molecular subclasses. CONCLUSION/UNASSIGNED:Higher average glucose values were associated with poorer OS in RTKI and Mesenchymal IDH-wt GBM, but not RTKII. There were no discernible epigenetic or metabolomic differences between tumors in different glucose environments, suggesting a potential survival benefit to lowering systemic glucose in selected molecular subtypes.

Safely maximizing extent of resection has become the central goal in glioma surgery. Especially in eloquent cortex, the goal of maximal resection is balanced with neurological risk. As new technologies emerge in the field of neurosurgery, the standards for maximal safe resection have been elevated. Fluorescence-guided surgery, intraoperative magnetic resonance imaging, and microscopic imaging methods are among the most well-validated tools available to enhance the level of accuracy and safety in glioma surgery. Each technology uses a different characteristic of glioma tissue to identify and differentiate tumor tissue from normal brain and is most effective in the context of anatomic, connectomic, and neurophysiologic context. While each tool is able to enhance resection, multiple modalities are often used in conjunction to achieve maximal safe resection. This paper reviews the mechanism and utility of the major adjuncts available for use in glioma surgery, especially in tumors within eloquent areas, and puts forth the foundation for a unified approach to how leverage currently available technology to ensure maximal safe resection.

ABSTRACT:The intersection of biology and technology has led to many advancements for the field of neurosurgery. Molecular developments have led to the identification of specific mutations, allowing for more accurate discussions in regard to prognosis and treatment effect. Even amid the progress from basic science benchwork, malignant gliomas continue to have a bleak natural history in lieu of the resistance to chemotherapy and the diffuse nature of the disease, leaving room for further research to discover more effective treatment modalities. Novel imaging methods, including the emerging field of radiogenomics, involve the merging of molecular and radiographic data, enabling earlier, detailed molecular diagnoses and improved surveillance of this pathology. Furthermore, surgical advancements have led to safer and more extensive resections. This review aims to delineate the various advancements in the many facets that are used daily in the care of our glioma population, specifically pertaining to its biology, imaging modalities, and perioperative adjuncts used in the operating room.

Patients with glioblastoma (GBM) need bold new approaches to their treatment, yet progress has been hindered by a relative inability to dynamically track treatment response, mechanisms of resistance, evolution of targetable mutations, and changes in mutational burden. We are writing on behalf of a multidisciplinary group of academic neuro-oncology professionals who met at the collaborative Christopher Davidson Forum at Washington University in St Louis in the fall of 2019. We propose a dramatic but necessary change to the routine management of patients with GBM to advance the field: to routinely biopsy recurrent GBM at the time of presumed recurrence. Data derived from these samples will identify true recurrence vs treatment effect, avoid treatments with little chance of success, enable clinical trial access, and aid in the scientific advancement of our understanding of GBM.

PURPOSE/OBJECTIVE:Resting state functional magnetic resonance imaging (rsfMRI) is an emerging tool to explore the functional connectivity of different brain regions. We aimed to assess the disruption of functional connectivity of the Default Mode Network (DMN), Dorsal Attention Network(DAN) and Fronto-Parietal Network (FPN) in patients with glial tumors. METHODS:rsfMRI data acquired on 3T-MR of treatment-naive glioma patients prospectively recruited (2015-2019) and matched controls from the 1000 functional-connectomes-project were analyzed using the CONN functional toolbox. Seed-Based Connectivity Analysis (SBCA) and Independent Component Analysis (ICA, with 10 to 100 components) were performed to study reliably the three networks of interest. RESULTS:). For the FPN, increased connectivity was noted in the precuneus, posterior cingulate gyrus, and frontal cortex. No difference in the connectivity of the networks of interest was demonstrated between low- and high-grade gliomas, as well as when stratified by their IDH1-R132H (isocitrate dehydrogenase) mutation status. CONCLUSION/CONCLUSIONS:Altered functional connectivity is reliably found with SBCA and ICA in the DMN, DAN, and FPN in glioma patients, possibly explained by decreased connectivity between the cerebral hemispheres across the corpus callosum due to disruption of the connections.

INTRODUCTION/BACKGROUND:Label-free Raman-based imaging techniques create the possibility of bringing chemical and histologic data into the operation room. Relying on the intrinsic biochemical properties of tissues to generate image contrast and optical tissue sectioning, Raman-based imaging methods can be used to detect microscopic tumor infiltration and diagnose brain tumor subtypes. METHODS:Here, we review the application of three Raman-based imaging methods to neurosurgical oncology: Raman spectroscopy, coherent anti-Stokes Raman scattering (CARS) microscopy, and stimulated Raman histology (SRH). RESULTS:bonds). Coherent Raman imaging, including CARS and stimulated Raman scattering microscopy, has been shown to detect microscopic brain tumor infiltration in fresh brain tumor specimens with submicron image resolution. Advances in fiber-laser technology have allowed for the development of intraoperative SRH as well as artificial intelligence algorithms to facilitate interpretation of SRH images. With molecular diagnostics becoming an essential part of brain tumor classification, preliminary studies have demonstrated that Raman-based methods can be used to diagnose glioma molecular classes intraoperatively. CONCLUSIONS:These results demonstrate how label-free Raman-based imaging methods can be used to improve the management of brain tumor patients by detecting tumor infiltration, guiding tumor biopsy/resection, and providing images for histopathologic and molecular diagnosis.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery¹. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive^2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce⁴. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)^5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)². In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

We recently developed and validated a bedside tissue-to-diagnosis pipeline using stimulated Raman histology (SRH), a label-free optical imaging method, and deep convolutional neural networks (CNN) in prospective clinical trial. Our CNN learned a hierarchy of interpretable histologic features found in the most common brain tumors and was able to accurately segment cancerous regions in SRH images.