Faculty Bibliography

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.

BACKGROUND:Niemann-Pick disease type C is a fatal and progressive neurodegenerative disorder characterized by the accumulation of unesterified cholesterol in late endosomes and lysosomes. We sought to develop new therapeutics for this disorder by harnessing the body's endogenous cholesterol scavenging particle, high-density lipoprotein (HDL). METHODS:Here we design, optimize, and define the mechanism of action of synthetic HDL (sHDL) nanoparticles. RESULTS:We demonstrate a dose-dependent rescue of cholesterol storage that is sensitive to sHDL lipid and peptide composition, enabling the identification of compounds with a range of therapeutic potency. Peripheral administration of sHDL to Npc1 I1061T homozygous mice mobilizes cholesterol, reduces serum bilirubin, reduces liver macrophage size, and corrects body weight deficits. Additionally, a single intraventricular injection into adult Npc1 I1061T brains significantly reduces cholesterol storage in Purkinje neurons. Since endogenous HDL is also a carrier of sphingomyelin, we tested the same sHDL formulation in the sphingomyelin storage disease Niemann-Pick type A. Utilizing stimulated Raman scattering microscopy to detect endogenous unlabeled lipids, we show significant rescue of Niemann-Pick type A lipid storage. CONCLUSIONS:Together, our data establish that sHDL nanoparticles are a potential new therapeutic avenue for Niemann-Pick diseases.

Background/UNASSIGNED:Cognitive and language dysfunction is common among patients with glioma and has a significant impact on survival and health-related quality of life (HRQOL). Little is known about the factors that make individual patients more or less susceptible to the cognitive sequelae of the disease. A better understanding of the individual and population characteristics related to cognitive function in glioma patients is required to appropriately stratify patients, prognosticate, and develop more efficacious treatment regimens. There is evidence that allelic variation among genes involved in neurotransmission and synaptic plasticity are related to neurocognitive performance in states of health and neurologic disease. Methods/UNASSIGNED:, rs4680) with neurocognitive function and ability to return to work in glioma patients at diagnosis and at 3 months. We developed a functional score based on the number of high-performance alleles that correlates with the capacity for patients to return to work. Results/UNASSIGNED:Patients with higher-performing alleles have better scores on neurocognitive testing with the Repeatable Battery for the Assessment of Neuropsychological Status and Stroop test, but not the Trail Making Test. Conclusions/UNASSIGNED:A better understanding of the genetic contributors to neurocognitive performance in glioma patients and capacity for functional recovery is necessary to develop improved treatment strategies based on patient-specific factors.