Faculty Bibliography

BACKGROUND AND PURPOSE/OBJECTIVE:-wt GBMs to help radiologists in differentiating them from nonmalignant mimic diagnoses (eg, encephalitis). Additionally, the histologic, genomic, and survival profiles of nonenhancing GBMs were compared with those of enhancing GBMs. MATERIALS AND METHODS/METHODS:-wt GBMs with nonmalignant mimics. Histopathologic and genomic analyses were performed on institutional cases. Overall survival between nonenhancing and enhancing GBMs was compared using Kaplan-Meier analysis. RESULTS:= .078). CONCLUSIONS:Nonenhancing GBMs demonstrate distinct MRI features that must be recognized for early diagnosis and differentiation from nonmalignant mimics. Nonenhancing GBMs demonstrated longer overall survival compared with enhancing GBMs, though they were not statistically significant.

The importance of rapid and accurate histologic analysis of surgical tissue in the operating room has been recognized for over a century. Our standard-of-care intraoperative pathology workflow is based on light microscopy and H&E histology, which is slow, resource-intensive, and lacks real-time digital imaging capabilities. Here, we describe an emerging and innovative method for intraoperative histologic analysis, called Intelligent Histology, that integrates artificial intelligence (AI) with stimulated Raman histology (SRH). SRH is a rapid, label-free, digital imaging method for real-time microscopic tumor tissue analysis. SRH generates high-resolution digital images of surgical specimens within seconds, enabling AI-driven tumor histologic analysis, molecular classification, and tumor infiltration detection. We review the scientific background, clinical translation, and future applications of intelligent histology in tumor neurosurgery. We focus on the major scientific and clinical studies that have demonstrated the transformative potential of intelligent histology across multiple neurosurgical specialties, including neurosurgical oncology, skull base, spine oncology, pediatric tumors, and peripheral nerve tumors. Future directions include the development of AI foundation models through multi-institutional datasets, incorporating clinical and radiologic data for multimodal learning, and predicting patient outcomes. Intelligent histology represents a transformative intraoperative workflow that can reinvent real-time tumor analysis for 21st century neurosurgery.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Interactions between cancer cells and their microenvironment are central to tumor formation. Regional microenvironmental variability in the brain may offer insights into essential factors in tumorigenesis. Surprisingly, a granular assessment of regional patterns of gliomagenesis has not been undertaken in the molecular era. The aim of this study was to quantitatively establish the anatomic distribution of the major molecular subtypes of adult diffuse glioma. METHODS:We retrospectively analyzed 204 isocitrate dehydrogenase (IDH)-mutant and 200 IDH-wildtype gliomas. Reproducibility was assessed in an external cohort (190 IDH-mutant, 227 IDH-wildtype), and microarray expressions from Allen Human Brain Atlas were used to compare transcriptomic profiles between IDH-mutant hotspots and coldspots. RESULTS:A total of 50.5% (103/204) of IDH-mutant tumors arose with the superior and middle frontal gyri, indicating a 3.1-fold regional enrichment relative to the volume of these gyri (P < .001). Totally, 9.5% (19/200) of IDH-wildtype tumors arose in the superior temporal gyrus with a 2.1-fold enrichment (P = .01). IDH-mutant and wildtype tumors were enriched by 4 and 4.5-fold, respectively, in the insula (both P < .001). Overall, 23.3% (24/103) of astrocytomas occurred disproportionately higher in the insula compared with oligodendrogliomas (P < .001). Transcriptomic analysis comparing the lobar hotspot (frontal lobe) to the coldspot (occipital lobe) revealed frontal enrichment of cholesterol (normalized enrichment score = 1.78) and fatty acid (normalized enrichment score = 1.94) metabolism pathways, paralleling the observed regional enrichment of IDH-mutant gliomas. CONCLUSION/CONCLUSIONS:This study identifies molecular subtype-specific glioma hotspots and may suggest that regional metabolic differences may underlie the brain's variable vulnerability to gliomagenesis. These findings provide a framework for investigating additional microenvironmental factors that drive human glioma formation.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Molecular markers such as isocitrate dehydrogenase (IDH) and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) status are essential for glioma classification and treatment planning, but their manual extraction from pathology reports creates significant research bottlenecks. This study evaluated 3 Natural Language Processing approaches with increasing computational complexity: deterministic Regular Expressions (RegEx), statistical Term Frequency-Inverse Document Frequency (TF-IDF) with logistic regression, and contextual deep learning Bidirectional Encoder Representations from Transformers (BERT). We address whether more intensive approaches provide sufficient performance benefits over simpler approaches in computational pathology research. METHODS:We analyzed pathology reports from 404 patients with glioma at Institution A and 197 at Institution B for external validation. IDH analysis included 399 (Institution A) and 193 (Institution B) patients; ATRX analysis included 361 and 130 patients, respectively. All approaches underwent identical preprocessing steps, including text normalization, terminology standardization, and context extraction. Performance was evaluated using standard classification metrics and memory usage benchmarks on internal and external validation data sets. RESULTS:Simpler approaches outperformed more intensive approaches on external validation. For IDH, Regex achieved near-perfect accuracy (99%, area under the curve [AUC] 1.000) and TF-IDF performed exceptionally (94.2%, AUC 0.984), while BlueBERT underperformed (85.2%, AUC 0.934). For ATRX, Regex achieved perfect accuracy (100%, AUC 1.000) and TF-IDF maintained high accuracy (98.0%, AUC 0.998), outperforming BERT-large (84.6%, AUC 0.931). BERT-based approaches required 1825-1953 MB of memory vs Regex (0.82-5.52 MB) and TF-IDF (17.27-34.89 MB). CONCLUSION/CONCLUSIONS:Simple Natural Language Processing approaches effectively automate molecular marker extraction from pathology reports with near-perfect accuracy while requiring minimal computational resources. This enables expanded sample sizes in retrospective studies, multi-institutional analyses of rare molecular subgroups, and accelerated biomarker research. Future work will focus on validation across larger data sets, infrastructure integration, and expansion to additional molecular markers.

Spinal tumor surgery requires rapid tissue diagnosis to guide surgical decisions and further treatment strategies, yet current intraoperative methods are time-intensive and require specialized expertise. No AI systems exist for real-time spinal tumor classification during surgery. We developed SpineXtract, the first AI-powered system for rapid intraoperative spinal tumor diagnosis using stimulated Raman histology (SRH) - a label-free Raman spectromics imaging technique without tissue processing available during surgery. We created a transformer-based classifier optimized for spinal tissue characteristics to identify common tumor types: meningioma, schwannoma, ependymoma, and metastasis. The system was tested in an international, multicenter, simulated, single-arm study using existing SRH datasets (44 patients, 142 slide-images) from three international institutions, with final pathological diagnosis as reference standard. SpineXtract achieved a 92.9% macro-average balanced accuracy (95% CI: 85.5-98.2) within 5 minutes (tumor-specific accuracy range, 84.2-98.6%), while providing quantitative microscopic feedback for granular tissue analysis. Performance remained consistent across institutions (macro balanced accuracy 91.4-92.0%) and outperformed existing brain tumor classifiers by 15.6%. Our results demonstrate clinical applicability, enabling rapid intraoperative diagnosis with performance exceeding current methods, potentially transforming intraoperative diagnostic workflows in spinal tumor surgery.

BACKGROUND:Glioblastoma intramedullary spinal cord metastasis (GISCM) is a rare sequela of high-grade astrocytoma and glioblastoma multiforme (GBM). Discrete intramedullary spinal cord metastases are less common than spinal leptomeningeal spread and may follow a more indolent course. Once identified as GISCM, palliative maximal safe resection of the tumor may be considered to alleviate neurological symptoms. Reports describing the surgical management of these rare lesions, including the use of emerging technologies that may aid in maximal safe resection, are sparse. A further understanding is also required regarding the course of disease and factors contributing to mortality in GISCM. METHODS:We reviewed the intraoperative management and clinical course of three patients treated for GISCM at our institution between 2015 and 2024. We additionally conducted a PRISMA-guided systematic literature review of PubMed Central, MEDLINE, and Bookshelf databases through May 26th, 2025, including original patient reports of GISCM from cranial astrocytoma or GBM. The disease course, management strategies, and causes of mortality in previously reported cases were analyzed. RESULTS:Our institutional cohort had a mean time to spinal metastasis of 26.2 months from diagnosis of cranial disease (range 17.5-40.5 months), with a mean survival of 9.2 months following maximal safe resection of extramedullary components (range 7-12 months). In two cases, intraoperative Stimulated Raman Histology (SRH) was employed to facilitate the rapid identification of metastatic GBM, thereby influencing surgical strategy. In one case, 5-aminolevulinic acid (5-ALA) was used to differentiate between tumor and spinal cord parenchyma, facilitating maximal safe debulking without neurological injury. Literature review identified 38 prior reported cases of GISCM, with a median time to spinal diagnosis of 11.0 months and a median survival of 3.5 months thereafter. The cause of death in the review cohort often involved multiple factors, and when analyzed for contributing factors to death, 38.7% involved cranial progression, 38.7% involved progression of spinal disease, and 29.0% involved medical complications. Gait ataxia at presentation was associated with shorter survival in review patients, potentially reflecting advanced disease with extramedullary cord compression. CONCLUSION/CONCLUSIONS:GISCM represents an entity distinct from leptomeningeal disease and may be managed in conjunction with recurrent cranial disease. Surgical debulking is a technically feasible strategy that can be safely facilitated using tools employed in the management of intracranial GBM, facilitating maximal safe resection without compromising survival.

Active nematic liquid crystals are the main structural phase of gliomas, promoting collective migration and aggression. We establish the existence of nematic order and topological defect lines and loops in 3D in vivo mouse and human glioma brain tumors. As predicted by theory, sections through the disclination lines in 3D appear as ±1/2 topological defects in 2D. In 3D, these defects either persist along disclination lines or twist as they interconvert from -1/2 to +1/2. Cell alignment exhibits quasi-long-range order, spreading throughout the tumor over distances between 300-3000 μm. In vitro -1/2 and +1/2 defects display changes in apoptosis levels, suggesting topological defects regulate glioma cell density. The large scale order of gliomas correlates with tumors' aggressive behavior. The organization of gliomas as active nematic liquid crystals provides a novel physical foundation of complex solid tumors; their deconstruction signposts potential treatments for deadly cancers.

A critical challenge in glioma treatment is detecting tumour infiltration during surgery to achieve safe maximal resection^1-3. Unfortunately, safely resectable residual tumour is found in the majority of patients with glioma after surgery, causing early recurrence and decreased survival^4-6. Here we present FastGlioma, a visual foundation model for fast (<10 s) and accurate detection of glioma infiltration in fresh, unprocessed surgical tissue. FastGlioma was pretrained using large-scale self-supervision (around 4 million images) on rapid, label-free optical microscopy, and fine-tuned to output a normalized score that indicates the degree of tumour infiltration within whole-slide optical images. In a prospective, multicentre, international testing cohort of patients with diffuse glioma (n = 220), FastGlioma was able to detect and quantify the degree of tumour infiltration with an average area under the receiver operating characteristic curve of 92.1 ± 0.9%. FastGlioma outperformed image-guided and fluorescence-guided adjuncts for detecting tumour infiltration during surgery by a wide margin in a head-to-head, prospective study (n = 129). The performance of FastGlioma remained high across diverse patient demographics, medical centres and diffuse glioma molecular subtypes as defined by the World Health Organization. FastGlioma shows zero-shot generalization to other adult and paediatric brain tumour diagnoses, demonstrating the potential for our foundation model to be used as a general-purpose adjunct for guiding brain tumour surgeries. These findings represent the transformative potential of medical foundation models to unlock the role of artificial intelligence in the care of patients with cancer.

PURPOSE/OBJECTIVE:Histopathological grading of IDH-mutant astrocytomas demonstrates limited prognostic accuracy. However, DNA methylation subclassification has demonstrated improved prognostication beyond histological grading. This study aimed to investigate the associations between imaging features, tumor volumetric data, and DNA methylation grade in IDH-mutant astrocytomas. METHODS:We analyzed imaging features and volumetric data for 72 patients diagnosed with IDH-mutant astrocytomas, who underwent preoperative MRI and DNA methylation profiling. VASARI features and multicompartmental volumetrics were evaluated. Logistic regression was used to identify imaging predictors of methylation subclass, WHO histologic grade, copy number variation (CNV), and CDKN2A/B homozygous deletion. Univariable and multivariable Cox proportional hazard models were also developed to assess these variables' influence on overall survival and progression-free survival. RESULTS:Patients were classified into 27 methylation high-grade (A_IDH_HG) and 45 methylation low-grade (A_IDH_LG) tumors. Tumor volumes and proportions varied by methylation grade, CNV status, and WHO histologic grade, but not by CDKN2A/B status. Imaging features distinguished methylation subclasses with 75% accuracy (AUC = 0.77). Methylation high-grade subclass was associated with imaging features such as midline crossing, ependymal extension, and poorly defined enhancing margins. Predictive performance for WHO histologic grade, CNV status, and CDKN2A/B deletion was moderate (AUC = 0.67, 0.69, and 0.65, respectively). Methylation grade, CDKN2A/B status, VASARI features, and proportions of edema and non-contrast enhancing tumor were significantly associated with survival. CONCLUSION/CONCLUSIONS:MRI-derived imaging features facilitate noninvasive prediction of DNA methylation subclass in IDH-mutant astrocytomas.