Chandler, James P.

BACKGROUND AND OBJECTIVES: The artery of Davidoff and Schechter (ADS) is a dural branch of the posterior cerebral artery, supplying the falcotentorial junction. It is not commonly depicted on angiography due to its small size; thus, its visibility is usually indicative of a pathology, such as a dural arteriovenous fistula (DAVF). Recognizing the ADS as a feeder could be vital in enhancing management success, and understanding its anatomy is important for preventing complications during embolization or microsurgery. We present a scoping review of DAVFs fed by the ADS followed by an illustrative case report encountered at our institution. METHODS: An extensive literature review was conducted using PubMed, Embase, Web of Science, Scopus, and Cochrane databases. The search included the keywords: “Davidoff,” “Schechter,” “Davidoff and Schechter,” “dural branch,” “tentorial branch,” “medial branch,” and “meningeal branch,” in combination with “arteriovenous fistula,” “posterior cerebral artery,” and “superior cerebellar artery.” Only articles published in English or Spanish were included, with no restriction on publication date. Case reports and series were further screened based on best relevance. Data extraction and synthesis were performed in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. RESULTS: A total of 16 articles describing 32 cases of DAVFs supplied by the ADS were identified. The most common onset was headache (43.8%), followed by altered mental status (21.2%). Most DAVFs were classified as high grade based on Borden/Cognard scales. Transarterial embolization was the most used treatment (81.3%), achieving complete occlusion in 76.9% of cases. Among patients with complete occlusion, approximately half experienced full resolution of symptoms. In a mean follow-up of 27.4 months (IQR: 30), 2 cases reported complications due to reflux of the embolic agent. CONCLUSION: A comprehensive understanding of the ADS's angioarchitecture is essential to take full advantage of this artery's features in the treatment, mitigating potential complications and optimizing patient outcomes.

ABSTRACT Meningiomas are the most common primary brain tumors and, despite their benign reputation, often behave aggressively. Meningiomas are morphologically heterogeneous, yet the full significance of their histologic diversity is unclear. This is in large part because many features are not readily quantifiable by traditional observer-based light microscopy. Molecular testing improves prognostic stratification, but is not universally accessible. We therefore sought to determine whether an artificial intelligence (AI)-trained program could predict specific genomic and epigenomic patterns in meningiomas, and whether it could extract more prognostic information out of standard hematoxylin and eosin (H&E) histopathology than the current WHO classification. To do this, we developed Morphologic Set Enrichment (MSE), an interpretable computational pathology framework that quantifies statistical enrichment of morphologic patterns, cells, and tissue architecture from H&E whole-slide images. The MSE meningioma histology program was able to accurately predict DNA methylation subtypes and concurrent chromosome 1p/22q losses, in the process identifying specific morphologic patterns associated with key genomic and epigenomic alterations. It also added prognostic value independent of standard clinical and pathological variables. These results demonstrate that AI-based quantitative morphologic profiling can capture clinically and biologically relevant information that redefines risk stratification for meningiomas, incorporating histological information not included in existing grading schemes.

Abstract Background Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07–0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37–0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.

AbstractNeuro-oncology encompasses a broad field focusing on an array of neoplasms, many of which can mimic several diseases. Neurologists will often be involved in the initial diagnostic evaluation and management of these patients. Their insight is central to optimizing the diagnostic yield and providing high-level clinical care. Several neuro-oncologic cases are reviewed with a goal of increasing the understanding of these diseases in a clinically relevant manner and providing updates on the contemporary thinking in the subspecialty.