Bendok, Bernard R.

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 High-grade gliomas (HGGs) are aggressive tumors with poor outcomes and limited treatment options. Here, we combined genomic and transcriptomic tumor profiling with drug testing in a patient-derived 3-dimensional culture model to identify individualized treatments and predictive biomarkers. Activity of single agents targeting frequently dysregulated glioma pathways was relatively poor ex vivo and generally reflected historical patient data. However, compounds targeting PI3K, epigenetic, and survival/senescence signaling were effective in some cases. Drug sensitivity correlated with transcriptional rather than genomic features and suggested heterogeneity as a resistance mechanism. Bromodomain and extraterminal domain inhibition was particularly effective in tumors enriched in the mesenchymal transcriptional subtype, promoted proneural transition, and was overcome by upregulated PI3K signaling. Notably, combinations were largely effective, with 6 strategies exhibiting stronger efficacy than corresponding single agents in most cases (58-77%). This study identifies HGG vulnerabilities and associated biomarkers, resistance mechanisms, and effective combination strategies that warrant further clinical validation. Graphical Abstract

Abstract High-grade gliomas (HGG) have significant spatial heterogeneity, complicating therapeutic approaches. While multiparametric MRI (mpMRI) enables noninvasive assessment of this heterogeneity, most predictive models lack biological interpretability when used to classify or predict outcomes. Here, we developed an unsupervised framework that identifies novel mpMRI feature patterns through dimensional reduction, linking them to clinically relevant molecular signatures. We analyzed 18 advanced mpMRI features from 73 stereotactic biopsies across 20 HGG patients. Imaging data were normalized, followed by dimensional reduction using an iterative UMAP across varying parameters. Density-based clustering was used to generate stable image-derived clusters through consensus co-clustering. RNA-seq data from matched biopsies were analyzed using DESeq2 and GSEA to identify differentially expressed genes and enriched pathways. We then constructed a network linking specific imaging features to these transcriptional signatures. Validation was performed in an independent cohort, allowing us to extend clusters across full brain mpMRI. Unsupervised clustering of mpMRI features produced nine stable image-based clusters. These clusters were not fully explained by contrast enhancement, metabolic class, or tumor purity, shown by intraclass correlation (ICC) analyses, suggesting they capture novel biological variation. Transcriptomic analysis showed each cluster was associated with distinct gene expression patterns and enriched biological pathways, including neuronal differentiation, immune chemotaxis, and chromatin remodeling. Key imaging features (sRCBV, MTT, K2)—combined in cluster-specific patterns—enabled construction of an interpretable imaging–transcriptomic network. This was validated in an independent cohort. We then applied to full brain mpMRI, mapping spatially distinct regions to biological trends and patient outcomes. Addressing a research gap in imaging HGG, we present an interpretable network that connects specific patterns of mpMRI to clinically meaningful molecular signatures. This approach advances our understanding of tumor heterogeneity and supports future translational work in precision patient care.