Citations

Renovascular hypertension is the most common cause of secondary hypertension, and radiologist awareness of the various imaging appearances and various causes is crucial for accurate diagnosis and guiding treatment.

After successfully implementing an acute care telemedicine program internally, health care institutions often consider expanding and scaling services externally beyond the initial organizational network. This report outlines important considerations when expanding acute care telemedicine services externally, including cultural alignment and agility, practice context, staffing models, telemedicine technology selection, interrelated services, legal and compliance considerations, quality assurance, risk management, financing, and integration with medical education. These factors may inform program leaders of major planning considerations, potential barriers, and could influence strategic planning. Prospectively identifying and resolving challenges prior to contracting with new external sites will facilitate smooth, successful implementation and high utilization of the acute care telemedicine service.

Background: Meniscal preservation has been demonstrated to contribute to long-term knee health and has been a successful intervention in isolation and in patients with anterior cruciate ligament reconstruction (ACLR). The long-term results of meniscal repair in the setting of revision ACLR have yet to be documented. Purpose: To report the incidence of meniscal repair failures at the 6-year follow-up in a cohort of patients who underwent concurrent revision ACLR and primary meniscal repair. Study Design: Prospective cohort study; Level of evidence, 2. Methods: All revision ACLRs with concomitant primary meniscal repair cases from a multicenter group between 2006 and 2011 were selected. Six-year follow-up was obtained to determine whether any subsequent surgery had occurred since their initial revision ACLR. If so, operative reports were obtained, whenever possible, to verify pathological condition and treatment. Results: In total, 221 patients from 1234 revision ACLRs underwent concurrent primary meniscal repairs (18% of the cohort). There were 238 repairs performed: 173 medial and 65 lateral. The majority of these repairs (n = 181; 76%) were performed with an all-inside technique. Six-year surgical follow-up was obtained in 77% (171/221) of the cohort, or 189 of 238 (79%) of the repairs (136 medial, 53 lateral). The meniscal repair failure rate, defined as reoperation, was 16% (31/189) at 6 years. Of the 31 failures, 28 were medial (24 all-inside, 4 inside-out; 28/136 = 20.6% failure rate) and 3 were lateral (2 all-inside, 1 inside-out; 3/53 = 5.7% failure rate). Three medial failures were treated in conjunction with a subsequent repeat revision ACLR. Medial tears underwent reoperation for failure at a significantly higher rate than lateral tears (20.6% vs 5.7%; P = .01) and had a significantly shorter survival time compared with lateral tears ( P = .02). No difference was found between the failure and nonfailure groups when it came to tear type, tear length, repair technique utilized, suture/implant type, or number of sutures used between the 2 groups. Conclusion: Meniscal repair in the revision ACLR setting has a 16% failure rate at 6 years. Failure rates for medial tears (20.6%) were found to be higher than that for lateral tears (5.7%), which aligns with previous studies in both the revision and primary ACLR setting.

The primary purpose of this study was to examine the acute effects of a multi-ingredient preworkout supplement (MIPS) on isometric, concentric, and eccentric peak torque and electromyographic (EMG) responses of the leg extensors during a fatiguing isokinetic protocol. Thirteen male subjects (mean age ± SD = 22.9 ± 2.2 years) were assigned in crossover fashion to ingest an MIPS or placebo before an isokinetic protocol that consisted of 30 maximal, concentric and eccentric muscle actions with EMG signals recorded from the vastus lateralis, rectus femoris, and vastus medialis muscles. Immediately before (PRE) and after (POST) the isokinetic fatigue protocol, subjects were assessed for isometric peak torque. The MIPS condition resulted in greater isometric (205 ± 48 vs. 185 ± 44 N·m, p = 0.04) and concentric (121 ± 34 vs. 103 ± 27 N·m, p = 0.015) torque values versus placebo (collapsed across time). For eccentric peak torque as well as EMG amplitude and frequency values, there were no significant (p > 0.05) interactions or main effects for each condition. These findings indicated that acute ingestion of the MIPS enhanced isometric and concentric peak torque of the leg extensors, which was not explained by changes in the EMG signal.

Abstract BACKGROUND Gaseous hydrogen sulfide (H2S), a by-product of cysteine metabolism, inhibits cancer cell behavior and growth in glioblastoma (GBM) models. H2S production capacity (HPC) is decreased in human GBM specimens compared to non-tumor controls. Thus, boosting HPC is a novel strategy for GBM treatment. Suppression of thyroid hormone signaling increases production of H2S. Hypothesis: methimazole-induced hypothyroidism will increase tumor HPC to enhance chemotherapy efficacy in GBM. GOAL Proof of concept that methimazole, by reducing thyroid hormone signaling, can increase HPC in patients with recurrent/progressive GBM (rGBM). PRIMARY ENDPOINTS 10% increase in HPC and a 1.5-fold increase in peripheral blood sulfhydration signaling. METHODS This window of opportunity trial evaluates safety, HPC in plasma and tumor, and efficacy of methimazole + chemotherapy in patients with rGBM. rGBM patients planned for a resection begin methimazole 15 mg/d 5-7 days pre-op with investigator’s choice chemotherapy added 10-28 days post-op. Patients receive combination until progression. Pre- and post-op plasma and resected tumor tissue are assayed for HPC and proteins relevant to H2S production. Key eligibility: rGBM for whom a clinically-indicated resection is planned; normal thyroid function with no history of thyroid dysfunction. Patients may have received unlimited prior regimens including bevacizumab. RESULTS To date 15 patients (8 male), median age 56 years, enrolled. The trial was amended to increase methimazole to 25 mg/d; 3 patients have received this dose. Median TSH rose from 1.3 mU/ml to 20.5 at cycle 6. Free T3 and T4 did not fall significantly. Median plasma HPC rose 3-fold while tumor HPC did not rise significantly. No treatment-related grade 3-5 toxicities. Efficacy: PFS6 – 15% and OS12 – 21%. CONCLUSIONS Induction of subclinical hypothyroidism is feasible and safe in rGBM with significant increases in plasma but not tumor HPC. Further tumor correlative data for the dose-escalated methimazole will be presented.

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.

Abstract Computer vision now enables deep learning models to link histopathologic features to molecular features in a quantitative manner not previously achievable by human pathologists. However, human interpretability is often limited. We trained a deep neural network to predict gene expression from digital whole slide images (WSIs) using 848 central nervous system (CNS) tumors with paired WSI and RNA-sequencing data. We used inferred RNA expression levels as a surrogate for protein expression of the CNS tumor lineage markers GFAP, OLIG2, and SSTR2, as well as the proliferation marker MKI67 (Ki67). We established thresholds for categorizing the inferred expression levels as positive or negative based on levels observed in cross-validation testing in select tumor types to approximate routine clinical interpretation of immunohistochemical (IHC) staining. We tested the sensitivity and specificity of our ‘virtual’ IHC on an independent, multi-institutional cohort of over 2,000 CNS tumors with objective diagnostic labels derived from DNA methylation-based tumor classification. As a dichotomous variable, inferred GFAP expression showed a sensitivity of 74% and a specificity of 99% as a glial marker in a cohort of gliomas and meningiomas. In the same cohort, OLIG2 showed a sensitivity of 75% and a specificity of 97% as a glial marker, while SSTR2 showed a sensitivity of 83% and a specificity of 97% as a marker of meningioma. In a cohort of ependymomas and gliomas, virtual IHC for OLIG2 showed a sensitivity of 75% and a specificity of 82% in distinguishing ependymomas from gliomas. Finally, in a cohort of gliomas, inferred expression of MKI67 demonstrated a trend consistent with what would be expected by actual IHC, with increasing MKI67 expression with increasing tumor grade. Our model provides the basis for a human interpretable and clinically applicable deep neural network to aid human pathologists in the diagnosis and grading of CNS tumors.

Abstract BACKGROUND Glioblastoma (GBM) is a molecularly and phenotypically heterogeneous tumor, with regional invasion patterns driving treatment resistance and universal recurrence. In infiltrative regions, GBM cells adopt at least two predominant invasive phenotypes: Neuronal-enriched (NEU), with synaptic and neurodevelopment programs, and Glycolytic/Plurimetabolic (GPM), with heightened immune and metabolic activity. These subtypes differ in therapeutic vulnerability and microenvironmental interactions; features not captured by conventional contrast-enhanced (T1+C) MRI due to minimal blood-brain barrier (BBB) disruption. Our group sought to develop predictive models to distinguish variability of NEU/GPM subtypes within infiltrative GBM, utilizing graph convolutional networks (GCN), advanced multiparametric MRI (mpMRI), and spatially-matched image-localized biopsies with transcriptomic profiling. METHODS We analyzed 18 mpMRI features —including conventional, diffusion-weighted, and Dynamic Susceptibility Contrast (DSC) perfusion MRI— of 101 image-localized biopsies from MRI non-enhancing infiltrative margins of 64 GBM patient tumors. Features were hierarchically clustered per modality; each cluster was represented as a node in a Neo4j graph. Edges connected nodes sharing overlapping samples. Nodes were annotated with NEU or GPM subtype proportion. GCNs were trained to classify subtype-enriched nodes using graph topology and node features. RESULTS GCNs achieved high predictive performance on held-out test data (AUC = 0.95 for NEU, 0.88 for GPM; 70:30 train-test split) with similar 5-fold cross-validation performance and generalized well to an independent cohort (AUC > 0.85, n = 45). Importantly, performance was robust without relying on T1+C imaging, enabling prediction beyond BBB-disrupted regions. GCNs outperformed traditional classifiers (logistic regression, random forest). CONCLUSION Our results demonstrate that GCN-modeling of mpMRI features enables non-invasive prediction of invasive GBM subtypes at the tumor margin. Unlike convolutional neural networks (CNN), which rely on spatial continuity, GCNs leverage relational structure between heterogeneous imaging features, making them ideal for modeling sparse, non-grid data. This may support non-invasive regional vulnerability detection, surgical targeting, and improved response assessment.

Abstract Molecular testing has become critical components in the diagnostic classification of the central nervous system (CNS) tumors. However, these methods require substantial resources, limiting accessibility for many patients. Recent advances in artificial intelligence (AI) and computer vision empower deep learning models to infer molecular features from histopathology images, which may often be more readily available than molecular testing, to classify CNS tumors. We trained pan-CNS tumor models to predict DNA methylation and gene expression from whole-slide images (WSIs). These molecular predictions were then used in a hierarchical machine learning framework, termed Neuropath-AI, to predict nine broad tumor families and 52 tumor types using a large diverse cohort of 5,715 histopathology samples, of which 2,988 had paired DNA methylation profiling and 848 had paired RNA-sequencing. We evaluated Neuropath-AI on another large independent multi-institutional cohort of 5,516 CNS tumors, for which Neuropath-AI predicted tumor class with an associated confidence score. Neuropath-AI designated 46% of test samples as predictable with high-confidence, which it then accuracy classified in 97% of samples. It made moderate-confidence (and above) predictions for 87% of test samples, for which it achieved a diagnostic accuracy of 80% for the top-1 prediction and 86% accuracy for a top-2 predictions. A comparison with human neuropathologists showed that Neuropath-AI is comparable at classifying tumors. A second test showed that human neuropathologists were more accurate when the Neuropath-AI classifier results were provided to them, testifying to the potential translational benefit of integrating AI classification tools into the pathologist’s workflow. Our model provides the basis for a clinically applicable deep learning assistant to improve human efficiency and accuracy of CNS tumor diagnosis. The model will be made publicly available and can be readily implemented to assist human pathologists in clinical workflows, in further expanded prospective studies.

Abstract There are no approved treatments for relapsed/refractory (R/R) PCNSL. We show promising efficacy in the salvage setting using emavusertib in combination with ibrutinib, which targets the BCR and TLR pathways. These 2 major pathways are associated with activation of NF-κB. Updated safety and efficacy data of this combination in R/R PCNSL from the ongoing Phase 1/2 TakeAim Lymphoma trial (NCT03328078) will be presented. As of 01 May 2025, 33 patients with R/R PCNSL were treated with emavusertib in combination with ibrutinib. Eligible patients receive emavusertib in combination with ibrutinib until tumor progression or unacceptable toxicity. Median age was 63 years (range 43-81); 16 patients were male. Median prior anti-cancer therapy was 2 (range 1-5); 26 (78.8%) patients had prior BTKi exposure. Median duration of emavusertib/ibrutinib treatment was 51 days (range 5-798). Grade ≥ 3 treatment-related adverse events (TRAEs) were reported in 11 (33.3%) patients; neutropenia in 5 (15.2%) patients, amylase increased, lipase increased, and leukopenia in 2 (6.1%) patients each. The majority of Grade ≥ 3 TRAEs were reversible and manageable. Among the 19 BTKi-experienced patients treated for ≥ 28 days, there were 5 CRs and 2 PRs, with an ORR of 36.8%. Three patients with CR were on treatment for 10+ mos, with 1 CR patient ongoing with DOT of 26.2+ mos. Among the 5 BTKi-naïve patients treated for ≥ 28 days, 1 achieved CR and 3 had PR, with ORR of 80%; the CR patient is ongoing with DOT of 18.1+ mos. The ORR in the combined population was 45.8% (6 CRs; 5 PRs). The combination of emavusertib and ibrutinib demonstrates promising clinical activity and an acceptable safety profile in patients with R/R PCNSL. This regimen appears capable of overcoming resistance to prior BTKi therapy. Patient enrollment in this trial continues.