Citations

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 High-grade gliomas (HGGs) are aggressive, infiltrative brain tumors with poor survival outcomes and limited therapeutic options. Their spatial and molecular heterogeneity necessitates large, well-curated datasets integrating imaging, clinical, and genomic information to enable precision medicine and drive novel treatment development. Using the Medtronic StealthStation neuronavigation system, we collected over 1,000 spatially localized intraoperative biopsy samples from more than 250 patients undergoing resection for glioma at Mayo Clinic and partnering sites. Each biopsy site was recorded with 3D coordinates, labeled in real time, flash-frozen, and annotated with metadata including collection time, anatomical location, and surgical plan. Samples were processed and entered into a secure database along with the corresponding sampling coordinates, frozen pathology, whole-exome and RNA sequencing data, 5-ALA fluorescence status, patient demographics, and T1, T1Gd, T2, and T2-FLAIR MRI. This multimodal dataset supports radiogenomic research by enabling precise mapping of imaging features to molecular characteristics. A subset of this data, including 268 biopsies from 52 patients (25 male, 27 female) with reported intraoperative 5-ALA fluorescence, was used to develop a radiomics model predicting locoregional patterns of 5-ALA positivity based on preoperative MRI. Image features were extracted from the biopsy sample coordinates across multiple MRI sequences and paired with corresponding 5-ALA fluorescence status to train the model for regional fluorescence prediction. The final model achieved 83% accuracy on the training set and 86% on the validation set using a 70-15-15 data split; this included 208 samples for training and 42 for validation, stratified by patient to ensure no overlap of samples from the same individual across sets. These findings demonstrate the feasibility of predicting 5-ALA fluorescence using radiomic features from standard preoperative MRI, offering a promising noninvasive strategy for improving intraoperative decision-making and enabling more precise, personalized resections in patients with high-grade gliomas.

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 Clinical and translational research is important for health care and the growth of medical specialties. Physical medicine and rehabilitation offers many opportunities for research, but research growth in physical medicine and rehabilitation is lacking, and research resources and productivity vary across academic physical medicine and rehabilitation departments across North America. The Physiatric Research Consulting Program was developed by the Association of Academic Physiatrists to provide customized recommendations to enhance research capacity and productivity in physical medicine and rehabilitation departments. This report outlines the three components of the Physiatric Research Consulting Program, including a previsit needs assessment, an in-person visit, and a postvisit follow-up final report. The report also provides a qualitative assessment of the impact of the Physiatric Research Consulting Program, with general themes of feedback, site visit evaluations, and final report evaluations. The Physiatric Research Consulting Program was found to be valuable in identifying gaps and needs for physical medicine and rehabilitation departments, providing outside perspectives, and energizing faculty toward research growth in their departments.

Abstract Background Lymphocyte function and immune competence can be measured by proliferative response to stimuli and is useful in several clinical contexts including primary (or genetic) and secondary immunodeficiencies (e.g., immunosuppression following transplantation). Proliferation assays have traditionally been prepared manually due to the difficulty in maintaining the sterile environment required to prevent bacterial and fungal impact on cell growth. On most automated liquid handlers, the sample and reagents are placed inside a cabinet whose inner surfaces include rails, pullies, pumps, and circuitry that face the sample and are not amenable to sterilization. The Andrew+ (Waters, MA) is a small surface cleanable liquid handler with all machinery enclosed. We demonstrate here the use of an Andrew+ placed in a biological safety cabinet (BSC) to perform sterile assay setup. Methods Peripheral blood monocytes (PBMCs) from five individuals were manually generated via buffy coat isolation, then brought to standardized concentration. An Andrew+ was placed in a 4’ BSC and all surfaces disinfected with Oxivir Tb (Diversey, SC). The Andrew+ prepared three serial dilutions of two stimulant reagents (phytohemagglutinin (PHA) and pokeweed (PWM)), then dispensed stimulant and control reagents horizontally and PBMCs vertically to a 48 well plate. This process was duplicated manually from the same specimens to create a reference plate. The plates were incubated at 37°C and 5% CO2 for three days, then pulsed with EdU (Thermo Fisher, MA) and incubated for one additional day. Cells were harvested and counted via flow cytometry. Overall time and hands-on time were measured. Results A total of five samples were tested inside a using both manual and automation setup under eight conditions including with isotype control, media without stimulant, three concentrations of PHA, and three concentrations of PWM. The percent proliferation for various cell types as induced by each stimulant was calculated from the difference of the highest stimulant signal from the background (media-only) signal. Comparing the automated and manual method results showed a slope of 1.0217 and R2 of 0.9966. For a simulated run of 6 samples, technologist time for automated steps was under 8 minutes with a total time of 56 minutes. When these steps were performed manually, the time was 30 minutes. Automation increased total time by 26 minutes but saved 22 minutes of technologist hands-on time. Conclusion The Andrew+ was able to perform sterile setup of a cell culture assay. While slower overall than manual processing, technologist time was reduced. Ergonomic, inter-tech variability, and sample transcription error issues were subjectively observed to be less likely. The Andrew+ can work with up to four “dominos” (reagent or tip blocks) in width and up to three dominos in depth. Non-custom, standard depth BSCs from a variety of manufacturers (Labconco, Nuaire, ThermoFisher) have room for one domino in depth so, except for timings performed in this evaluation, all steps were broken down to be performed with a max of four dominos. Efficient sterile use of the system would require an extended depth BSC.

ABSTRACT Hepatopulmonary syndrome (HPS) affects up to one‐third of patients with advanced chronic liver disease (ACLD) and is an independent risk factor for increased mortality. While HPS often improves following liver transplantation, patients with severe or very severe HPS—classified by partial pressure of arterial oxygen (PaO 2 ) <60 mmHg—face higher post‐transplant complications and mortality. However, evidence suggests that even patients with severe HPS may benefit from liver transplantation when performed at high‐volume centers. To assess post‐transplant outcomes in this population, we conducted a retrospective chart review of 20 patients diagnosed with severe or very severe HPS who underwent liver transplantation at Mayo Clinic Hospital in Arizona between 2014 and 2024. The most common post‐transplant complication was persistent hypoxemia, managed on a case‐by‐case basis with various treatments, though no single approach proved consistently superior. All patients who received transplants demonstrated improvement; 84% of patients ultimately discontinued supplemental oxygen, with a mean time of 250 days (range: 0–1113 days). Time was the key factor in recovery. Notably, no deaths in our cohort were directly attributed to HPS. These findings support liver transplantation as a viable treatment option for HPS, even in severe cases, and underscore the importance of experienced transplant centers in optimizing patient outcomes.

ABSTRACT Capillary malformation-arteriovenous type 2 syndrome (CM-AVM2) is a rare, genetic vascular disorder associated with abnormal capillary malformations and arteriovenous malformations. We present a case of CM-AVM2 with refractory diarrhea and failure to thrive due to aberrant, telangiectatic capillaries, and subepithelial collagen deposition found throughout the GI tract on bidirectional endoscopy. The patient was recalcitrant to standard therapies, so bevacizumab was trialed, but the patient expired. This is the first case of CM-AVM2 with diffuse GI mucosal involvement resulting in malabsorption. There is no treatment for this pathology, but treatment with bevacizumab or a mammalian target of rapamycin inhibitor may be efficacious.

Background: Evaluation of the performance of Cobas ® Pulse (Roche Diagnostics GmbH, Mannheim, Germany), a new blood glucose (BG) monitoring system (BGMS; referred to here as BGMSA) intended for point-of-care testing using samples from patients in diverse clinical settings by intended point-of-care test operators. Methods: Arterial, capillary, venous, or heel stick whole blood (WB) samples from patients in non-critical and critical care settings were collected and analyzed using BGMSA and the Nova StatStrip ® BGMS (Nova Biomedical, Waltham, MA; referred to here as BGMSB), and a hexokinase comparator (Cobas 6000 Analyzer Series; Roche Diagnostics GmbH). The blood glucose measurement accuracy was assessed by the Food and Drug Administration guidance criteria. Results: Two studies are presented. In the first, 2678 samples (622 arterial, 706 capillary, 1203 venous, 147 heel stick) were collected from 1577 patients in 14 US and three European sites. All accuracy criteria were met for arterial and venous samples considering all data combined. BGMSA showed better accuracy than BGMSB for arterial, venous, and heel stick, and similar results to BGMSB for capillary WB vs venous comparator. No endogenous interference from pO 2 , hematocrit, and sodium was identified. BGMSA was also accurate when analyzing contrived samples used to show accuracy over a wide range of glucose concentrations, alone and when combined with extreme hematocrit. In the second study, 117 capillary fingerstick samples collected at one US site were measured on both BGMSA and the comparator, and all accuracy criteria were met. No clinically significant medical risks were observed via Diabetes Technology Society Error Grids. Conclusions: BGMSA was effective for determining blood glucose in venous, arterial, neonatal arterial, neonatal heel stick, and capillary WB samples.

10556 Background: The coexistence of interstitial lung diseases (ILD) and lung cancer (LC) presents a clinical challenge. ILD, including idiopathic pulmonary fibrosis (IPF) and non-IPF entities, is associated with an elevated risk of LC. However, the clinical features, treatment patterns, and survival outcomes of ILD-LC, especially IPF-LC, remain underexplored. We aimed to comprehensively evaluate survival outcomes and risk factors for mortality of ILD-LC patients based on a real-world long-term clinical cohort. Methods: A total of 1,341 eligible patients for this study were drawn from the Epidemiology and Genetics of Lung Cancer research program database (Mayo Clinic) including 96 IPF-LC, 111 Non-IPF ILD-LC, 99 ILD-only, and 1,035 propensity-matched LC-only patients (Controls). Data were collected on demographics, comorbidities, tumor features, treatment, and patient outcomes. Survival analyses used Kaplan-Meier methods; risk factors were assessed via Cox Proportional Hazards models. All comparisons reported below were statistically significant with p-values≤0.001 unless specified. Results: Among the 20,470 primary LC patients who were diagnosed from 1997 through 2016 and followed to 2021, 207 (1.0%) had ILD-LC, of which 96 (46.4%) had IPF. Comparing to Controls, ILD-LC patients were 4 years older at the time of LC diagnosis, 63.8% (vs. 52.8%) male, had a higher rate of former smokers (60.4% vs. 47.3%), a lower proportion of adenocarcinoma (37.7% vs. 57.5%), and a higher proportion of squamous cell (28.0% vs. 20.3%) and small cell carcinoma (13.5% vs. 8.4%). Although no significant differences in either LC stage or grade, ILD-LC patients had a significantly higher mortality rate (90.3% vs. 78.8%), particularly among IPF-LC patients (97.9% vs. 83.8%). ILD-LC patients were less likely to undergo surgery or receiving pharmacological treatments (73.9% vs. 93.5%). The median 5-year overall survival rate (OS, months) were 49.5 for Controls, 35.2 for Non-IPF ILD-LC patients, and 15.9 for IPF-LC patients. Analysis of six most common comorbidities revealed that pulmonary hypertension was associated with worse OS in ILD-LC patients compared to those without it (11.9% vs. 20.1%, P < 0.05); however unexpectedly, hypertension in ILD-LC patients was associated with better OS (21.3% vs. 14.4%) and emphysema in IPF-LC patients with better OS (7.9% vs. 3.4%, P < 0.05) when compared to those without the two comorbidities, respectively. Conclusions: This study highlights the poorer survival outcomes in ILD-LC patients, particularly IPF-LC patients, emphasizing the need for further investigation into optimized therapies and comorbidity management. Our findings also call for attention to two unexpected associations of hypertension and emphysema, warranting in-depth research on the possible biological and physiological mechanisms.