Kenaston, Matthew W.

Background. Although solid organ transplant prolongs survival in end-stage disease, recipients face high symptom burden and complex decisions at multiple junctures. Palliative care (PC) may address these needs, but referrals in transplant programs are infrequent, often reactive, and the benefits remain unclear. Methods. We retrospectively studied 12 676 heart, liver, lung, and kidney transplants across 3 Mayo Clinic sites (2018–2024). PC encounters were classified as pretransplant (≤1 y before admission), peritransplant (during hospitalization), or posttransplant (≤1 y after discharge). Results. Only 8.3% engaged PC, with patterns varying by organ and timing. Heart programs demonstrated the highest overall engagement, primarily pre- and peritransplant, whereas lung and kidney referrals occurred more often after discharge. Earlier timing coincided with longer survival, whereas peri- and posttransplant encounters occurred closer to death. In heart transplantation, pre- and peritransplant PC were observed in recipients with lower procedural volume and fewer short-term readmissions. Liver and lung cohorts displayed variable patterns. Kidney referrals occurred infrequently and were concentrated among recipients with higher morbidity. Timing of inpatient consultation showed a strong positive correlation with hospital length of stay, and pretransplant PC coincided with higher rates of goals-of-care discussions and fewer hospital interventions. Adapting screening criteria to focus on high-risk recipients, PC was associated with fewer short-term readmissions for heart and lung recipients. Conclusions. PC among solid organ transplant was infrequent and varied. Referral timing often aligned with patient morbidity and mortality. Structured, prospective validation of screening methods may identify patients most likely to benefit from palliative involvement.

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.

Palliative care (PC) has been shown to improve quality of life (QoL), reduce symptom burden, and align care with patient goals in those with serious illnesses. However, its integration into the emergency department (ED), where many patients with advanced illness present, remains underexplored and inconsistently applied. This narrative review evaluates several recent studies on ED-based palliative care, aiming to clarify whether the early benefits in symptom management and QoL are associated with reduced healthcare utilization and cost. A comprehensive literature search identified five new key studies with significant methodological differences, including variations in patient populations, outcome measures, and study designs. Some studies demonstrated short-term symptom relief, yet only one showed significant improvements in QoL at long-term follow-up. For healthcare utilization, still only one of these new studies reported substantial cost savings, underscoring the need for further research to validate financial benefits. We suggest how future work should be designed to explore both clinical and economic outcomes in tandem to establish a clear value proposition for ED-based palliative care. By addressing this lack of cohesion, we can determine the merit of ED-palliative care integrations with the goal of improving patient care and optimizing resource utilization.

Abstract Introduction: High-grade gliomas (HGG) are aggressive brain tumors with heterogeneous cell populations, including invasive regions driving disease progression and recurrence. Our previous work integrated molecular profiling with spatially matched advanced multiparametric MRI in a cohort of HGG patients with multi-regional sampling, uncovering distinct biological signatures linked to individual advanced MRI phenotypes. Yet, the relationships between MRI phenotypes—and how this potentially informs underlying biology—remains unexplored. Here, we leverage an unsupervised approach to dimensional reduction for a broad array of MRI features and compare regional transcriptomic signatures in a cohort of image-localized HGG samples. Methods: From our existing data, we selected 20 HGG patients with 73 multi-regional tumor biopsies, each including 18 advanced MRI parameter calculations. With an iterative approach, data was projected using uniform manifold approximation (UMAP) at multiple resolutions and clustered, producing high-confidence stable clusters. We used random forest modeling to assess cluster associations with previously identified signatures. Cluster-specific transcriptomic profiles were detected by DESeq2 and subsequent gene-set enrichment analysis. We further evaluated Pearson correlations and statistical fit with MRI image parameters by performing pathway analysis on genes with significant correlations by image feature, across clusters. Results: 8 clusters emerged from our analyses. Most advanced MRI features exhibited statistically significant differences across clusters, forming unique patterns of feature behavior. Notably, while these image-based clusters did not correlate well with metabolic profiles, contrast enhancement, or genomics, they did demonstrate distinct and HGG-relevant transcriptomic profiles, with shared and divergent signatures. Across clusters, we also identified several correlations between specific MRI parameters and gene pathway trends. These highlighted combined patterns of multiple MRI features, offering a spectrum of regional phenotypes imperceptible with single feature analysis. We generalize these results to an image-based decision matrix to detect biologic signatures for validation. Conclusion: Our integrative analysis of MRI mapping stratifies imaged-based patterns to non-invasively assess molecular signatures within HGG. This approach will help elucidate MRI biomarkers that may predict glioma behavior and support future individualized treatment approaches. Citation Format: Matthew W. Kenaston, Matthew Flick, Kasthuri Kannan, Michele Ceccarelli, Nhan Tran, Leland S. Hu. Integrative analysis of regional MRI phenotypes in high-grade glioma: Dimensional reduction of multi-omics imaging and molecular profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5776.