Citations

Abstract Patients with multiple myeloma have an elevated risk of severe complications from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) infection. To demonstrate proof of concept for a pandemic preparedness and response network, the American Society of Hematology Research Collaborative Multiple Myeloma Program developed a prospective, decentralized study within a site-based provider network. In the COVID-19 outcomes in myeloma and the impact of vaccines (COSMIC) study, patients provided consent, permitted electronic health record data collection, completed patient-reported outcomes at baseline, day 30, and day 180 through an electronic study portal, and were compensated for participation. Clinicians at participating sites facilitated study recruitment and shared best practices monthly to accelerate study execution. The COSMIC study rapidly consented 201 patients from 8 participating sites over 4 months (22 January 2024 through 17 May 2024). Consented participants were 30 to 93 years old, with a median age of 67; 46% were female, 69% were non-Hispanic White, 17% were non-Hispanic Black, and 8% were Hispanic/Latino. Patient-reported data completion was 97% at baseline, 81% at 1 month, and 79% at 6 months. At baseline, 55% of patients reported a history of COVID-19 infection and had received a median of 4 SARS-CoV-2 vaccines. For all patients, there were an additional 18 COVID-19 infections and 57 vaccines received during the 6-month observation period. There were no significant differences in health-related quality of life over time or by history of COVID-19 infection. COSMIC demonstrates the feasibility of an innovative evidence generation strategy to drive rapid accrual of patients to address an evolving area of need. This trial was registered at www.clinicaltrials.gov as #NCT05831787.

Abstract Background Despite the high precision of deep learning models on internal tests on CT, their effectiveness often drops on external validation due to artifacts caused by patient motion and implants like metal or silicone that were not accounted for in the carefully curated training data. Potential wide categories of “unknown” anomalies within a CT exam makes training a supervised model for out‐of‐distribution (OOD) identification impractical, especially when considering unseen external data. Purpose To develop an artificial intelligence (AI) model to detect and identify anomalies/OOD data in abdominal–pelvis CT exams for the purpose of improving the performance of downstream applications. Methods Our proposed 2D and 3D generative architecture receives the third lumbar vertebra (L3) slice (slice‐level model) or all the slices from the series (series‐level model), generates a reconstruction and the secondary part of our architecture—anomaly score computation block, computes the anomalies pixels/voxels (slice‐level/series‐level) to identify anomalous L3‐slices/volumes (slice‐level/series‐level). We trained on data from over 2850 abdominal–pelvis CT volumes from adults over age 50 years collected throughout multiple Mayo Clinic campuses (60% female; mean age: 66.9, 92.4% non‐Hispanic White) and tested on a prospective test set of 544 CTs from July 2024 (47.3% female; mean age: 70.9, 94% non‐Hispanic White) as well as an external test set. Results We found that while traditional methods show moderate success, our generative models—Vector Quantized Variational Autoencoder (VQVAE) and Vision Transformer‐Masked Autoencoder (VIT‐MAE)—deliver excellent results with negligible false positives (FPs) and are also superior in identifying varied types of OOD samples. Prospective analysis showed the model was able to handle the under‐documentation of anomaly in radiology reports with 86.11% true positive (TP) rate. We also performed external validation using the publicly available AbdominalCT‐1k dataset, which contains 1062 CT scans compiled from several existing benchmark datasets. The model achieved a 75.26% TP rate, while the 24.7% FP rate was primarily triggered by anomalies located outside the body. Conclusions The proposed method can be leveraged to detect both intra‐ and interclass OOD data from abdominal CT images and can assess the quality of CT datasets to provide actionable insights. This workflow is particularly valuable for nonshareable healthcare collaborations, where it can be deployed as a service within local firewalls for automated dataset curation without prior knowledge about the OOD types. The implementation of the algorithm is available in the GitHub: https://github.com/gokul‐ramasamy/implant_detection.git .

Abstract Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment for many hematological malignancies, but graft-versus-host disease (GVHD) is a common complication. Low gut microbiome diversity is associated with higher GVHD risk and shorter survival in multiple studies. Recently, the BMT CTN 1703 clinical trial demonstrated superiority of a GVHD-prophylaxis regimen including post-transplant cyclophosphamide (PTCy) compared to the standard prophylaxis (tacrolimus and methotrexate, Tac/MTX) in terms of GVHD-free, relapse-free survival at one year among reduced intensity conditioning allo-HCT recipients. However, the effect of PTCy on the gut microbiome and its association with clinical outcome have not been described. Here, we report on a companion randomized clinical controlled trial (BMT CTN 1801), which collected 2575 longitudinal stool samples from 304 study participants. Samples were obtained up to weekly up to day 84 post allo-HCT and at less frequent intervals thereafter, up to 2 years. Microbiome diversity and absolute microbial load were lower in the PTCy group compared to the Tac/MTX group on days 14–28 post-HCT. However, diversity at the timepoint closest to neutrophil engraftment was not significantly associated with non-relapse mortality after one year or other clinical outcomes, contrary to expectations from previous studies. Microbial domination events, when a single species exceeds 30% relative abundance, were comparable across treatment arms and reflected both pathogen blooms as well as less severe disruptions of the microbial community. Clostridium scindens and secondary bile acid metabolism pathways were less prevalent in the PTCy arm than in the Tac/MTX arm post-HCT, yet presence of secondary bile acid metabolism pathways was associated with a lower risk of chronic GVHD. Given that PTCy was associated with a greater disruption of the microbiome as measured by diversity, absolute microbial abundance, and bile acid metabolism capability, but better clinical outcomes overall, these data suggest that the importance of the microbiome in modulating the host immune systems after allo-HCT is specific to different types of GVHD prophylaxis.

Objectives: To summarize recent high-quality trials and provide an update to the original 2022 American College of Radiology Appropriate Use Criteria for Treatment of Locoregional Gastric Adenocarcinoma. This multi-specialty-led committee included gastrointestinal radiation and medical oncology, gastroenterology, radiology, and surgical oncology. Methods: Using the population, intervention, comparator, outcome, timing, and study design framework, the evidence was assessed using Cochrane and PRISMA 2020 methodology. Eligible studies included prospective Phase II/II trials and select retrospective studies published between 5/1/2020 and 8/1/2025 in the Embase, Medline, and Pubmed databases. Study type and quality were assessed. Well-established RAND-UCLA consensus methodology (modified Delphi) was used to rate the appropriateness of the treatment options. Results: ARS AUC recommendations include adjuvant FLOT chemotherapy for localized gastric adenocarcinoma after neoadjuvant FLOT and surgery, poCT or perioperative chemoimmunotherapy for localized gastric adenocarcinoma, adjuvant chemotherapy +/− CRT for localized gastric adenocarcinoma that undergoes upfront surgery, and systemic therapy alone for localized gastric adenocarcinoma in a medically inoperable patient. Conclusions: This updated ARS AUC summary provides guidelines for the multimodality treatment of resectable gastric adenocarcinoma.

BACKGROUND We constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting. METHODS PRSmix + , a multi-trait PRS framework, is used to train a composite PRS (PRS multi ) in COPDGene non-Hispanic White participants ( n = 6,647). Associations of PRS multi with COPD status (GOLD 2–4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American ( n = 2,466), ECLIPSE ( n = 1,858), Mass General Brigham Biobank ( n = 15,152), and All of Us ( n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRS multi and were validated with proteomic data in COPDGene ( n = 5,173) and UK Biobank ( n = 5,012). RESULTS PRSmix + selected 7 traits for PRS multi . In multivariable models, PRS multi was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28–1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11–0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRS multi outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations. CONCLUSIONS Multi-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management. TRIAL REGISTRATION COPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

Advances in cancer therapeutics have substantially improved survival across multiple malignancies but have concurrently increased the burden of cancer therapy–related cardiovascular toxicity, establishing cardio-oncology as an essential discipline situated at the interface of oncology and cardiovascular medicine. Despite growing incorporation of cardiac imaging and circulating cardiac biomarkers into surveillance strategies, current approaches remain limited by modest specificity, heterogeneous risk profiles, and inconsistent translation into effective cardioprotective interventions. This review synthesizes contemporary evidence on the biological mechanisms underlying cancer therapy–related cardiovascular toxicity and critically evaluates the current role and limitations of established cardiac biomarkers, including high-sensitivity cardiac troponins and natriuretic peptides, in risk stratification and early injury detection. We further examine why many cardioprotective clinical trials have yielded neutral or modest results, highlighting signal dilution arising from low-risk populations, endpoint heterogeneity, and timing mismatches between injury biology and intervention. This review advances a precision cardio-oncology framework by integrating tumor-derived liquid biopsy platforms—such as circulating tumor DNA, tissue-specific cell-free DNA, and extracellular vesicles—with cardiac biomarkers to provide a biologically contextualized assessment of cardiovascular risk. Tumor-derived biomarkers may reflect treatment intensity, systemic inflammatory burden, and cardiotoxic susceptibility, thereby enhancing the interpretation of cardiac biomarker signals and refining patient selection for targeted cardioprotective strategies.

Background/Objectives: Hypofractionated ablative radiation is an increasingly popular option for patients with hepatocellular carcinoma (HCC). However, concern remains about the risk for radiation-induced liver toxicity in patients with decompensated liver function. Methods: We retrospectively identified patients with underlying Child-Pugh (CP) B or C liver function treated at our University and Veterans Affairs (VA) departments from 2014 to 2019. Primary endpoints included treatment-related toxicity and dosimetric parameters. Results: 38 patients were included in the analysis. Most patients (98%) had CP B or Albumin-Bilirubin (ALBI) grade 2–3 (100%) liver disease. The median dose was 50 Gy (range 30–50) delivered in 5 or 10 fractions. Most patients had a single tumor treated (66%) with a median size of 3.1 cm (Interquartile Range (IQR) 2.3–4.1). The mean liver dose was 9.28 Gy (IQR 6.76–13.64) with a liver D800cc of 3.99 Gy (IQR 1.41–8.02). All patients completed their intended course with a median follow-up of 43 months. Four patients (10.3%) developed non-classical radiation-induced liver disease (RILD), comparable to the rate for patients with CP A function treated contemporaneously (8.3%). Otherwise, one patient (2.6%) experienced acute grade 3+ (non-RILD) hepatobiliary toxicity, while one patient (2.6%) experienced late grade 3+ hepatobiliary toxicity. Local control was promising with 2-year freedom from progression in the treated lesion of 73% (95% CI 38–91%). Median overall survival was 12 months (95% CI 5–25 months). Conclusions: Ablative radiation for patients with decompensated liver function and HCC appears well tolerated with low rates of RILD and encouraging local control. With careful selection, these patients should be considered for inclusion in future randomized trials.