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PURPOSE Systematic integration of tobacco cessation treatment within cancer centers is needed to improve smoking cessation outcomes among cancer patients and survivors. The current study characterizes baseline rates of tobacco use, use of cessation support, and changes in tobacco use over time among 756 participants diagnosed with cancer and enrolled in 9 ECOG-ACRIN Cancer Research Group trials. MATERIALS AND METHODS Participants completed surveys at trial enrollment and at 3- and 6-month follow-ups. Descriptive statistics were used to characterize patient characteristics and selected items from the Cancer Patient Tobacco Use Questionnaire (C-TUQ). Generalized estimating equations were used to examine the time point effect on tobacco product use. RESULTS At baseline, 11% of participants (n = 740) currently smoked cigarettes. Among participants with data available at all time points (n = 443), prevalence of current smoking was stable over time, with 7.2% of participants (n = 32) reporting current smoking at baseline, 7.0% (n = 31) at the 3-month follow-up assessment, and 6.8% (n = 30) at the 6-month follow-up assessment. Most participants (n = 77/104) who smoked cigarettes within the year prior to their cancer diagnosis continued smoking after their diagnosis, and during (n = 44/72) and following completion (n = 30/46) of their treatment. Sixty-two percent of participants (n = 81) who were currently smoking at baseline reported using smoking cessation medication and 32.1% reported use of nonpharmacologic smoking cessation support since their cancer diagnosis. In longitudinal analyses, the odds of using cigarettes since their cancer diagnosis, 3-month follow-up, and 6-month follow-up was higher than the odds of using cigarettes for the past 30 days at baseline (odds ratio [OR] = 1.97, OR = 1.62, and OR = 1.50, respectively; all p s < .01). CONCLUSION Smoking persists over time among patients with cancer enrolled in therapeutic cancer trials. Findings highlight the importance of increasing access to integrated and longitudinal smoking cessation support.

The Pulmonary Embolism Reporting and Data System (PE-RADS) provides a standardized framework for reporting acute pulmonary embolism at CT and MR angiography to guide treatment considerations based on clot location and right heart imaging features.

Background Radionuclide SPECT/computed tomography (CT) is standard for diagnosing transthyretin cardiac amyloidosis (ATTR-CM), but the prognostic value of quantitative metrics remains incompletely defined. We investigated their prognostic utility and additive value beyond existing staging systems. Methods Retrospective study of patients undergoing technetium-99m-pyrophosphate (PYP) SPECT/CT, including consecutive clinically referred patients (October 2023 to 2024) and trial participants with positive scans from January 2022. Myocardial PYP was quantified using standardized uptake values (SUV mean and SUV max ) and volumetric measures [percent injected dose (%ID), cardiac amyloid activity (CAA)]. Major adverse cardiovascular events (MACEs) comprised heart failure hospitalization or death. Cox and Kaplan–Meier analyses assessed associations; likelihood ratio testing and change in area under the curve (AUC) evaluated the additive prognostic value to existing staging systems. Results Among 43 patients with ATTR-CM, 16 experienced MACE over a median 518 days. AUCs ranged from 0.74 to 0.88 at 3 months and 0.56–0.62 at 12 months. All variables trended toward association with MACE: SUV mean ( P = 0.02), SUV max ( P = 0.03), %ID ( P = 0.06), and CAA ( P = 0.06). After age adjustment, cut-points for SUV max (5.8) and SUV mean (3.5) remained associated with MACE. Adding imaging variables to the National Amyloidosis Centre and Columbia staging systems did not significantly improve prognostic accuracy (Δ χ 2 : 0.40–2.48, P = 0.11–0.53). Interobserver reproducibility was high (intraclass correlation coefficients = 0.89–0.99). Conclusion Quantitative Tc-99m–PYP SPECT/CT metrics were associated with short-term outcomes in ATTR-CM, but did not improve established staging systems. Findings are hypothesis-generating and require validation in larger cohorts.

Abstract Background Breast cancer chemoprevention is underutilized among high-risk women. We examined whether decision support could increase informed decision making. Methods We conducted a cluster randomized controlled trial to evaluate the effect of a patient-facing decision aid (RealRisks) and provider tool (BNAV) compared with standard education on the number of women with high-risk breast lesions making informed choices. Questionnaires were administered at baseline and 6 and 12 months. The primary outcome was chemoprevention informed choice (defined as adequate knowledge and attitudes congruent with decision) at 6 months. Secondary endpoints included breast cancer risk perceptions, worry, chemoprevention knowledge, decision conflict, and chemoprevention decision. Results Across 31 randomized sites, we enrolled 210 providers, who were primarily breast health specialists. Of these, 24 sites (14 intervention, 10 control) enrolled 412 patients. Among 287 patients evaluable for the primary outcome, there were no statistically significant differences in informed choice between the intervention and control arms at 6 months (35% vs 27%, respectively; P = .20) and 12 months (37% vs 25%, respectively; P = .05). At 6 months, women in the intervention compared with control arm were more likely to have accurate breast cancer risk perceptions (31% vs 21%, respectively; P = .03) and adequate chemoprevention knowledge (33% vs 23%, respectively; P = .04). At 12 months, 52% of women in the control and 50% in the intervention arm self-reported initiating chemoprevention. Conclusion Decision support led to modest improvements in accurate risk perceptions and chemoprevention knowledge but not informed choice. Relatively high chemoprevention uptake was achieved among women with high-risk breast lesions managed mainly by breast health specialists. Trial Registration NCT04496739, https://clinicaltrials.gov/study/NCT04496739?cond=NCT04496739&viewType=Card&rank=1

Abstract Background Rare pediatric and young adult brain tumors pose major diagnostic and therapeutic challenges due to limited evidence and scattered expertise. The International Rare Brain Tumor Board was established to enable multidisciplinary case review, shared learning, and collaborative decision support. Methods Launched in February 2025, the Rare Brain Tumor Board is a monthly collaborative initiative led by Children’s National Hospital and the National Institutes of Health (NIH). Close partnership with the European Pediatric Rare CNS Tumor Board enhances transatlantic exchange and alignment of diagnostic and treatment recommendations. De-identified cases are presented with clinical history, neuroimaging, histopathology, and molecular profiling, including DNA methylation classification, available for the majority of cases. Sessions are structured to support integrated diagnosis and evidence-informed management recommendations through multidisciplinary discussion (neuropathology, neuroradiology, neurosurgery, radiation oncology, and pediatric as well as adult neuro-oncology). Findings Since its inception, 12 sessions have been held averaging 138 participants per session, reviewing a total of 32 cases (mean 3 cases/session; range 1–4) from 7 different countries and 3 different continents. Entities reviewed include DICER1-intracranial sarcoma (n = 5), astroblastoma (n = 4), FOXR2-activated neuroblastoma (n = 4), CNS tumors with BCOR internal tandem duplication (n = 3) and, BCOR-fused tumors (n = 3), CIC-rearranged-sarcoma (n = 3), intracranial mesenchymal tumor FET::CREB fusion positive (n = 3), MN1::PATZ1 fusion tumors (n = 2), undifferentiated sarcoma (n = 2), PLAG1-fusion tumor (n = 1), medullomyoblastoma (n = 1), and EZHIP-altered diffuse midline glioma (DMG) (n = 1). Multidisciplinary review has supported diagnostic clarification, strengthened familiarity with ultra-rare entities, and informed real-world treatment discussions in settings where prospective data are limited. Discussions have also increased awareness of rare brain tumor registries and opportunities for systematic case capture. Conclusion An international multidisciplinary rare brain tumor board is a feasible, scalable model to advance integrated diagnosis and collaborative management of ultra-rare pediatric and young adult brain tumors, while building a shared knowledge base across institutions.

Abstract Genome sequencing of the heterogeneous primary mitochondrial disorders (PMD) frequently reveals variants of uncertain significance that require functional tests for diagnosis, and does not identify variants in all patients. We analyzed mitochondrial enzyme assays, blue native polyacrylamide gel electrophoresis (BN-PAGE) with in-gel activity staining, complex I assembly blot, and select protein abundances in fibroblasts of a case series of 204 PMD patients divided into functional classes, in comparison to 51 controls and 53 differential diagnostic conditions. Overall, sensitivity and specificity for respiratory chain enzyme assays were 46% and 93% respectively, for BN-PAGE 40% and 98%, for complex I assembly assay 49% and 99%. The overall sensitivity of all tests was 76%, specificity 93%, with positive predictive value 96% and negative predictive value 67%. Categories with high sensitivity were isolated complex deficiencies, nuclear DNA-encoded mitochondrial protein synthesis defects, co-factor defects, and mitochondrial amino-acyl-tRNA synthetase conditions when aided by protein abundance. Mitochondrial DNA mutations and maintenance disorders showed poor sensitivities. Secondary dysfunctions were rare. A complete battery of functional tests showed strong diagnostic clinical utility in fibroblasts. One sentence summary A combination of four mitochondrial functional tests to identify or confirm suspected primary mitochondrial disease in fibroblasts had good sensitivity and excellent specificity, well beyond what was perceived using enzyme assays only.

TPS10057 Background: Ewing sarcoma (EWS) is an aggressive tumor of the bone and soft tissue primarily affecting adolescents and young adults. For patients with recurrent disease, outcomes remain poor with 5-year survival rates of 10%–15%, highlighting the need for novel approaches. STEAP1 is highly expressed in EWS cell lines, xenograft models, and patient tumor samples 1 and is transcriptionally regulated by EWSR1::FLI1 , the main oncogenic driver in EWS. Xaluritamig, a humanized bispecific XmAb 2+1 T-cell engager targeting STEAP1 and CD3, has demonstrated potent, antigen-dependent T-cell–mediated lysis of STEAP1-expressing EWS cell lines 1 and induced robust in vivo antitumor activity in EWS xenograft models with concomitant CD8+ T-cell activation. In metastatic castration-resistant prostate cancer, another STEAP1-expressing solid malignancy, xaluritamig has shown meaningful antitumor activity and a manageable safety profile 2 supporting its clinical evaluation in R/R EWS. Methods: This ongoing, phase 1b, single-arm, open-label, two-part, multicenter study (NCT07297979) is evaluating xaluritamig in pediatric, adolescent, and adult patients with a histologic diagnosis of EWS and an EWSR1 :: ETS fusion gene locally confirmed by next-generation sequencing. Patients must have R/R EWS following ≥1 line of systemic therapy and Karnofsky or Lansky performance status ≥70. Part 1 (dose confirmation) consists of cohort 1 (age ≥12 years) and cohort 2 (age ≥2 to <12 years), and part 2 (dose expansion) includes patients aged ≥2 years with no upper age limit at enrollment. This study includes a 21-day screening period, a treatment period, a safety follow-up period, and a long-term follow-up period. Patients receive xaluritamig every 2 weeks once reaching the target dose until radiographic disease progression per RECIST v1.1, clinical disease progression, unacceptable toxicity, initiation of other anticancer therapy not permitted per protocol, withdrawal of consent, death, or end of study as determined by the sponsor, whichever is earlier. Primary endpoints are safety, tolerability, and determination of recommended dose(s) for expansion. Secondary endpoints are pharmacokinetics, preliminary antitumor activity, and immunogenicity. Biomarker evaluation as an exploratory endpoint includes assessment of serum cytokines, other pro-inflammatory markers, immune-cell subsets, circulating tumor DNA, STEAP1 protein expression, and tumor genomic profile and their association with safety and efficacy. CRS mitigation plan includes step-up dosing, steroid premedication, and administration of interleukin-6 inhibitors as needed. Site activation commenced in January 2026 and enrollment is ongoing. 1. Nolan-Stevaux O. Cancer Res. 2020;80(16_Suppl):DDT02–03. 2. Kelly WK, Danila DC, Lin CC, et al. Cancer Discov . 2024;14(1):76-89. Clinical trial information: NCT07297979 .

Abstract Background High‐resolution MRI is essential for accurate diagnosis and treatment planning, but its clinical acquisition is often constrained by long scanning times, which increase patient discomfort and reduce scanner throughput. While super‐resolution (SR) techniques offer a post‐acquisition solution to enhance resolution, existing deep learning approaches face trade‐offs between reconstruction fidelity and computational efficiency, limiting their clinical applicability. Purpose This study aims to develop an efficient and accurate deep learning framework for MRI SR that preserves fine anatomical detail while maintaining low computational overhead, enabling practical integration into clinical workflows. Materials and Methods We propose a novel SR framework based on multi‐head selective state‐space models (MHSSM) integrated with a lightweight channel multilayer perceptron (MLP). The model employs 2D patch extraction with hybrid scanning strategies (vertical, horizontal, and diagonal) to capture long‐range dependencies while mitigating pixel forgetting. Each MambaFormer block combines MHSSM, depthwise convolutions, and gated channel mixing to balance local and global feature representation. The framework was trained and evaluated on two distinct datasets: 7T brain T1 MP2RAGE maps (142 subjects) and 1.5T prostate T2w MRI (334 subjects). Performance was compared against multiple baselines including Bicubic interpolation, GAN‐based (CycleGAN, Pix2pix, SPSR), transformer‐based (SwinIR), Mamba‐based (MambaIR), and diffusion‐based (I 2 SB, Res‐SRDiff) methods. Results The proposed model demonstrated superior performance across all evaluation metrics while maintaining exceptional computational efficiency. On the 7T brain dataset, our method achieved the highest structural similarity (SSIM: ) and peak signal‐to‐noise ratio (PSNR: dB), along with the best perceptual quality scores (LPIPS: ; GMSD: ). These results represented statistically significant improvements over all baselines (), including a 2.1% SSIM gain over SPSR and a 2.4% PSNR improvement over Res‐SRDiff. For the prostate dataset, the model similarly outperformed competing approaches, achieving SSIM of , PSNR of dB, LPIPS of , and GMSD of . Notably, our framework accomplished these results with only 0.9 million parameters and 57 GFLOPs, representing reductions of 99.8% in parameters and 97.5% in computational operations compared to Res‐SRDiff, while also substantially outperforming SwinIR and MambaIR in both accuracy and efficiency metrics. Conclusion The proposed framework provides a computationally efficient yet accurate solution for MRI SR, delivering well‐defined anatomical details and improved perceptual fidelity across anatomically distinct datasets. By significantly reducing computational demands while maintaining state‐of‐the‐art performance, the model offers strong potential for feasibility toward clinical translation and scalable integration into future imaging workflows.