Citations

BACKGROUND AND OBJECTIVES Recent changes in European diagnostic criteria allow for serologic diagnosis of celiac disease in children. Those guidelines have not been adopted in North America; hence, we aim to assess the positive predictive value (PPV) of tissue transglutaminase (tTG) immunoglobulin A (IgA) assays used in North America in identifying histologic findings of celiac disease. METHODS Multicenter retrospective cohort study of children (<18 years) with an elevated tTG IgA within 6 months of an esophagogastroduodenoscopy between January 2016 and December 2021. Biopsy-confirmed celiac disease was determined by the presence of intraepithelial lymphocytosis and villous atrophy. The primary outcomes were the PPV of an elevated tTG IgA and tTG IgA greater than or equal to 10 times the upper limit of normal (10× ULN). RESULTS Overall, 4019 children (63.3% female; 9% type 1 diabetes, 2% Down syndrome) were included. Histologic findings were consistent with celiac disease for 3321 children (PPV = 82.6% [95% CI, 81.4–83.8]). Among the 1739/4019 (43.2%) children with tTG IgA greater than or equal to 10× ULN, 1651 had biopsy-confirmed celiac disease (PPV10× = 94.9% [95% CI, 93.8–95.9]). Five percent (88/1739) of children did not have histologic findings of celiac disease, including 41/1739 (2%) with normal histology. Diagnostic accuracy of tTG IgA varied widely among assays used in North America (PPV range: 71.5%–88.8%; PPV10× range: 89.3%–97.3%). Assays performed worse in children with type 1 diabetes (PPV10× 89% [95% CI, 83.5–92.8]). CONCLUSIONS Elevated tTG IgA in isolation is insufficient to confidently diagnose celiac disease. As tTG assay performance varied widely, diagnostic confirmation by a specialist prior to dietary changes is essential.

Objective: In this study, we evaluate the utility of ctDNA analysis in a large cohort of patients for whom ctDNA testing was ordered commercially with real-world application. Summary Background Data: Circulating tumor DNA (ctDNA) has emerged as a prognostic and predictive biomarker for assessing post-surgical molecular residual disease (MRD) and response to treatment. Methods: A real-world data analysis was performed using commercial ctDNA testing (SignateraTM) from patients (N=795, n=5,971 plasma samples) with stage I-III colon cancer treated at multiple US institutions. The association of ctDNA detection within the MRD window, during surveillance, and the impact of ACT was correlated with patient outcomes. Results: ctDNA-positive patients during the MRD window and surveillance showed significantly shorter DFS compared to ctDNA-negative patients (hazard ratio (HR): 9.85, P<0.0001; HR: 26.91, P<0.0001). Multivariate analysis of the MRD window revealed ctDNA-positivity as the most significant factor associated with inferior DFS (adjHR: 7.7, P<0.001). MRD-positive patients who received ACT showed improved DFS compared to patients observed post-surgery (adjHR: 6.1, FDR adj P=0.0007). No ACT benefit was observed in MRD-negative patients (adj HR: 1.20, FDR adj P=0.768). On evaluating ctDNA dynamics from MRD to surveillance, patients who remained ctDNA-positive or converted from negative to positive showed a significantly inferior DFS (HR: 34.40, P<0.0001; HR: 13.65, P<0.0001) compared to persistently ctDNA-negative patients. Conclusions: Postsurgical ctDNA detection is prognostic of relapse and potentially predictive of ACT benefit in patients with resectable colon cancer, which may enable personalized surveillance, intervention, and/or trial options, ultimately improving patient outcomes.

Abstract Extracellular vesicles (EV) are phospholipid‐encapsulated nanoparticles secreted by cells into their surrounding environment. EVs can transfer a variety of biomolecules that mediate intercellular communication and play a key role in physiological and pathological processes. Therefore, EVs are emerging as new biomarkers for diseases, therapeutic targets, and drug delivery vehicles. The isolation and detection of EVs requires time‐consuming and labor‐intensive processes, first to extract EVs from biological and physiological fluids and then to detect EV‐associated molecules with high sensitivity. The methodologies and instruments commonly used for EV analysis are not widely accessible outside of dedicated research laboratories, creating practical barriers for the study of EV‐associated molecules in biology research and clinical applications. To bridge this gap, we developed a proof‐of‐concept digital microfluidic device that can automatedly extract EVs from 20 µL of culture and human plasma samples within 25 min and detect EV‐bound proteins (e.g PD‐L1) on‐chip using an electrochemical sensor. This work serves as a framework for the development of streamlined EV analysis in both research and diagnostics.

Abstract Tisagenlecleucel (tisa-cel) is a CD19-directed chimeric antigen receptor T-cell therapy for relapsed/refractory precursor B-cell acute lymphoblastic leukemia (R/R B-ALL). We report infectious complications for 100 days (D100) following tisa-cel therapy in 471 pediatric and young adults (median age 13.8 years) with R/R B-ALL reported from September 2017 to June 2022. By D100, 137 (29%) patients had an infectious event, with an infection density of 0.542 per 100 person-days at risk. D100 cumulative incidences of bacterial, viral, and fungal infections were 14.1%, 11.6%, and 1.3%, corresponding to infection density scores of 0.296, 0.213, and 0.033 per 100 person-days at risk, respectively. In a multivariable analysis, receipt of ≥3 lines of therapy before tisa-cel (hazard ratio [HR], 1.86; 95% confidence interval [CI], 1.13-3.08; P = .015), any-grade cytokine release syndrome (HR, 1.78; 95% CI, 1.17-2.71; P = .007), and lack of neutrophil recovery (HR, 2.63; 95% CI, 1.47-4.69; P = .001) were associated with an increased risk for any infection. Similar associations were observed for bacterial infections, with the addition of younger age as an adverse risk (&lt;6 vs 6-15 years; HR, 2.38; 95% CI, 1.23-4.61; P = .01). Risk factors for viral infections included increasing age (1-year increase; HR, 1.05; 95% CI, 1.01-1.09; P = .016), prior history of any infection (HR, 2.76, 95% CI, 1.40-5.46; P = .004), and prior hematopoietic cell transplant (HR, 2.10; 95% CI, 1.18-3.71; P = .011). D100 infection-related mortality (IRM) rate was low at 0.2% (95% CI, 0.0-0.8). In this multicenter real-world study, we observed a high incidence of infectious complications but a low IRM following tisa-cel for R/R B-ALL.

INTRODUCTION: Treatment of celiac disease (CeD) requires a strict gluten-free diet (GFD). Adherence is difficult to assess and influenced by many factors. Identification of potential gluten exposures is essential, but no validated screening measures exist in pediatrics. The aim of this study was to develop and validate a measure screening for gluten exposure risk in pediatric patients with CeD. METHODS: The Gluten Exposure Risk Assessment (GERA) was developed in 3 phases. First, items were generated and refined by a modified Delphi process. Next, psychometrics, factor structure, and validity were examined in a sample of caregivers of pediatric patients younger than 18 years with CeD at 12 pediatric celiac centers. Finally, the final measure was tested on an independent sample population using confirmatory factor analysis and test-retest data. RESULTS: A total of 134 items were generated representing up to 6 domains, with 36 items selected for initial testing. Based on psychometrics and confirmatory factor analysis data from 484 caregivers, the final measure included a total of a single screening question and 9 validated items representing 2 domains (Home, Outside). Repeat testing on a separate sample (N = 369) confirmed factor structure and validity of the final measure (Comparative Fit Index = 0.96, Tucker–Lewis index = 0.95, root mean square error of approximation = 0.06). DISCUSSION: The GERA is a valid screening measure, completed by caregivers, for assessment of gluten exposure in children and adolescents with CeD following a GFD. GERA is a useful tool for evaluating the potential of risk of gluten exposure and potential need for intervention.

Abstract With global aging, assessing functional status is vital for precision medicine. Electronic Health Records (EHRs), particularly unstructured data, hold abundant information on patient mobility. This study explores using Large Language Models (LLMs) to extract and standardize mobility status from unstructured EHR data (i.e., clinical notes). We annotated 600 clinical notes from three health care institutions located in southeastern Minnesota and west-central Wisconsin, focusing on expressions of mobility and associated impairment. Leveraging the open-source Llama 3 model, we tested various prompting strategies—including zero-shot, few-shot, and task decomposition—and evaluated their performance. Error analysis showed that while the model sometimes inferred impairments without explicit evidence, most errors were clinically reasonable, often reflecting borderline or ambiguous cases. While considering reasonable inference as correct, at the patient-level, Mobility Extraction achieves a micro-average accuracy of 0.952 with an F1-score of 0.962, and Impairment Classification produces a micro-average accuracy of 0.912 and an F1-score of 0.948. A local, deterministic setup improved trustworthiness by ensuring consistent outputs, safeguarding privacy, and demonstrating cross-institution generalizability. These findings highlight the feasibility of LLM-based solutions for extracting mobility functional status from unstructured EHR data, supporting both clinical applications and research.

Abstract Background and Aims Invasive coronary function testing (CFT) identifies coronary vasomotor disorders in up to 90% of patients with angina with non-obstructive coronary arteries (ANOCA). The ILIAS ANOCA trial hypothesized that routine ad hoc CFT would be feasible, safe, and effective in providing an early, comprehensive diagnosis. Additionally, it was anticipated that combining CFT with a disease-specific treatment protocol would significantly improve quality of life in ANOCA patients compared with standard care. Methods After excluding patients with obstructive coronary artery disease (CAD) during clinically indicated invasive coronary angiography (ICA), eligible patients underwent CFT and were randomized to either the standard care group, where CFT results remained blinded, or the intervention group, where CFT results were disclosed along with a tailored medical therapy protocol. The primary outcome was the mean difference in the within-subject change in Seattle Angina Questionnaire summary score (SAQSS) between groups from baseline to a follow-up of 6 months. The trial is registered with the International Clinical Trials Registry Platform (NL-OMON20739). Results A total of 255 patients consented, of whom 153 patients (60%) without CAD underwent CFT and were randomized 1:1 to the standard care (n = 76) or intervention group (n = 77). All CFT procedures were successful without adverse events. A vasomotor disorder was identified in 120 patients (78%). At 6-month follow-up, the SAQSS improved significantly in the intervention group compared with the control group, with an intervention effect of 9.4 units (95% confidence interval 3.9–14.9, P = .001). There were no major adverse cardiac events at the 6-month follow-up. Conclusions Routine CFT during the initial ICA was feasible, safe, and had high diagnostic yield. Implementing a pragmatic CFT protocol combined with a disease-specific treatment protocol significantly improved disease-related quality of life in patients with ANOCA compared with standard care.

Background/Objectives: Proton therapy delivers highly conformal doses to the target area without producing an exit dose, minimizing cumulative doses to healthy liver tissue. This study aims to evaluate current practices, challenges, and variations in the implementation of proton stereotactic body radiation therapy (SBRT) and hypofractionated therapy for liver malignancies, with the goal of providing a technical assessment to promote broader adoption and support future clinical trials. Methods and Materials: An extensive survey was conducted by NRG Oncology across North American proton treatment centers to assess the current practices of proton liver SBRT and hypofractionated therapy. The survey focused on key aspects, including patient selection, prescription and normal tissue constraints, simulation and motion management, treatment planning, quality assurance (QA), treatment delivery, and the use of image-guided radiation therapy (IGRT). Results: This survey captures the current practice patterns and status of proton SBRT and hypofractionated therapy in liver cancer treatment. Proton therapy is increasingly preferred for treating inoperable liver malignancies due to its ability to minimize healthy tissue exposure. However, the precision required for proton therapy presents challenges, particularly in managing uncertainties and target motion during high-dose fractions and short treatment courses. Survey findings revealed significant variability in clinical practices across centers, highlighting differences in motion management, dose fractionation schedules, and QA protocols. Conclusion: Proton SBRT and hypofractionated therapy offer significant potential for treating liver malignancies. A comprehensive approach involving precise patient selection, treatment planning, and QA is essential for ensuring safety and effectiveness. This survey provides valuable insights into current practices and challenges, offering a foundation for technical recommendations to optimize the use of proton therapy and guide future clinical trials.

Abstract Background The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose This study aims to investigate whether fine‐tuning LLMs with domain knowledge can improve the performance on Task (1) treatment regimen generation, Task (2) treatment modality selection (photon, proton, electron, or brachytherapy), and Task (3) ICD‐10 code prediction in radiation oncology. Methods Data for 15 724 patient cases were extracted. Cases where patients had a single diagnostic record, and a clearly identifiable primary treatment plan were selected for preprocessing and manual annotation to have 7903 cases of the patient diagnosis, treatment plan, treatment modality, and ICD‐10 code. Each case was used to construct a pair consisting of patient diagnostics details and an answer (treatment regimen, treatment modality, or ICD‐10 code, respectively) for the supervised fine‐tuning of these three tasks. Open source LLaMA2‐7B and Mistral‐7B models were utilized for the fine‐tuning with the Low‐Rank Approximations method. Accuracy and ROUGE‐1 score were reported for the fine‐tuned models and original models. Clinical evaluation was performed on Task (1) by radiation oncologists, while precision, recall, and F‐1 score were evaluated for Task (2) and (3). One‐sided Wilcoxon signed‐rank tests were used to statistically analyze the results. Results Fine‐tuned LLMs outperformed original LLMs across all tasks with p value ≤ 0.001. Clinical evaluation demonstrated that over 60% of the fine‐tuned LLMs‐generated treatment regimens were clinically acceptable. Precision, recall, and F1‐score showed improved performance of fine‐tuned LLMs. Conclusion Fine‐tuned LLMs demonstrated statistically significant improvements over original LLMs upon three clinically important tasks in radiation oncology. This study explored the feasibility of applying fine‐tuned LLMs in radiation oncology, inspiring further development of utilizing LLMs to assist with radiation oncology tasks.