Citations

Abstract Purpose To develop and implement an in‑house treatment‑management platform to improve communication, data accessibility, and task tracking across a radiation oncology department. Methods We developed Capsule, a custom software platform that centralizes patient data from the ARIA oncology information system (Varian Medical Systems, Palo Alto, United States), and integrates it with data from Outlook (Microsoft, Redmond, United States), spreadsheets, and manual inputs. Built using C#, SQL, and the .NET framework, it features a graphical interface in XAML with twelve modular tools to support workflows across a large multi‐modality department. A formal validation, feedback, and maintenance cycle was established. Its impact on workflow efficiency was evaluated using average CarePath task duration before and after implementation, supplemented by user interviews and surveys. Results The Department Schedule, Treatment Planning and On‐Treatment Whiteboard, Spreadsheet, and various workload tracking modules emerged as the most frequently used among dosimetrists surveyed.The Whiteboard module consolidates planning metadata for streamlined workflows. Workload and scheduling modules help distribute tasks fairly. The Department Schedule module centralizes daily role and contact information. The Spreadsheet module allows dosimetrists to identify a reference for unique plans and provides secure access to large exportable tabulated patient data and clinical trends. Other modules support proton down‐time replanning and special procedure physics assignment. One year after Capsule's implementation, most tasks in the external beam radiation therapy workflow experienced a reduction in average duration. Discussion Capsule streamlines workflows by integrating multiple tools into a single platform, supporting urgent scenarios, and improving task hand‐off. Its modular design allows customization to local workflows, but implementation must be guided by institutional oversight and formal quality management to ensure data security and compliance. Conclusion Capsule can provide a solution for improved workflow efficiency, communication, and interdisciplinary coordination. By democratizing access to routine and retrospective data, it supports treatment planning, scheduling, and quality improvement. Ongoing updates are essential as clinical practices evolve.

ABSTRACT Background Primary lateral sclerosis (PLS) is an ultrarare upper motor neuron syndrome with a prognosis unique from classical ALS. The study of PLS is complicated by its rarity and the difficulty distinguishing PLS from ALS. We present data from a 1‐year prospective follow‐up study on PLS and efforts to distinguish it from ALS. Methods Seventy‐six PLS participants enrolled in this prospective natural history study. EMG studies, blood neurofilament light chain levels (NfLs), and demographic characteristics were obtained at baseline. At 1‐year follow‐up, repeat EMG studies were conducted to determine which participants fulfilled criteria for ALS. Baseline characteristics were then compared to determine features that predict reclassification. Results Seventy participants completed 1‐year follow‐up. Five of the 70 were reclassified to ALS (7.1%). Those reclassified had higher trends in baseline blood NfL levels (91.4 vs. 34.0 pg/mL, p = 0.13) and shorter symptom duration (39 vs. 69 months in the PLS group, p = 0.15). Reclassification was noted in both probable and definite PLS participants. All cases with a symptomatic duration of less than 2 years retained the PLS phenotype (5 of 5). NfL levels over 90 pg/mL predicted reclassification with 94% specificity and 60% sensitivity. No other features predicted reclassification to ALS. Conclusions In our population, reclassification of PLS to ALS occurred at a low frequency at 1 year follow‐up (7.1%). Baseline NfL was the strongest predictor in differentiating UMN dominant ALS from PLS at 1‐year follow‐up. Based on our data, we propose EMG and NfL criteria for enrollment in future PLS trials.

ABSTRACT Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.

Abstract Cytokine receptor–like factor 2 rearrangement (CRLF2r), an aberration causing a Philadelphia chromosome–like gene expression profile and a poor prognostic marker in B-cell acute lymphoblastic leukemia (B-ALL), endows relative chemotherapeutic resistance. Extramedullary disease (EMD), including both central nervous system (CNS) and non-CNS involvement, can also be associated with worse outcomes. The relationship between CRLF2r and EMD is unknown. Based on an initial observation of patients with relapsed/refractory (R/R) CRLF2r B-ALL and EMD, a single-center retrospective review was conducted to evaluate the incidence of EMD in patients with CRLF2r B-ALL. Across 177 children, adolescents, and young adults with R/R B-ALL, 21 (11.9%) harbored CRLF2r, of whom 19 (90.5%) had EMD at some point during their treatment course. In contrast, only 78 of the 156 patients (50%) without CRLF2r ever had EMD (P = .0003). In the CRLF2r cohort, 8 (38.1%) presented with EMD at diagnosis: 7 with CNS and 1 with non-CNS EMD. At relapse, 17 (80.9%) had EMD: 4 (19%) had CNS EMD, 9 (42.9%) had non-CNS EMD, and 4 (19%) had both. Isolated EMD was observed in 2 patients (11.8%) at first relapse and in 3 (17.6%) with multiple relapses. Given these findings, further study to evaluate this potential association between EMD and CRLF2r in R/R B-ALL is indicated.

ABSTRACT Background One in five emergency department (ED) visits is by older adults aged 65+ years. Clinical practice guidelines are needed to optimize patient care by translating the best available evidence into actionable recommendations to guide person‐centered management for this medically complex and growing patient population. Our objective was to understand contributors' experiences and share best practices to inform other guideline working groups. Methods We conducted a qualitative study based on thematic analysis with an interpretivist paradigm in June–July 2025, using semi‐structured interviews of Geriatric Emergency Department (GED) Guidelines 2.0 contributors to understand their experiences. Interviews were transcribed, independently reviewed by team members, and reflexively coded to identify themes. Results We interviewed 18 participants. Four main themes emerged: (1) motivations for participation, (2) varying opportunities to be involved, (3) personal benefits, and (4) challenges for improvement. Participants contrasted the strengths of shared learning and development with the burdens of organization and coordination. Conclusion Contributors to the GED Guidelines 2.0 reported a positive experience. They joined because they wanted to contribute to better care of older patients and to achieve personal and professional goals. Many cited that their roles aligned with their strengths and expertise. They also gained skills and knowledge on systematic reviews and benefited from networking. Future groups planning a similar process should consider having multiple levels of leadership, experts in systematic reviews, regular reminders, and creative incentives to improve the process and foster improved networking opportunities within their disciplines.

Abstract Objective To test the effectiveness of a surgical web‐based decision aid (DA) in improving knowledge. Summary of Background Data DAs support decision making by providing information about the options. Methods A stepped wedge trial was conducted in 10 National Cancer Institute Community Oncology Research Program clinics (Alliance for Clinical Trials in Oncology). Clinics were randomized to time of transition from usual care (UC) to delivery of a web‐based DA. Patients with stage 0 through 3 breast cancer being considered for surgery were enrolled. Knowledge (primary outcome) was measured using the Breast Cancer Surgery Decision Quality Instrument and patients were asked, “How informed do you feel?” Intervention effects were tested with linear mixed‐effects models, accounting for surgeon and clinic‐level clustering, time, and enrollment after COVID. Additional models controlled for demographics. Results A total of 44% of DA arm patients reviewed the DA and 58% in UC arm reported reviewing “any information.” Being in the DA arm versus UC was not associated with knowledge. However, “review of information” was associated with higher knowledge. In addition, non‐White race and lower education were associated with lower knowledge. The DA arm was associated with higher perceptions of feeling informed (parameter estimate 1.36; 95% CI, 0.18–2.55; p = .02); this persisted even when controlling for review of information or demographics. Conclusion Improved knowledge was not demonstrate with a web‐based DA versus UC. Interestingly, the DA was associated with a higher likelihood of feeling informed. Future research will explore the discrepancy between patients feeling informed but having low knowledge, especially for disadvantaged patients. Trial Registration ClinicalTrials.gov Identifier: NCT0376600

Convergent framework for future POC cancer diagnostics aligning clinical needs (why), enabling technologies (how), and translational bridges. It integrates lab-on-chip biosensing and analytics for decentralized, early, longitudinal care.

Abstract Cancer occurs in ∼1 per 1000 pregnancies; thousands of patients may require radiation procedures for diagnosis and treatment each year in the United States alone. This rare but high-risk scenario, coupled with fear of radiation, has created ambiguity in the ideal management of pregnant patients. Without a comprehensive guide for the use of radiation in imaging and treatment for pregnant cancer patients, it is difficult for providers to offer optimal patient-centered care without introducing disparities. The goal of this paper is to provide guidance on the use of radiation for screening, diagnosis, staging, and treatment of cancer, while highlighting gaps in existing knowledge and guidelines. The intention is that physicians, medical physicists, and patients could use this document as a resource for shared decision-making, ensuring safe and effective practice. A team of physicians and medical physicists with expertise in imaging, radiotherapy, and maternal fetal medicine was assembled, along with a patient advocate and lawyer. Existing guidelines and recent literature were reviewed. Authors also drew from their experience where published guidance was lacking. The resulting document discusses best practice to guide use of radiation for cancer, as well as patient-centered care management and legal considerations. Overall, it is possible to safely and effectively deliver radiation to pregnant patients in numerous circumstances. Use of radiation or other modalities should be discussed through shared decision-making with the physician and patient, contextualizing the maternal and fetal risk from treatments. Healthcare providers should support wide access to reproductive healthcare to allow equitable, evidence-based, patient-centered healthcare.