Citations

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Objectives/Goals: Manual skin assessment in chronic graft-versus-host disease (cGVHD) can be time consuming and inconsistent (>20% affected area) even for experts. Building on previous work we explore methods to use unmarked photos to train artificial intelligence (AI) models, aiming to improve performance by expanding and diversifying the training data without additional burden on experts. Methods/Study Population: Common to many medical imaging projects, we have a small number of expert-marked patient photos (N = 36, n = 360), and many unmarked photos (N = 337, n = 25,842). Dark skin (Fitzpatrick type 4+) is underrepresented in both sets; 11% of patients in the marked set and 9% in the unmarked set. In addition, a set of 20 expert-marked photos from 20 patients were withheld from training to assess model performance, with 20% dark skin type. Our gold standard markings were manual contours around affected skin by a trained expert. Three AI training methods were tested. Our established baseline uses only the small number of marked photos (supervised method). The semi-supervised method uses a mix of marked and unmarked photos with human feedback. The self-supervised method uses only unmarked photos without any human feedback. Results/Anticipated Results: We evaluated performance by comparing predicted skin areas with expert markings. The error was given by the absolute difference between the percentage areas marked by the AI model and expert, where lower is better. Across all test patients, the median error was 19% (interquartile range 6 – 34) for the supervised method and 10% (5 – 23) for the semi-supervised method, which incorporated unmarked photos from 83 patients. On dark skin types, the median error was 36% (18 – 62) for supervised and 28% (14 – 52) for semi-supervised, compared to a median error on light skin of 18% (5 – 26) for supervised and 7% (4 – 17) for semi-supervised. Self-supervised, using all 337 unmarked patients, is expected to further improve performance and consistency due to increased data diversity. Full results will be presented at the meeting. Discussion/Significance of Impact: By automating skin assessment for cGVHD, AI could improve accuracy and consistency compared to manual methods. If translated to clinical use, this would ease clinical burden and scale to large patient cohorts. Future work will focus on ensuring equitable performance across all skin types, providing fair and accurate assessments for every patient.

BACKGROUND Malignant peripheral nerve sheath tumors (MPNSTs) are rare and highly malignant tumors of neural crest origin. No matter their anatomical location, they are difficult to treat and carry a poor prognosis. OBSERVATIONS This report highlights the complexities of the diagnosis and management of a patient with MPNST of the cauda equina. The tumor retained the expression of H3K27me3, which is expected in spinal MPNSTs but uncommon in most sporadic and neurofibromatosis type 1–driven MPNSTs. LESSONS The described diagnostic nuance should be taken into consideration in patients with suspected MPNST of the cauda equina, as these tumors require swift neurosurgical intervention in order to maintain neurological function. Additional treatment modalities include neoadjuvant and adjuvant chemotherapy and radiotherapy. Although improvements in local control and progression-free survival have been reported, the role of radiation therapy and chemotherapy has been underinvestigated due to the rarity of MPNSTs presenting in this region. https://thejns.org/doi/10.3171/CASE24723

Extramedullary multiple myeloma (EMM), defined in this review as soft tissue plasmacytomas resulting from hematogenous spread, is characterized by the ability of MM cells to proliferate outside of the bone marrow microenvironment. It is aggressive, often associated with high-risk cytogenetics and early relapse, and independently portends significantly shorter progression-free and overall survival, even in the era of highly effective immunotherapies. The molecular and microenvironmental factors underlying extramedullary MM dissemination continue to be studied to inform the development of better treatments. In this review, we discuss our current understanding of the biology of EMM, focusing on its distinct molecular and microenvironmental characteristics vis-à-vis MM. We also review the current treatment strategies, acknowledging the paucity of large, randomized studies specific to this population.

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care.

Interest in the potential applications of artificial intelligence in medicine, anesthesiology, and the world at large has never been higher. The Anesthesia Research Council steering committee formed an anesthesiologist artificial intelligence expert workgroup charged with evaluating the current state of artificial intelligence in anesthesiology, providing examples of future artificial intelligence applications and identifying barriers to artificial intelligence progress. The workgroup’s findings are summarized here, starting with a brief introduction to artificial intelligence for clinicians, followed by overviews of current and anticipated artificial intelligence–focused research and applications in anesthesiology. Anesthesiology’s progress in artificial intelligence is compared to that of other medical specialties, and barriers to artificial intelligence development and implementation in our specialty are discussed. The workgroup’s recommendations address stakeholders in policymaking, research, development, implementation, training, and use of artificial intelligence–based tools for perioperative care.

Background/Objectives: Pneumonitis caused by radiotherapy for lung cancer may be missed since it often occurs only several months later. In a previous trial including patients of any age, a scoring system was tested to facilitate the correct diagnosis of radiation pneumonitis. Since elderly lung cancer patients have a greater risk of developing this complication, a separate scoring system for this group appears useful. Our prospective multi-center trial (NCT06480734) investigates a specific tool for elderly patients irradiated for lung cancer. Methods: Patients aged ≥65 years with lung cancer will complete paper-based questionnaires and rate symptoms potentially caused by pneumonitis weekly during and up to 24 weeks following radiotherapy. The total score of this symptom-based scoring system ranging from 0 to 9 points is correlated to pneumonitis. The discriminative power of the scoring system is evaluated by calculating the area under the receiver operating characteristic curve. Optimality is defined as a cut-off score with sensitivity ≥90% and specificity ≥80%. Moreover, the Youden index will be applied. Fifty-nine patients are required for the full analysis set. Assuming 5% will not qualify for this set, 65 patients should be enrolled. Moreover, patient satisfaction with the scoring system is evaluated. If the dissatisfaction rate is >20%, the system needs modifications; if the dissatisfaction rate is >40%, it is considered not useful. An optimal cut-off score facilitating the diagnosis of pneumonitis and its discrimination from other lung diseases will contribute to a corresponding mobile application to be used by elderly lung cancer patients at home.

Abstract Purpose: Selinexor is a first-in-class, central nervous system (CNS)–penetrant, oral inhibitor of exportin 1 (XPO1), the main nuclear exporter of many key tumor suppressors. We report a phase I trial of selinexor in children and adolescents with recurrent CNS and solid tumors (NCT02323880). Patients and Methods: A rolling six design was used to evaluate the maximum tolerated dose (MTD) and first dose pharmacokinetics of selinexor administered once (35–45 mg/m2) or twice (20–35 mg/m2) weekly during a 28-day cycle (part A). Ten additional patients with high-grade glioma (HGG) were treated at the MTD administered once weekly (part B). Results: In part A, 49 patients were enrolled. Continuous twice weekly dosing was limited by extended hematologic toxicity. The MTD on a twice weekly schedule for 3 weeks on/1 week off (twice weekly 3/1) was 20 mg/m2/dose. Dose-limiting toxicities (DLTs) on this schedule included fatigue, acute reversible neurologic changes, neutropenia, thrombocytopenia, and aspartate aminotransferase/alanine aminotransferase increase. On a continuous once weekly schedule, the MTD was 35 mg/m2/dose; DLTs included seizure and thrombocytopenia. In part B (HGG expansion), there were no additional DLTs observed. Non-DLTs included lymphopenia, leukopenia, neutropenia, thrombocytopenia, anorexia, fatigue, hypophosphatemia, nausea, and vomiting. There were no objective responses. The median number of cycles received was 1 (range, 1–9); eight of 59 patients (13.5%) received 5 to 9 cycles, five of whom had HGG. Conclusions: Selinexor-related toxicities were primarily hematologic and neurologic, requiring dose or dose-frequency reduction. The MTD and recommended initial phase II dose of selinexor in children and adolescents with recurrent solid and CNS tumors is 35 mg/m2/dose once weekly.