Citations

Abstract BACKGROUND Ovarian cancer rarely metastasises to the brain, thus studies reporting the outcomes of affected patients are lacking. Stereotactic radiosurgery (SRS) is now the mainstay of management for patients with brain metastases (BM) from most primary sites, but little evidence of its efficacy in ovarian cancer is available. The current study was undertaken to provide guidance for SRS management using pooled data from multiple institutions. MATERIAL AND METHODS Centers participating in the IRRF (International Radiosurgery Research Foundation) were asked to provide outcome data for patients who had SRS for ovarian cancer brain metastases between 2020 and 2024 and at least one clinical and imaging follow-up after the procedure. Primary endpoints included survival from SRS, local tumor response according to RANO-BM criteria and occurrence of adverse radiation effects (ARE). Cox regression analyses were performed to identify variables impact each endpoint. RESULTS 128 patients had SRS for a total of 532 BM treated. Epithelial histology was the most common (91%). Median age at SRS was 62 years (IQR 56-70). Median KPS was 80% (IQR 80-90%) and 71.9% of patients had neurological symptoms at presentation. Other active systemic metastases were present in 41.4%. The median number of treated BM was 2 (IQR1-3) and the median cumulative treatment volume was 6 cc (IQR 2-12.9). The median margin dose was 18 Gy (IQR 16-20). At last follow-up, 19.5% of patients were still alive. The median overall survival (OS) after SRS was 27 months, and 6-, 12- and 24-month OS was 83.8%, 74.2% and 52.2%, respectively. Multivariate analyses revealed that increasing age at SRS (HR 1.02, p=0.05), active systemic metastases (HR 2, p=0.005) and increasing number of brain metastases (HR 1.05, p=0.04) were associated with worse survival, while repeating SRS (HR 0.4, p=0.002) led to improved survival. Local failure occurred in 12.8% of treated BM. Actuarial progression-free survival (PFS) at 6, 12 and 24 months was 92.3%, 86.6% and 70.3%, respectively. Only prior WBRT (HR 4.36, p=0.03) led to worse local control on multivariate analyses. New remote BM appeared in 49.5% of patients and 14.5% suffered from leptomeningeal dissemination. ARE were seen in 12.1% of BM but were symptomatic in only 3.2%. On multivariate analyses, prior SRS (HR 3.13, p=0.002) was associated with increased risk of ARE. CONCLUSION Ovarian cancer brain metastases can be safely and effectively managed with SRS as the primary treatment modality in most patients.

Abstract Introduction This study investigated the impact of visual editing of sleep recordings for use in predicting neurodegenerative disorder (NDD) risk. Methods All participants underwent in-home Sleep Profiler studies. Forty-four were diagnosed with mild cognitive impartment (MCI = 40) or Alzheimer’s disease (AD = 4)(MCI/AD: 55% women, 72.6+/-8.6 years), 26 with REM sleep behavior disorder (RBD: women = 27%, age = 67+/-9.8), and 29 were controls (NC: women = 69%, age = 63+/-11.5). A 4-class machine learning algorithm used age plus eight sleep biomarkers to assign NDD risk probabilities. Records with CG-assigned probabilities >70% were labeled Probably-Normal, and from 45-70% Likely-Normal. Similar thresholds were used to assign Likely- or Probably-Abnormal. NDD risk characterizations were generated based on auto-scoring (“auto”), and again after the recordings were visually edited for accuracy (“edited”). Results Comparing the auto- vs. edited-NDD classifications in the MCI/AD group, the same NDD risk classifications were observed in 91% (39/43), with 2 cases shifting from Likely-Normal to Probably-Normal, one shifted from Likely-AD to Likely-Normal with AD Indications, and one changed from Probably-AD to Likely-AD. The same NDD risk classifications were observed in 81% (21/26) of the RBD cases, 3 shifted from Probably-Abnormal to Likely-Abnormal, one shifted from Likely-Normal to Probably-Normal, and one changed from Likely-Abnormal to Likely-Normal. The same NDD risk classifications were observed in 86% of the NC group, with 4 cases shifting from Likely-Normal to Probably-Normal. Conclusions These findings suggest that the likelihood of a gross misclassification of NDD risk resulting from auto-detected sleep biomarkers is relatively low with inconsistencies directed toward slightly increased NDD risk classifications.

Abstract Introduction This study investigated differences in opinions about neurodegenerative disorders (NDD) in countries with large populations and expanded access to healthcare. Methods A 5-point Likert scale survey assessed opinions of 30–59-year-olds, pertaining to their parents and NDD conditions. 100 surveys, translated into native languages, were acquired in eight developed countries. Positive (somewhat- or strongly-agree) vs. other responses, sex, and country were submitted to logistic regression and Chi-squared analyses. Results “It is important for me to know if my parent has a risk for developing a NDD condition in the future, even if nothing can be currently done to prevent or cure those diseases” had higher positive responses in Australia (92%) compared to Germany (74%) and the USA (78%). “I believe that knowing about my parent’s risk for developing a NDD condition will help me assist in the planning of their future” had higher number of positive responses in Australia (91%) versus Japan (70%), Germany (76%), Italy (80%) and UK (80%). “If my parent(s) was found to be at risk for a NDD condition, I would want to learn more about medications and adaptive therapies that could delay onset” was responded to more positively in Australia (92%), Canada (78%), France (87%), Germany (78%), Italy (88%), UK (88%), and USA (81%) versus Japan (60%). Conclusions Australians responded more positively to questions related to care of a parent with a NDD disorder. These findings suggest cultural differences that could influence education and support systems requirements for sleep medicine providers and NDD patients.

ABSTRACT Background As head and neck cancer survival rates improve, there is growing demand for quality survivorship care and tools to predict patient adherence; this study aims to evaluate an advanced practice provider ( APP )‐led survivorship clinic and assess these key metrics. Methods We performed a retrospective review of 213 head and neck cancer patients treated with curative radiation therapy between 2016 and 2023, to assess follow‐up adherence in an advanced practice provider–led survivorship clinic. Results With a median follow‐up of 2.4 years, follow‐up care metrics identified high completion percentages (> 90%) for lung cancer screening, smoking cessation, thyroid screening, dental evaluation, xerostomia, dysphagia, and dysgeusia. The HOUsing ‐based SocioEconomic Status ( HOUSES ) index at diagnosis showed a significant adherence difference (median percentile, 78% vs. 55%, adherent vs. nonadherent; p = 0.03). Conclusions High adherence rates with guideline‐defined care metrics are achievable in an advanced practice provider‐led head and neck cancer survivorship clinic. The HOUSES index may assist in predicting nonadherence; however, further studies are needed.

Abstract Perioperative low cardiac output syndrome (LCOS) remains a significant complication in adult cardiac surgery, contributing to substantial morbidity, prolonged intensive care, and increased mortality. Its incidence is expected to rise further due to the evolving complexity of referred surgical patients, often characterised by advanced age, multiple comorbidities, challenging anatomy, and impaired haemodynamics. Despite advances in pharmacological and perioperative care, outcomes for high-risk patients have not shown significant improvement, prompting interest in temporary mechanical circulatory support (tMCS) as a proactive strategy. This Expert Consensus Document from the European Association for Cardio-Thoracic Surgery (EACTS) presents the first dedicated guidance on the pre-emptive use of tMCS in high-risk adult cardiac surgical patients. Developed by a multidisciplinary task force, it emphasises structured risk stratification, early initiation, and individualised device management informed by interdisciplinary Heart Team discussions. The document proposes clinical pathways for patient selection, defines criteria for tMCS initiation, and provides practical algorithms for various scenarios, including advanced heart failure, cardiogenic shock, and post-cardiotomy LCOS. It reviews the current evidence on available tMCS devices, such as intra-aortic balloon pumps, microaxial flow pumps, veno-arterial extracorporeal life support and hybrid strategies, and addresses perioperative care, intensive care unit protocols, ethical considerations, as well as informed consent and support withdrawal. Despite promising results, substantial knowledge gaps remain, including long-term outcome data, device selection criteria, and cost-effectiveness analyses. This consensus aims to support clinical decision-making, standardise practice, and stimulate research to improve outcomes in a growing population of high-risk surgical patients.

Abstract Background Anti-citrullinated peptide antibodies (ACPA) (tested as anti-cyclic citrullinated peptide [CCP] antibodies) are frequently ordered in the evaluation of patients with suspected rheumatoid arthritis (RA) and considered to be specific biomarkers for RA. The 2010 American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria for RA include both ACPA and rheumatoid factor in their scoring system, despite limited standardization of commercially available anti-CCP antibody tests. In an attempt to overcome this limitation, the classification criteria recommends using multiples of the assay-specific upper limit of normal (ULN) to normalize results and standardize classification. This study evaluates the correlation between normalized results from different assays and the concordance in result classification according to the ACR/EULAR criteria. Methods A total of 265 samples with ACPA ordered as part of routine clinical care were tested on four commercial anti-CCP antibody assays, including QUANTA Lite CCP3 IgG ELISA (Werfen) (Negative: <20 Units), QUANTA Flash CCP3 chemiluminescence immunoassay (Werfen) (Negative: <20 Chemiluminescent Units), EUROIMMUN Anti-CCP ELISA (IgG) (EUROIMMUN) (Negative: <=5 Relative Units/mL), and Elecsys anti-CCP electrochemiluminescence immunoassay (Roche) (Negative: <17.0 Units/mL). The QUANTA Lite and EUROIMMUN ELISA were performed on the Agility platform (Dynex), QUANTA Flash was performed on BioFlash platform (Werfen), and Elecsys anti-CCP was performed on Cobas e601 platform (Roche). All testing was performed according to manufacturer’s instructions. Results from each assay were converted to the multiples of assay-specific ULN as the normalized results. Regression analysis was performed, and the coefficient of determination (R²) was calculated to assess the correlation between paired assays. The concordance in result classification across assays was also investigated using both assay specific cut-offs and the ACR/EULAR criteria-defined cut-offs: Negative (<=1xULN), Low positive (>1xULN and <3xULN), High positive (>=3xULN). Results Using assay-specific cut-offs, 63 samples were negative on all assays. 202 samples were positive on at least one assay, of which 79 (39%) were positive across all assays. The overall concordance of qualitative result was 54% ([63+79]/265). After result normalization using multiples of assay-specific ULNs, the highest correlation was observed between the EUROIMMUN ELISA and Elecsys anti-CCP (R²=0.83), despite differences in methodology and manufacturer. The lowest correlation was observed between QUANTA Lite and QUANTA Flash (R²=0.49), even though both are from the same manufacturer although methodologies differ. When applying the criteria-defined cut-offs, 64 samples were negative on all assays. 130 samples were classified as low positive on at least one assay; however, only 3 (2%) samples were classified as low positive across all assays. 103 samples were classified as high positive on at least one assay, of which 54 (52%) were classified as high positive across all assays. With result normalization, the overall concordance in result classification across all assays using the criteria-defined cut-offs were 46% ([64+3+54]/265). Conclusion Despite normalization using multiplies of assay-specific ULN values, the inter-assay correlation remains suboptimal, and the concordance in result classification is limited. These findings suggest that the anti-CCP antibody results from different assays may not be interchangeable even after normalization as recommended by current criteria, potentially impacting disease classification.

Abstract Background The use of autonomous mobile robots (AMRs) to move materials/supplies is long-standing in manufacturing/warehouse settings and is expanding into hospitals and clinical laboratories. The recently launched Proxie robot (Collaborative Robotics i.e. Cobot) is an AMR that can autonomously move existing carts between defined locations. However, AMRs pose new challenges compared to traditional lab automation. The aims of this study were to: i) perform functional validation to verify Proxie*s manufacturer performance specifications within the laboratory, ii) assess the risk of using Proxie to deliver laboratory specimens, and iii) pilot the use of the Proxie robot to autonomously deliver loaded carts from the Pathology case intake area to the sorting station at Mayo Clinic Laboratories. Methods Functional validation of Proxie consisted of verifying manufacturer’s performance specifications for the following robot parameters (5 test runs): maximum speed, emergency-stop, and ability to maintain localization, travel autonomously, stop and/or avoid moving/fixed objects, and park/unpark. Risk assessment for the robotic use case was performed by a multi-disciplinary team utilizing an AMR risk assessment tool developed by the institutional robotics group. Hazards were categorized as mechanical, electrical, noise, ergonomic, environmental, material/substance or combination risks. Injury severity, exposure(frequency), and avoidance scores were assigned to each hazard to determine negligible-, low-, medium-, high- or very high-risk levels. Proxie’s workstream was evaluated weekly over four weeks by measuring number of cart deliveries, total time/miles traveled, % autonomous deliveries(goal >80%), % on-time deliveries, average±standard deviation(SD) time (min:sec±min:sec) for delivery/return trips, and dwell times at intake/sorting stations. Results Manufacturer’s performance specifications were verified in 100% of test runs. Proxie traveled at human walking speed, stopped within 1 second of emergency stop being enabled, maintained localization, and traveled autonomously for =5 consecutive workflow cycles. Proxie navigated through spaces =4’ wide along fixed pathways and stopped safely when encountering obstacles. Proxie navigated around people and carts in the main 6’ hallway if they were blocking <2’ hallway width. For objects blocking >2’, Proxie stopped and resumed motion after the object moved. After task completion, Proxie side-shifted into the AMR parking space stopping no closer than 7.8” from any object. Twenty-four potential hazards were identified through the risk assessment tool with all risk levels scored as negligible or low. Proxie completed 304 cart deliveries/returns operating 17:35 hours and traveling 11.5 miles during the pilot. The % autonomous deliveries for week 1-4 was 82%,88%,91% and 88%, respectively with =99% on-time deliveries. Delivery from pathology case intake to sorting station averaged 5:43±0:25 min and return trips averaged 3:32±0:34. Proxie dwell times were 13:10±0:33 and 37:34±0:23 min at the pathology intake and sorting stations, respectively. Conclusions The Proxie AMR was capable of successfully and autonomously moving carts with laboratory specimens between the pathology intake and sorting stations of the laboratory during the pilot study. Adding Proxie to this workstream translated to eliminating 5,800 steps and 4 hours 24 min of human cart-pushing per week. Incorporating Proxie into laboratory operations as a cart-moving AMR would increase efficiency and allow laboratory staff to focus on technical, non-repetitive work.

Abstract Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems (“clinical collaborators”), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task‐specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.