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Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

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Journal of Cerebral Blood Flow and Metabolism 2024年第9期44卷 1480-1514页

作者： Kostoglou, Kyriaki Bello-Robles, Felipe Brassard, Patrice Chacon, Max Claassen, Jurgen AHR Czosnyka, Marek Elting, Jan-Willem Hu, Kun Labrecque, Lawrence Liu, Jia Marmarelis, Vasilis Z Payne, Stephen J Shin, Dae Cheol Simpson, David Smirl, Jonathan Panerai, Ronney B Mitsis, Georgios D Department of Electrical and Computer Engineering McGill University Montreal QC Canada Institute of Neural Engineering Graz University of Technology Graz Austria Departamento de Ingeniería Informática Universidad de Santiago de Chile Santiago Chile Department of Kinesiology Faculty of Medicine Université Laval QC Canada Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec Quebec QC Canada Department of Geriatrics Radboud University Medical Center Research Institute for Medical Innovation and Donders Institute Nijmegen Netherlands Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM) Department of Cardiovascular Sciences University of Leicester Leicester United Kingdom Department of Clinical Neurosciences Neurosurgery Department University of Cambridge Cambridge United Kingdom Department of Neurology and Clinical Neurophysiology University Medical Center Groningen Groningen Netherlands Medical Biodynamics Program Division of Sleep and Circadian Disorders Brigham and Women's Hospital Harvard Medical School Boston MA United States Division of Sleep Medicine Harvard Medical School Boston MA United States Laboratory for Engineering and Scientific Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA United States Institute of Applied Mechanics National Taiwan University Taipei Taiwan Institute of Sound and Vibration Research University of Southampton Southampton United Kingdom Cerebrovascular Concussion Laboratory Faculty of Kinesiology University of Calgary Calgary AB Canada Sport Injury Prevention Research Centre Faculty of Kinesiology University of Calgary Calgary AB Canada Hotchkiss Brain Institute University of Calgary Calgary AB Canada NIHR Leicester Biomedical Research Centre Bri

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA. © The Author(s) 2024.

关键词： CARNet cerebral autoregulation cerebral blood flow time-domain methods white paper

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***: Dissemination platform for deep learning models

arXiv

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arXiv 2019年

作者： Hosny, Ahmed Schwier, Michael Berger, Christoph Örnek, Evin P. Turan, Mehmet Tran, Phi V. Weninger, Leon Isensee, Fabian Maier-Hein, Klaus H. McKinley, Richard Lu, Michael T. Hoffmann, Udo Menze, Bjoern Bakas, Spyridon Fedorov, Andriy Aerts, Hugo J.W.L. Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Department of Radiology Brigham and Women's Hospital Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Harvard Medical School BostonMA United States Institute for Advanced Study Department of Informatics Technical University of Munich Munich Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Booz Allen Hamilton McLeanVA United States Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Heidelberg Germany Support Centre for Advanced Neuroimaging University Institute of Diagnostic Interventional Neuroradiology Inselspital Bern University Hospital Bern Switzerland Cardiovascular Imaging Research Center Massachusetts General Hospital Harvard Medical School Boston United States Cardiac MR PET CT Program Department of Radiology Massachusetts General Hospital BostonMA United States Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Radiology & Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Radiology and Nuclear Medicine GROW CARIM Maastricht University Maastricht Netherlands

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present *** (***), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on *** and allows both researchers and developers to utilize models out-of-the-box. *** is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences. Copyright © 2019, The Authors. All rights reserved.

关键词： Containers

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Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources

arXiv

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arXiv 2024年

作者： Hansen, Lasse Hyldig Andersen, Nikolaj Gallifant, Jack McCoy, Liam G. Stone, James K. Izath, Nura Aguirre-Jerez, Marcela Bitterman, Danielle S. Gichoya, Judy Celi, Leo Anthony Cognitive Science Aarhus University Jens Chr. Skou 2 Aarhus8000 Denmark Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy’s & St Thomas’ NHS Trust London United Kingdom Division of Neurology University of Alberta AB Canada University of Manitoba Max Rady College of Medicine WinnipegMB Canada Faculty of Computing Mbarara University of Science and Technology Mbarara Uganda Digital Health Department Fundacion Arturo Lopez Perez Santiago Chile Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women’s Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School BostonMA United States Department of Radiology Emory University School of Medicine GA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States

Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited. Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxiv, Wikipedia, and Common Crawl. The study analyzed the context in which various diseases are discussed alongside markers of race and gender. Given that LLMs are pre-trained on similar datasets, this approach allowed us to examine the potential biases that LLMs may learn and internalize. We compared these findings with actual demographic disease prevalence as well as GPT-4 outputs in order to evaluate the extent of bias representation. Results Our findings indicate that demographic terms are disproportionately associated with specific disease concepts in online texts. gender terms are prominently associated with disease concepts, while racial terms are much less frequently associated. We find widespread disparities in the associations of specific racial and gender terms with the 18 diseases analyzed. Most prominently, we see an overall significant overrepresentation of Black race mentions in comparison to population proportions. Conclusions Our results highlight the need for critical examination and transparent reporting of biases in LLM pretraining datasets. Our study suggests the need to develop mitigation strategies to counteract the influence of biased training data in LLMs, particularly in sensitive domains such as healthcare. © 2024, CC BY.

关键词： Large datasets

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Journal of Information Science and Engineering: Editorial notice

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Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

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MRSEGMENTATOR: MULTI-MODALITY SEGMENTATION OF 40 CLASSES IN MRI AND CT

arXiv

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arXiv 2024年

作者： Häntze, Hartmut Xu, Lina Mertens, Christian J. Dorfner, Felix J. Donle, Leonhard Busch, Felix Kader, Avan Ziegelmayer, Sebastian Bayerl, Nadine Navab, Nassir Rueckert, Daniel Schnabel, Julia Aerts, Hugo J.W.L. Truhn, Daniel Bamberg, Fabian Weiß, Jakob Schlett, Christopher L. Ringhof, Steffen Niendorf, Thoralf Pischon, Tobias Kauczor, Hans-Ulrich Nonnenmacher, Tobias Kröncke, Thomas Völzke, Henry Schulz-Menger, Jeanette Maier-Hein, Klaus Prokop, Mathias van Ginneken, Bram Hering, Alessa Makowski, Markus R. Adams, Lisa C. Bressem, Keno K. Department of Radiology Charité - Universitätsmedizin Berlin Freie Universität Berlin Humboldt Universität zu Berlin Germany Diagnostic Image Analysis Group Radboud University Medical Center Netherlands Department of Diagnostic and Interventional Radiology School of Medicine and Health Klinikum rechts der Isar TUM University Hospital Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Erlangen-Nürnberg Germany Laboratory for Computer Aided Medical Procedures Technical University of Munich Germany Klinikum rechts der Isar Technical University Munich Germany Department of Computing Imperial College London United Kingdom Institute for Advanced Study Technical University Munich Germany Program Mass General Brigham Harvard Medical School United States Departments of Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Hospital United States Radiology and Nuclear Medicine CARIM GROW Maastricht University Netherlands Department of Diagnostic and Interventional Radiology University Hospital Aachen Germany Department of Diagnostic and Interventional Radiology Medical Center University of Freiburg Faculty of Medicine University of Freiburg Germany Max Delbrück Center for Molecular Medicine in the Helmholtz Association Germany Clinic for Diagnostic and Interventional Radiology Heidelberg University Hospital Germany Department of Diagnostic and Interventional Radiology and Neuroradiology Universitätsklinikum Augsburg Germany Institute for Community Medicine University Medicine Greifswald Germany Experimental Clinical Research Center Charité - Universitätsmedizin Berlin Freie Universität Berlin Humboldt Universität zu Berlin Germany Division of Medical Image Computing Deutsches Krebsforschungszentrum Heidelberg Germany Department of Cardiovascular Radiology and Nuclear Medicine School of Medicine and Health German Heart Center TUM University Hospital Technical U

Background: Automated MRI analysis is limited by low resolution, lack of standardized signal intensity values, and variability in acquisition protocols. Purpose: To develop and evaluate a deep learning model for multi-organ segmentation of MRI scans. Materials and Methods: The model was trained on 1,200 manually annotated 3D axial MRI scans from the UK Biobank, 221 in-house MRI scans, and 1228 CT scans from the TotalSegmentator dataset. A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures. The annotation process began with a model based on transfer learning between CT and MR, which was iteratively refined based on manual corrections to predicted segmentations, until the entire training dataset was annotated. The model's performance was evaluated on MRI examinations obtained from the German National Cohort (NAKO) study (n=900) from the AMOS22 dataset (n=60) and from the TotalSegmentator-MRI test data (n=29). The Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD) were used to assess segmentation quality, stratified by organ and scan type. The model and its weights will be open-sourced. Results: MRSegmentator demonstrated high accuracy for well-defined organs (lungs: DSC 0.96, heart: DSC 0.94) and organs with anatomic variability (liver: DSC 0.96, kidneys: DSC 0.95). Smaller structures showed lower accuracy (portal/splenic veins: DSC 0.64, adrenal glands: DSC 0.69). On external validation using NAKO data, mean DSC ranged from 0.85 ± 0.08 for T2-HASTE to 0.91 ± 0.05 for in-phase sequences. The model generalized well to CT, achieving mean DSC of 0.84 ± 0.11 on AMOS CT data. Conclusion: MRSegmentator accurately segments 40 anatomical structures in MRI across diverse datasets and imaging protocols, with additional generalizability to CT images. This open-source model will provide a valuable tool for automated multi-organ segmentation in medical

关键词： Computerized tomography

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Iatrogenic esophageal dysmotility as a barrier to transplantation in pulmonary arterial hypertension

JHLT open

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JHLT open 2024年 5卷 100098页

作者： Michael S Miller Shelsey W Johnson Alexander R Opotowsky Michael J Landzberg Nirmal S Sharma Hilary J Goldberg Alexandra K Wong Alison S Witkin Josanna Rodriguez-Lopez Ronald H Goldstein Bradley A Maron Bradley M Wertheim Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts. Division of Pulmonary and Critical Care Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts. Department of Pulmonary Allergy Sleep and Critical Care Medicine Veterans Affairs Boston Healthcare System Boston Massachusetts. Department of Pediatrics University of Cincinnati School of Medicine Cincinnati Ohio. Adult Congenital Heart Program Heart Institute Cincinnati Children's Hospital Medical Center Cincinnati Ohio. Division of Cardiovascular Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston Massachusetts. Department of Pediatrics Boston Children's Hospital Boston Massachusetts. Department of Medicine Division of Pulmonary and Critical Care Medicine Massachusetts General Hospital and Harvard Medical School Boston Massachusetts. The Pulmonary Center Division of Pulmonary Allergy Sleep and Critical Care Boston University School of Medicine Boston Massachusetts. Department of Medicine University of Maryland School of Medicine Baltimore MD and the University of Maryland-Institute for Health Computing Bethesda Maryland.

Esophageal dysmotility is identified as a contraindication to lung transplantation at some centers due to increased risks of acute rejection, pulmonary infection, and chronic lung allograft dysfunction. Phosphodiesterase-type 5 inhibitors (PDE5i) are a cornerstone pharmacotherapy for pulmonary arterial hypertension (PAH) and are known to exert off-target effects that may impact lung transplant candidacy, including impaired esophageal contractility and decreased lower esophageal sphincter tone. We report 2 patients with PAH who were initially declined listing for lung transplantation due to iatrogenic esophageal dysmotility induced by PDE5is. Upon discontinuation of PDE5i therapy, these patients experienced significant improvement in esophageal motility within 14 days and met the criteria for transplant listing at their centers. Recognizing and mitigating the off-target effects of PDE5i medications is critical for maximizing access to transplant for patients with PAH.

关键词： esophageal dysmotility lung transplantation phosphodiesterase-type 5 inhibitor pulmonary arterial hypertension

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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LGG-52. Volumetry-based response characterization of recurrent pediatric low-grade gliomas in PNOC clinical Neuro-oncology trials

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Neuro-Oncology 2022年第SUPPLEMENT_1期24卷 i100–i100页

作者： Marc von Reppert MingDe Lin Khaled Bousabarah Ariana Familiar Ryan Velasco Angela Waanders Arastoo Vossough Amanda Haddock Theodore Nicolaides Kristin Swanson Anahita Kazerooni Cassie Kline Ali Nabavizadeh Daphne Haas-Kogan Michael Prados Joshua Rubin Sabine Mueller Mariam Aboian Department of Radiology &amp Biomedical Imaging Yale School of Medicine New Haven CT USA Visage Imaging Inc San Diego CA USA Visage Imaging GmbH Berlin Germany Center for Data Driven Discovery in Biomedicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Pediatrics Feinberg School of Medicine Northwestern University Chicago IL USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Children' s Brain Tumor Network Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Division of Pediatric Hematology/Oncology NYU Langone Medical Center New York NY USA Mathematical NeuroOncology Lab Precision Neurotherapeutics Innovation Program Mayo Clinic Phoenix AZ USA Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Division of Oncology Children’s Hospital of Philadelphia Philadelphia PA USA Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women's Hospital Harvard Medical School Boston MA USA Division of Neuro-Oncology Department of Neurological Surgery University of California San Francisco CA USA Department of Pediatrics and Neuroscience Washington University School of Medicine St. Louis MO USA Departments of Pediatrics Neurology and Neurological Surgery University of California San Francisco San Francisco CA USA

BACKGROUND : Endpoints in clinical trials using novel treatments are evaluated by RANO criteria, which provide an estimate of tumor size from two-dimensional measurements along the most prominent axial slice. However, pediatric low-grade gliomas (pLGG) commonly have variegated shapes with solid and cystic components, potentially resulting in misevaluation of true tumor volume and thus, ultimately, trial outcome. OBJECTIVES : We aim to characterize treatment response through volumetric assessment of progressive/recurrent pLGGs treated with single-agent everolimus on PNOC001 clinical trial. We seek to identify clinically relevant criteria that provide added value to response assessment beyond 2D measurements. METHODS : In a cohort of 44 patients we performed 3D-segmentation of solid, cystic and whole tumor within our PACS-framework and compared results to previously carried-out central imaging review by RANO criteria which had yielded 15 PD, 27 SD, 2 PR and 0 CR. RESULTS : 8 tumors were solid only and 36 had a mixed solid-cystic appearance. When evaluating the entire tumor (i.e. solid and cystic components combined) and using the same RANO cutoff criteria, one case changed from PR to SD, one changed from SD to PR, 3 changed from SD to PD, and 7 changed from PD to SD, resulting in an overall discordance of 27% of cases. CONCLUSION : We propose that incorporation of volumetrics into response assessment provides additional and potentially more accurate information beyond RANO-based measurements. It is crucial to note that the above-reported changes represent numerical discrepancies as opposed to true-to-reality changes in clinical outcome. Determining representative thresholds for the deployment of volumetric measures in clinical trials will be critical. Future work will include data from the PNOC002 clinical trial and evaluate inter-reader agreement and reader discordance. With the availability of PACS-based 3D-tools in neuroradiology practice, well-defined volumetric c

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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