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A cellular hierarchy in melanoma uncouples growth and metastasis (vol 610, pg 190, 2022)

NATURE 2022年第7934期611卷 E4-E4页

作者： Karras, Panagiotis Bordeu, Ignacio Pozniak, Joanna Nowosad, Ada Pazzi, Cecilia Van Raemdonck, Nina Landeloos, Ewout Van Herck, Yannick Pedri, Dennis Bervoets, Greet Makhzami, Samira Khoo, Jia Hui Pavie, Benjamin Lamote, Jochen Marin-Bejar, Oskar Dewaele, Michael Liang, Han Zhang, Xingju Hua, Yichao Wouters, Jasper Browaeys, Robin Bergers, Gabriele Saeys, Yvan Bosisio, Francesca van den Oord, Joost Lambrechts, Diether Rustgi, Anil K. Bechter, Oliver Blanpain, Cedric Simons, Benjamin D. Rambow, Florian Marine, Jean-Christophe Laboratory for Molecular Cancer Biology Center for Cancer Biology VIB Leuven Belgium Department of Oncology KU Leuven Leuven Belgium Laboratory of Tumor Microenvironment and Therapeutic Resistance Center for Cancer Biology VIB Leuven Belgium Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK The Wellcome Trust/CRUK Gurdon Institute University of Cambridge Cambridge UK Wellcome Trust-MRC Stem Cell Institute University of Cambridge Cambridge UK Department of General Medical Oncology UZ Leuven Leuven Belgium BGI-Shenzhen Shenzhen China VIB BioImaging Core VIB Center for Brain and Disease Research Leuven Belgium VIB Bioimaging Core VIB Center for Inflammation Research Ghent Belgium Department of Biomedical Molecular Biology Ghent University Ghent Belgium FACS Expertise Center Center for Cancer Biology VIB Leuven Belgium Center for Brain & Disease Research VIB-KU Leuven Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Data Mining and Modeling for Biomedicine Group VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Laboratory for Translational Cell and Tissue Research Department of Imaging and Pathology KU Leuven Leuven Belgium Laboratory of Translational Genetics Center for Cancer Biology VIB Leuven Belgium Laboratory of Translational Genetics Center for Human Genetics KU Leuven Leuven Belgium Herbert Irving Comprehensive Center Columbia University Irving Medical Center New York USA Laboratory of Stem Cells and Cancer Université Libre de Bruxelles (ULB) Brussels Belgium

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Bone Marrow Spatial Multi-Omics Identifies Reduced interactions between Hematopoietic Stem Cells and Myeloid Cells in Myelodysplastic Syndrome

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Blood 2024年 144卷 4577-4577页

作者： Alyssa C. Parker Joseph C. Van Amburg Tianzi Zhang Diane C. Saunders Thomas J. Gracie Pawan Bhat J. Brett Heimlich John W. Hickey Emily F. Mason Ashwin Kishtagari P. Brent Ferrell Alexander G. Bick Vanderbilt University Nashville TN School of Medicine Human Genetics Program Vanderbilt University Nashville TN Department of Computer Science Vanderbilt University Nashville TN Department of Medicine Vanderbilt University Nashville TN Internal Medicine Residency Vanderbilt University Medical Center Nashville TN Department of Medicine Vanderbilt University Medical Center Nashville TN Duke University Durham NC Department of Pathology Microbiology Immunology Vanderbilt University Medical Center Nashville TN Department of Medicine Division of Hematology and Oncology Vanderbilt University Medical Center Franklin TN Center for Immunobiology Vanderbilt University School of Medicine Nashville TN Department of Medicine Division of Genetic Medicine Vanderbilt University Medical Center Nashville TN

Introduction: Myelodysplastic syndrome (MDS) is driven by aberrant hematopoietic stem and progenitor cells and, therefore, affects the cellular constitution of bone marrow. While it is known that cell proportions change in MDS, how cell signaling and cell-cell interactions change is poorly understood. Alterations are thought to involve interactions of hematopoietic stem and progenitor cells (HSPCs) with myeloid and lymphoid cells. Understanding these changes is crucial for uncovering the mechanisms underlying MDS pathogenesis and identifying therapeutic targets. Methods: We collected bone marrow biopsies from 12 MDS patients at a single site. Utilizing Akoya's spatial multiplex immunohistochemistry method, co-detection by indexing (CODEX), we characterized the bone marrow at single-cell resolution using a panel of 24 markers defining cell types or cell states. CODEX multiplex imaging was performed using the Islet and Pancreas Analysis (IPA) Core supported by the Vanderbilt Diabetes Research center. Cell types and neighborhoods were annotated based on label-projections from publicly available data, using the Python package MaxFuse and the R package Seurat. We compared cell populations and neighborhoods between samples from 12 MDS patients and from 12 publicly available controls (Bandyopadhyay et al. Cell, 2024). Additionally, we performed single-cell RNA sequencing (scRNAseq) on bone marrow biopsies from these 12 MDS patients and an additional 5 MDS patients. Analysis of scRNAseq data was performed with the R packages SoupX, Seurat, DoubletFinder, and CellChat. Results: We identified several alterations in cell proportions and cell interactions in MDS that were consistent across spatial and scRNAseq data. First, in the spatial data, we observed an expansion of HSPCs with MDS (p-value = 4.11x10 -6 ). We also observed a reduction in the myeloid:erythroid ratio in MDS (5:1) compared to normal (8.1:1). Furthermore, analysis of cell neighborhoods in the spatial data highl

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human-centered artificial intelligence in education: Seeing the invisible through the visible

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Yang, Stephen J.H. Ogata, Hiroaki Matsui, Tatsunori Chen, Nian-Shing Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Academic Center for Computing and Media Studies Kyoto University Yoshidahonmachi Sakyo Ward Kyoto606-8501 Japan School of Human Sciences Waseda University 1-104 Totsukamachi Shinjuku-ku Tokyo169-8050 Japan Department of Applied Foreign Languages National Yunlin University of Science and Technology 123 University Road Section 3 Douliou CityYunlin64002 Taiwan

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives. © 2021 The Author(s)

关键词： Artificial intelligence

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990–2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet Infectious Diseases 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H.A. El Hafeez, Samar Abd Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Syed Shujait Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwar, Saleha Anwari, Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishnan, Senthi Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background: Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods: We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age–sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings: In 2021, diarrhoeal diseases caused an estimated 1·17 million (95% uncertainty interval 0·793–1·62) deaths globally, representing a 60·3% (50·6–69·0) decrease since 1990 (2·93 million [2·31–3·73] deaths). The most pronounced decline was in children younger than 5 years, with a 79·2% (72·4–84·6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147–221) in 1990 to 51·4 million (39·9–65·9) in 2021. In 2021, an e

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The Future of Fighter Pilot Training? Live Virtual Constructive in Large Force Exercises: Perceived and Expected Training Value

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The International Journal of Aviation Psychology 2024年第1期34卷

作者： Rogier Woltjer Robert Ramberg Henrik Artman Sanna Aronsson Mikael Mitchell Per-Anders Oskarsson a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedenb Department of Computer and Systems Sciences Faculty of Social Sciences Stockholm University Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedenc Division of Media Technology and Interaction Design School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden a Swedish Air Force Combat Simulation Center FLSC Swedish Defence Research Agency FOI Stockholm Swedend 221 Sqn Swedish Air Force Luleå Sweden e Department of Man-Technology-Organisation Swedish Defence Research Agency FOI Linköping Sweden

ABSTRACTObjectiveThis study investigates whether and how perceived training value varies among fighter pilots participating in a large force exercise (LFE), and whether they expect Live Virtual Constructive (LVC) training to enhance training value of future *** technology is maturing but its expected training value is under-investigated, especially regarding future LVC in *** pilots at a Live-flying LFE answered questionnaires, 120 responses were analyzed statistically. The questions focused on whether the sortie they just flew provided training value, generally and for specific Desired Learning Objectives (DLOs), on whether the inclusion of Virtual and Constructive entities in future LFEs would contribute to training value generally and to the DLOs specifically, and on specific LVC implementation *** of experienced training value varied across DLOs, and DLO ratings varied across Mission types. Fighter pilots were positive toward including Virtual and Constructive entities in LFEs for certain DLOs, mostly for flying a complex mission. DLOs covaried around the factors of LVC-tradeoff advantages and Live advantages, as well as temporal dimensions of planning before a mission and stages of engagement. LVC implementation lessons were *** pilots are generally moderately positive about LVC in LFEs. The measurement of perceived training value per DLO seems a relevant instrument for investigating perceived training value of an LFE and expected training value of future LVC training.

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A graph transformer-based foundation model for brain functional connectivity network

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Pattern Recognition 2025年

作者： Yulong Wang Vince D Calhoun Godfrey D Pearlson Peter Kochunov Theo G.M. van Erp Yuhui Du School of Computer and Information Technology Shanxi University Taiyuan China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Departments of Psychiatry and Neurobiology Yale University New Haven CT USA Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School The University of Texas Health Science Center Houston TX USA Department of Psychiatry and Human Behavior School of Medicine University of California Irvine Irvine CA USA Center for the Neurobiology of Learning and Memory University of California Irvine Irvine CA USA

Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain function and widely used in neuroscience. We propose a foundation model tailored for brain functional connectivity networks (FCN). Our graph transformer model integrates node and edge embeddings to extract FCN features and adapts flexibly to classification, regression, and clustering via task-specific adapters. We validate the model on fMRI data from 10,718 subjects across multiple tasks: gender classification, mental disorder classification (distinguishing schizophrenia or autism from healthy population), brain age prediction, and depressive and anxiety disorder biotyping. Compared to 14 competing methods, our model consistently outperforms them. Moreover, it facilitates biomarker discovery by identifying task-specific FC patterns. In summary, we present a novel, versatile foundation model for FCN that advances neuroimaging research through scalable and interpretable analysis.

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Complicated legacies: The human genome at 20

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science 2021年第6529期371卷 564-564页

作者： Wible, Brad School of Life Sciences Arizona State University Tempe AZ USA Center for Medical Ethics and Health Policy Baylor College of Medicine Houston TX USA School for the Future of Innovation in Society and Consortium for Science Policy and Outcomes Arizona State University Washington DC USA Center for Research on Genomics and Global Health National Human Genome Research Institute Bethesda MD USA Department of Clinical Research and Leadership George Washington University School of Medicine and Health Sciences Washington DC USA School of Humanities Nanyang Technological University Singapore Center for Translational and Policy Research on Personalized Medicine (TRANSPERS) Department of Clinical Pharmacy University of California San Francisco CA USA Philip R. Lee Institute for Health Policy Studies University of California San Francisco CA USA Helen Diller Family Comprehensive Cancer Center University of California San Francisco CA USA Department of Africana Studies Department of Sociology and Law School University of Pennsylvania Philadelphia PA USA Université de Paris INSERM 970 Paris Translational Research Centre for Organ Transplantation Paris France Doctoral School 515 Sorbonne Université Paris France Efi Arazi School of Computer Science IDC Herzliya Herzliya Israel Institute for Society & Genetics University of California Los Angeles CA 90095 USA Institute for Precision Health David Geffen School of Medicine University of California Los Angeles CA 90095 USA Division of General Internal Medicine & Health Services Research David Geffen School of Medicine University of California Los Angeles CA 90095 USA Native Nations Institute Udall Center for Studies in Public Policy University of Arizona Tucson AZ 85724 USA Mel and Enid Zuckerman College of Public Health University of Arizona Tucson AZ 85724 USA Morgridge Institute for Research and University of Wisconsin Law School Madison WI USA University of Leuven (KU Leuven) Leuven Belgium Human Righ

Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains. science invited the experts below to help explore how we got here and where we should (or ought not) be going.

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ITE Review 2019 Series (1);The Trend of Three Dimensional Image Technology

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Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers 2019年第1期73卷 90-95页

作者： Horikoshi, Tsutomu Takaki, Yasuhiro Yoshikawa, Hiroshi Koike, Takafumi Ujike, Hiroyasu Yamamoto, Hirotsugu Kiyokawa, Kiyoshi Department of Information Science Shonan Institute of Technology Kanagawa Japan Institute of Engineering Tokyo University of Agriculture and Technology Tokyo Japan College of Science and Technology Nihon University Chiba Japan Faculty of Computer and Information Sciences Hosei University Tokyo Japan Human Technology Research Institute National Institute of Advanced Industrial Science and Technology Ibaraki Japan Center for Optical Research and Education Utsunomiya University Tochigi Japan Cybermedia Center Osaka University Osaka Japan

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GREGoR: Accelerating Genomics for Rare Diseases

arXiv

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arXiv 2024年

作者： Dawood, Moez Heavner, Ben Wheeler, Marsha M. Ungar, Rachel A. LoTempio, Jonathan Wiel, Laurens Berger, Seth Bernstein, Jonathan A. Chong, Jessica X. Délot, Emmanuèle C. Eichler, Evan E. Gibbs, Richard A. Lupski, James R. Shojaie, Ali Talkowski, Michael E. Wagner, Alex H. Wei, Chia-Lin Wellington, Christopher Wheeler, Matthew T. Carvalho, Claudia M.B. Gifford, Casey A. May, Susanne Miller, Danny E. Rehm, Heidi L. Sedlazeck, Fritz J. Vilain, Eric O'Donnell-Luria, Anne Posey, Jennifer E. Chadwick, Lisa H. Bamshad, Michael J. Montgomery, Stephen B. Human Genome Sequencing Center Baylor College of Medicine HoustonTX United States Department of Molecular and Human Genetics Baylor College of Medicine HoustonTX United States Medical Scientist Training Program Baylor College of Medicine HoustonTX United States Department of Biostatistics University of Washington SeattleWA United States Department of Genetics School of Medicine Stanford University StanfordCA United States Department of Pathology School of Medicine Stanford University Stanford CA United States Stanford Center for Biomedical Ethics School of Medicine Stanford University Stanford CA United States Institute for Clinical and Translational Science University of California IrvineCA United States Division of Cardiovascular Medicine School of Medicine Stanford University Stanford CA United States Division of Genetics and Metabolism Children’s National Rare Disease Institute WashingtonDC United States Center for Genetic Medicine Research Children’s National Rare Disease Institute WashingtonDC United States Department of Genomics and Precision Medicine George Washington University WashingtonDC United States Department of Pediatrics School of Medicine Stanford University Stanford CA United States Department of Pediatrics Dvision of Genetic Medicine University of Washington SeattleWA United States Brotman Baty Institute for Precision Medicine University of Washington SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Howard Hughes Medical Institute University of Washington SeattleWA United States Department of Pediatrics Baylor College of Medicine HoustonTX United States Center for Genomic Medicine Massachusetts General Hospital BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard BostonMA United States Department of Neurology Massachusetts General Hospital Harvard Medical School BostonMA United States Stanley Center for

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://***/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics. © 2024, CC BY.

关键词： Genome

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Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach

arXiv

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arXiv 2023年

作者： Spitzer, Hannah Ripart, Mathilde Fawaz, Abdulah Williams, Logan Z.J. Project, Meld Robinson, Emma Iglesias, Juan Eugenio Adler, Sophie Wagstyl, Konrad Institute of Computational Biology Helmholtz Center Munich Munich85764 Germany University Hospital LMU Munich Munich Germany Department of Developmental Neuroscience UCL Great Ormond Street Institute for Child Health LondonWC1N 1EH United Kingdom Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Martinos Center for Biomedical Imaging MGH & Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Boston02139 United States Centre for Medical Image Computing University College London LondonWC1V 6LJ United Kingdom Wellcome Centre for Human Neuroimaging University College London LondonWC1N 3AR United Kingdom

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. This improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review. © 2023, CC BY.

关键词： Brain

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