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Author Correction: Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

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Nature communications 2024年第1期15卷 6376页

作者： Alexander P L Martindale Carrie D Llewellyn Richard O de Visser Benjamin Ng Victoria Ngai Aditya U Kale Lavinia Ferrante di Ruffano Robert M Golub Gary S Collins David Moher Melissa D McCradden Lauren Oakden-Rayner Samantha Cruz Rivera Melanie Calvert Christopher J Kelly Cecilia S Lee Christopher Yau An-Wen Chan Pearse A Keane Andrew L Beam Alastair K Denniston Xiaoxuan Liu Brighton and Sussex Medical School Brighton UK. Department of Primary Care and Public Health Brighton and Sussex Medical School Brighton UK. Birmingham and Midland Eye Centre Sandwell and West Birmingham NHS Trust Birmingham UK. Christ Church University of Oxford Oxford UK. University College London Medical School London UK. Institute of Inflammation and Ageing University of Birmingham Birmingham UK. University Hospitals Birmingham NHS Foundation Trust Birmingham UK. National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre University of Birmingham Birmingham UK. York Health Economics Consortium University of York Birmingham UK. Northwestern University Feinberg School of Medicine Chicago Illinois USA. Centre for Statistics in Medicine//UK EQUATOR Centre Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. Centre for Journalology Clinical Epidemiology Program Ottawa Hospital Research Institute Ottowa Canada. Department of Bioethics The Hospital for Sick Children Toronto Canada. Genetics & Genome Biology Research Program Peter Gilgan Centre for Research & Learning Toronto Canada. Division of Clinical and Public Health Dalla Lana School of Public Health Toronto Canada. Australian Institute for Machine Learning University of Adelaide Adelaide Australia. Birmingham Health Partners Centre for Regulatory Science and Innovation University of Birmingham Birmingham UK. Centre for Patient Reported Outcomes Research (CPROR) Institute of Applied Health Research College of Medical and Dental Sciences University of Birmingham Birmingham UK. NIHR Applied Research Collaboration (ARC) West Midlands University of Birmingham Birmingham UK. NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics University of Birmingham Birmingham UK. Google Health London UK. University of Washington Seattle WA USA. Nuffield Department of Women's and Rep

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29P Long-term performance of residual cancer burden (RCB): Clinical pathological stage (CPS) and CPS+ER+Grade (CPS-EG) models in ER+HER2- breast cancer (BC) patients (pts) after neoadjuvant chemotherapy (NAC) - Role of Ki67 drop and extended endocrine therapy (EET)

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ESMO Open 2024年 9卷 103037-103037页

作者： L. Sanz Gomez E. Zamora Adelantado L. Joval E. Monescillo C. Ortiz C.F.P. Trabulo B. Rojas Garcia J. Pascual D.A. Gomez Puerto S.A. Bueno Salazar J. Rivero I. Miranda Gómez L. Rodriguez Ariza P. Gomez Pardo M. Espinosa-Bravo C. Saura Manich G. Villacampa Javierre V. Peg Cámara R. Dienstmann M. Bellet Ezquerra Breast Cancer Unit - Medical Oncology Dept. VHIO - Vall d’Hebron Institute of Oncology Barcelona Spain Medical Oncology Vall d’Hebron University Hospital Barcelona Spain OddiSey VHIO Vall d’Hebron Institute of Oncology Barcelona Spain Departamento Oncologia Medica Hospital Nossa Senhora do Rosário (Centro Hospitalar Barreiro Montijo EPE) Barreiro Portugal HM Sanchinarro Hospital Madrid Norte San Chinarro - Centro Integral Oncologico Clara Campal Madrid Madrid Spain Breast Unit HUVV - Hospital Universitario Virgen de la Victoria Malaga Spain Medical Oncology Department Vall d’Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Oncology Vall d’Hebron University Hospital Barcelona Spain Breast Cancer Unit Vall d’Hebron University Hospital Barcelona Spain Radiology Department IBCC - International Breast Cancer Center Pare Claret Atenció Primaria i a la Comunitat de Barcelona Ciutat Barcelona Spain Pathology Vall d’Hebron University Hospital Barcelona Spain Medical Oncology Dept. Vall d’Hebron Institute of Oncology - Cellex Center Barcelona Spain Breast Surgical Unit Vall d’Hebron University Hospital Barcelona Spain Breast Cancer Program Vall d’Hebron University Hospital Vall d’Hebron Institute of Oncology (VHIO) and SOLTI Breast Cancer Research Group Barcelona Spain Scientific Department SOLTI Cancer Research Group Barcelona Spain Pathology Department Vall d’Hebron University Hospital Barcelona Spain Oncology Data Science Department Grupo Oncoclinicas São Paulo Brazil Oncology Dept. Vall d’Hebron Institute of Oncology - Cellex Center Barcelona Spain

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Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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Comparison of Polarized Radiative Transfer Codes used by the EHT Collaboration

arXiv

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arXiv 2023年

作者： Prather, Ben S. Dexter, Jason Moscibrodzka, Monika Pu, Hung-Yi Bronzwaer, Thomas Davelaar, Jordy Younsi, Ziri Gammie, Charles F. Gold, Roman Wong, And George N. Akiyama, Kazunori Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Baloković, Mislav Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blackburn, Lindy Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brinkerink, Christiaan D. Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bustamante, Sandra Byun, Do-Young Carlstrom, John E. Ceccobello, Chiara Chael, Andrew Chan, Chi-Kwan Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Cordes, James M. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dhruv, Vedant Doeleman, Sheperd S. Dougal, Sean Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Ed Alyson Ford, H. Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Haworth, Kari Hecht, Michael H. Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Ikeda, Shiro Violette Impellizzeri, C.M. Inoue, Makoto Issaoun, Sara James, David J. Jannuzi, Buell T. Janssen, Michael Jeter, Britton Jiang, Wu Jiménez-Rosales, Alejandra Johnson, Michael D. Jorstad, Svetlana Joshi, Abhishek V. Jung, Taehyun Karami, Mansour Karuppusamy, Ramesh Kawashima, Tomohisa Keating, Garrett K. Kettenis, Mark Kim, Dong-Jin Kim, Jae-Young Kim, Jongsoo Kim, Junhan Kino, Motoki Koay, Jun Yi Kocherlakota, Prashant K CCS-2 Los Alamos National Laboratory P.O. Box 1663 Los AlamosNM87545 United States JILA Department of Astrophysical and Planetary Sciences University of Colorado BoulderCO80309 United States Department of Astrophysics IMAPP Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Department of Physics National Taiwan Normal University No. 88 Sec.4 Tingzhou Rd. Taipei116 Taiwan Center of Astronomy and Gravitation National Taiwan Normal University No. 88 Sec. 4 Tingzhou Road Taipei116 Taiwan Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Department of Astronomy Columbia Astrophysics Laboratory Columbia University 550 W 120th Street New YorkNY10027 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Mullard Space Science Laboratory University College London Holmbury St. Mary Dorking SurreyRH5 6NT United Kingdom Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Department of Physics University of Illinois 1110 West Green Street UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street UrbanaIL61801 United States NCSA University of Illinois 1205 W Clark St UrbanaIL61801 United States CP3-Origins University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Harvard University 20 Gar

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U, and V respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties. © 2023, CC BY.

关键词： Mean square error

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First Sagittarius A* Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole in the Center of the Milky Way

arXiv

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arXiv 2023年

作者： Akiyama, Kazunori Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Asada, Keiichi Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Baloković, Mislav Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blackburn, Lindy Blundell, Raymond Bouman, Katherine L. Bower, Geoffrey C. Boyce, Hope Bremer, Michael Brinkerink, Christiaan D. Brissenden, Roger Britzen, Silke Broderick, Avery E. Broguiere, Dominique Bronzwaer, Thomas Bustamante, Sandra Byun, Do-Young Carlstrom, John E. Ceccobello, Chiara Chael, Andrew Chan, Chi-Kwan Chatterjee, Koushik Chatterjee, Shami Chen, Ming-Tang Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Cordes, James M. Crawford, Thomas M. Crew, Geoffrey B. Cruz-Osorio, Alejandro Cui, Yuzhu Davelaar, Jordy De Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Doeleman, Sheperd S. Dougal, Sean Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fish, Vincent L. Fomalont, Ed Ford, H. Alyson Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter Gammie, Charles F. García, Roberto Gentaz, Olivier Georgiev, Boris Goddi, Ciriaco Gold, Roman Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Haworth, Kari Hecht, Michael H. Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Ikeda, Shiro Impellizzeri, C.M. Violette Inoue, Makoto Issaoun, Sara James, David J. Jannuzi, Buell T. Janssen, Michael Jeter, Britton Jiang, Wu Jiménez-Rosales, Alejandra Johnson, Michael D. Jorstad, Svetlana Joshi, Abhishek V. Jung, Taehyun Karami, Mansour Karuppusamy, Ramesh Kawashima, Tomohisa Keating, Garrett K. Kettenis, Mark Kim, Dong-Jin Kim, Jae-Young Kim, Jongsoo Kim, Junhan Kino, Motoki Koay, Jun Yi Kocherlakota, Prashant Kofuji, Yutaro Koch, Patrick M. Koyama, Shoko Kramer, Carsten Kramer, Michael Krichbaum, Thomas P. Ku Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Department of Physics & Astronomy The University of Texas at San Antonio One UTSA Circle San AntonioTX78249 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan Departament d'Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Observatori Astronòmic Universitat de València C. Catedrático José Beltrán 2 Paterna ValènciaE-46980 Spain Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Yale Center for Astronomy & Astrophysics Yale University 52 Hillhouse Avenue New HavenCT06511 United States Department of Physics University of Illinois 1110 West Green Street UrbanaIL61801 United States Fermi National Accelerator Laboratory MS209 P.O. Box 500 BataviaIL60510 United States Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue ChicagoIL60637 United States East Asian Observatory 660 N. A'ohoku Place HiloHI96720 United States 660 N. A'ohoku Place HiloHI96720 United States California Institute of Technology 1200 East California Boulevard PasadenaCA91125 United States Institute of Astronomy and Astrophysics Academia Sinica 645 N. A'ohoku Place HiloHI

We present the first Event Horizon Telescope (EHT) observations of Sagittarius A* (Sgr A∗), the Galactic center source associated with a supermassive black hole. These observations were conducted in 2017 using a global interferometric array of eight telescopes operating at a wavelength of λ = 1.3 mm. The EHT data resolve a compact emission region with intrahour variability. A variety of imaging and modeling analyses all support an image that is dominated by a bright, thick ring with a diameter of 51.8 ± 2.3 µas (68% credible interval). The ring has modest azimuthal brightness asymmetry and a comparatively dim interior. Using a large suite of numerical simulations, we demonstrate that the EHT images of Sgr A∗ are consistent with the expected appearance of a Kerr black hole with mass ∼4 × 106 M☉, which is inferred to exist at this location based on previous infrared observations of individual stellar orbits as well as maser proper motion studies. Our model comparisons disfavor scenarios where the black hole is viewed at high inclination (i > 50◦), as well as non-spinning black holes and those with retrograde accretion disks. Our results provide direct evidence for the presence of a supermassive black hole at the center of the Milky Way galaxy, and for the first time we connect the predictions from dynamical measurements of stellar orbits on scales of 103 − 105 gravitational radii to event horizon-scale images and variability. Furthermore, a comparison with the EHT results for the supermassive black hole M87∗ shows consistency with the predictions of general relativity spanning over three orders of magnitude in central mass. © 2023, CC BY.

关键词： Telescopes

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Evaluating Large Language Models for Enhancing Radiology Specialty Examination: A Comparative Study with Human Performance

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Academic radiology 2025年

作者： Hao-Yun Liu Shyh-Jye Chen Weichung Wang Chung-Hsi Lee Hsian-He Hsu Shu-Huei Shen Hong-Jen Chiou Wen-Jeng Lee Department of Medical Imaging National Taiwan University Hospital Hsin-Chu Branch Br. No.25 Lane 442 Sec.1 Jingguo Rd. Hsinchu City 300 Taiwan ROC (H.Y.L.) National Taiwan University College of Medicine No.1 Jen Ai Road Section 1 Taipei 100 Taiwan ROC (H.Y.L.). Department of Medical Imaging National Taiwan University Hospital No. 7 Zhongshan S. Rd. Zhongzheng Dist. Taipei City 100229 Taiwan (S.J.C. W.J.L.). Institute of Applied Mathematical Sciences Department of Mathematics and Data Science Degree Program National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei 106319 Taiwan (W.W.). Graduate Institute of Health and Biotechnology Law Taipei Medical University 250 Wuxing Street Taipei City Taiwan (C.H.L.). Department of Radiology Tri-Service General Hospital National Defense Medical Center No.325 Sec.2 Chenggong Rd. Neihu District Taipei City Taiwan (H.H.H.). Department of Radiology Taipei Veterans General Hospital No.201 Sec. 2 Shipai Rd. Beitou District Taipei City Taiwan (S.H.S. H.J.C.) National Yang Ming Chiao Tung University School of Medicine No. 118 Sec. 1 Zhongxiao West Road Taipei City 100 Taiwan (S.H.S. H.J.C.). Department of Medical Imaging National Taiwan University Hospital No. 7 Zhongshan S. Rd. Zhongzheng Dist. Taipei City 100229 Taiwan (S.J.C. W.J.L.). Electronic address: wenjeng@***.

RATIONALE AND OBJECTIVES:The radiology specialty examination assesses clinical decision-making, image interpretation, and diagnostic reasoning. With the expansion of medical knowledge, traditional test design faces challenges in maintaining accuracy and relevance. Large language models (LLMs) demonstrate potential in medical education. This study evaluates LLM performance in radiology specialty exams, explores their role in assessing question difficulty, and investigates their reasoning processes, aiming to develop a more objective and efficient framework for exam design. MATERIALS AND METHODS:This study compared the performance of LLMs and human examinees in a radiology specialty examination. Three LLMs (GPT-4o, o1-preview, and GPT-3.5-turbo-1106) were evaluated under zero-shot conditions. Exam accuracy, examinee accuracy, discrimination index, and point-biserial correlation were used to assess LLMs' ability to predict question difficulty and reasoning processes. The data provided by the Taiwan Radiological Society ensures comparability between AI and human performance. RESULTS:As for accuracy, GPT-4o (88.0%) and o1-preview (90.9%) outperformed human examinees (76.3%), whereas GPT-3.5-turbo-1106 showed significantly lower accuracy (50.2%). Question difficulty analysis revealed that newer LLMs excel in solving complex questions, while GPT-3.5-turbo-1106 exhibited greater performance variability. Discrimination index and point-biserial Correlation analyses demonstrated that GPT-4o and o1-preview accurately identified key differentiating questions, closely mirroring human reasoning patterns. These findings suggest that advanced LLMs can assess medical examination difficulty, offering potential applications in exam standardization and question evaluation. CONCLUSION:This study evaluated the problem-solving capabilities of GPT-3.5-turbo-1106, GPT-4o, and o1-preview in a radiology specialty examination. LLMs should be utilized as tools for assessing exam question difficu

关键词： Accuracy Evaluation GPT LLMs Radiology specialty examination

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Outcomes of CAR T-Cells Therapy in High-Grade B-Cell Lymphomas Compared to Diffuse Large B-Cell Lymphomas: A Weighted Comparison Analysis from CAR-T SIE Study

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Blood 2024年 144卷 3131-3131页

作者： Anna Dodero Giusy Ceparano Beatrice Casadei Piera Angelillo Stefania Bramanti Maria Chiara Tisi Silva Ljevar Annalisa Chiappella Barbara Botto Ilaria Cutini Patrizia Chiusolo Anna Maria Barbui Mirko Farina Alice Di Rocco Giovanni Grillo Jacopo Olivieri Mauro Krampera Lucia Brunello Anna Guidetti Pier Luigi Zinzani Paolo Corradini Hematology and Stem Cell Transplantation Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Department of Hematology and Stem Cell Transplantation Fondazione IRCCS Istituto Nazionale dei Tumori； Università degli Studi di Milano Milano Italy IRCCS Azienda Ospedaliero-Universitaria di Bologna  Istituto di Ematologia “Seràgnoli” Bologna Italy Lymphoma Unit IRCCS San Raffaele Scientific Institute Milano Italy Bone Marrow Transplantation and Cell Therapy Unit IRCCS Humanitas Research Hospital Rozzano Italy Hematology Unit San Bortolo Hospital A.U.L.S.S. 8 “Berica” Vicenza Italy Unit of Biostatistics for Clinical Research Department of Data Science Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Hematology Fondazione IRCCS Istituto Nazionale dei Tumori Milano Italy Division of Hematology Città della Salute e della Scienza Hospital and University Torino Italy Department of Cellular Therapies and Transfusion Medicine Careggi University Hospital Firenze Italy Dipartimento di Diagnostica per Immagini Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario Agostino Gemelli IRCCS Roma Italy Sezione di Ematologia Dipartimento di Scienze Radiologiche ed Ematologiche Università Cattolica del Sacro Cuore Rome Italy Department of Oncology and Hematology Azienda Socio Sanitaria Territoriale Papa Giovanni XXIII Bergamo Italy Unit of Blood Diseases and Bone Marrow Transplantation Cell Therapies and Hematology Research Program Department of Clinical and Experimental Science University of Brescia ASST Spedali Civili di Brescia University of Brescia ASST Spedali Civili Brescia Italy Hematology Section Department of Traslational and Precision Medicine Sapienza University of Rome Roma Italy Department of Hematology and Stem Cell Transplantation ASST Grande Ospedale Metropolitano Niguarda Milano Italy Clinica Ematologica Centro Trapianti e Terapie Cellulari “Carlo Melzi” Azienda Sanitaria Universitaria Integrata di Udine Udine Italy UOC di

Background: High-grade B-cell lymphomas (HGBL) account for 10% of all aggressive lymphomas and include double-hit/triple-hit (DH/TH), characterized by myc/bcl2 or myc/bcl2/bcl6 rearrangements and HGBL not otherwise specified (NOS). This category previously included also diffuse large B-cell lymphomas (DLBCL) with myc/bcl6 rearrangements. Due to biological and clinical characteristics, the risk of relapse remains high following first-line intensive treatments. Approval of anti-CD19 Chimeric Antigen Receptor (CAR) T-cells for third-line aggressive lymphomas has resulted in long-term remission in up to 40% of pts. Recent studies have demonstrated similar efficacy of CAR T-cells therapy in the HGBL subgroup versus DLBCL. However, these studies included few HGBL pts with limited long-term follow-up results. The present study compares the outcomes of patients affected by HGBL and DLBCL treated with CAR T-cells using a weighted comparison analysis and evaluates the response to salvage therapy following treatment failure. Methods : Pts diagnosed with relapsed/refractory HGBL and DLBCL who received commercial CAR T-cells in the third line or beyond betweenFebruary 2020 and January 2024 were included. All data were collected from the CAR-T SIE prospective observational study. When available, histological reports and FISH analyses were centrally collected. Propensity score inverse probability weighting (IPW) was used to create a balanced covariate distribution between pts with HGBL and those with DLBCL. Covariates used for propensity score construction included age, sex, C-reactive protein (CRP), ECOG, refractory vs relapsed, International Prognostic Index, number of prior lines of treatment, previous ASCT, response to bridge, bulky disease, CAR T-cells product, and vein-to-vein time. Results: We analyzed a population of 420 pts: 66 HGBL [n=41 HGBL DH/TH, n=20 HGBL NOS, n=5 DLBCL myc/bcl6] and 354 DLBCL [n=278 de-novo DLBCL (79%), n=76 transformed DLBCL (21%)]. All pts were tr

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Active learning of uniformly accurate inter-atomic potentials for materials simulation

arXiv

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arXiv 2018年

作者： Zhang, Linfeng Lin, De-Ye Wang, Han Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. Copyright © 2018, The Authors. All rights reserved.

关键词： Potential energy

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Publisher Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2023年第1期55卷 166页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Paul Jongjoon Choi Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

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Correction to: A phase II/III randomized clinical trial of CisPlatin plUs Gemcitabine and Nabpaclitaxel (GAP) as pReoperative chemotherapy versus immediate resection in patIents with resecTable BiliarY tract cancers (BTC) at high risk for recurrence: PURITY study

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BMC cancer 2024年第1期24卷 563页

作者： Monica Niger Federico Nichetti Lorenzo Fornaro Chiara Pircher Federica Morano Federica Palermo Lorenza Rimassa Tiziana Pressiani Rossana Berardi Andrea Casadei Gardini Elisa Sperti Lisa Salvatore Davide Melisi Francesca Bergamo Salvatore Siena Stefania Mosconi Rafaella Longarini Giuseppina Arcangeli Salvatore Corallo Laura Delliponti Stefano Tamberi Elena Fea Giovanni Brandi Ilario Giovanni Rapposelli Massimiliano Salati Paolo Baili Rosalba Miceli Silva Ljevar Ilaria Cavallo Elisa Sottotetti Antonia Martinetti Michele Droz Dit Busset Carlo Sposito Maria Di Bartolomeo Filippo Pietrantonio Filippo de Braud Vincenzo Mazzaferro Department of Medical Oncology Fondazione IRCCS Istituto Nazionale dei Tumori Via G. Venezian 1 Milan 20133 Italy. Monica.niger@istitutotumori.mi.it. Department of Medical Oncology Fondazione IRCCS Istituto Nazionale dei Tumori Via G. Venezian 1 Milan 20133 Italy. Computational Oncology Molecular Diagnostics Program National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ) Heidelberg Germany. Medical Oncology Unit Azienda Ospedaliero-Universitaria Pisana Pisa Italy. Department of Biomedical Sciences Pieve Emanuele (Milan) Humanitas University Milan Italy. Medical Oncology and Hematology Unit Humanitas Cancer Center IRCCS Humanitas Research Hospital Rozzano Milan Italy. Clinica Di Oncologia Medica A.O.U. Delle Marche Università Politecnica Delle Marche Ancona Italy. Vita-Salute San Rafaele University Milan Italy. Department of Medical Oncology San Rafaele Scientifc Institute IRCCS Milan Italy. Department of Oncology University of Turin AO Ordine Mauriziano Hospital Turin Italy. Oncologia Medica Comprehensive Cancer Center Fondazione Policlinico Universitario Agostino Gemelli IRCCS 11Oncologia Medica Rome Italy. Oncologia Medica Università Cattolica del Sacro Cuore Rome Italy. Molecular Clinical Oncology Research Unit Università Degli Studi Di Verona Verona Italy. Investigational Cancer Therapeutics Clinical Unit Azienda Ospedaliera Universitaria Integrata Verona Italy. Medical Oncology 1 Veneto Institute of Oncology IOV-IRCCS Padua Italy. Department of Hematology Oncology and Molecular Medicine Grande Ospedale Metropolitano Niguarda Milan Italy. Department of Oncology and Hemato-Oncology University of Milan Milan Italy. Medical Oncology Unit Giovanni XXIII Hospital Bergamo Italy. San Gerardo Hospital Monza Italy. Department of Medical Oncology ASST Spedali Civili Brescia Italy. Medical Oncology Unit Fondazione IRCCS Policlinico San Matteo Pavia Italy. Department of Medical Oncology Ospedale Santa Maria Delle Croci

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