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CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19 Definition and Evaluation

Autor: Hester A. Gietema, Bram Geurts, Mathias Prokop, Ludo F. M. Beenen, Lauran Stöger, Monique Brink, Cornelia M. Schaefer-Prokop, Wouter M. van Everdingen, Tjalco van Rees Vellinga, Bram van Ginneken, Jasenko Krdzalic, Henriette M. E. Quarles van Ufford

Publikováno v: Radiology, 296, 2, pp. E97-E104
Radiology, 296, E97-E104
Radiology, 296(2), E97-E104. Radiological Society of North America Inc.
Radiology, 296(2), E97-E104. Radiological Society of North America, Inc.

Background A categorical CT assessment scheme for suspicion of pulmonary involvement of coronavirus disease 2019 (COVID-19 provides a basis for gathering scientific evidence and improved communication with referring physicians. Purpose To introduce t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0aa203041a4414c7f8f14ede75d8fc8e
https://hdl.handle.net/2066/221440

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Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists

Autor: Bram Geurts, Mario Silva, Paul F. Pinsky, Erik Ranschaert, Monique Brink, Bram van Ginneken, Colin Jacobs, Stephen Lam, Pim A. de Jong, Haimasree Bhattacharya, Steven Schalekamp, Keyvan Farahani, Kaman Chung, Paul K. Gerke, Arnaud Arindra Adiyoso Setio, Joke Meersschaert, Anand Devaraj, Firdaus A. A. Mohamed Hoesein, Ernst T. Scholten

Publikováno v: RADIOLOGY ARTIFICIAL INTELLIGENCE
Radiology: Artificial Intelligence, 3
Radiology: Artificial Intelligence, 3, 6
Radiol Artif Intell

An observer study showed that two of the three top-performing algorithms from a public competition (Kaggle Data Science Bowl 2017) attained performances that were not significantly worse than that of 11 radiologists for estimating lung cancer risk on

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::234071e147645c5633209c0ebd5b338b
https://hdl.handle.net/https://repository.ubn.ru.nl/handle/2066/245169

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Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

Publikováno v: Radiology, 298(1), E18-E28. Radiological Society of North America Inc.
Radiology, 298, E18-E28
Radiology, 298(1), E18-E28. Radiological Society of North America, Inc.
Radiology, 298, 1, pp. E18-E28
Radiology, 298(1), E18-E28. RADIOLOGICAL SOC NORTH AMERICA

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29d2019f6b62530a28afdd6892dc6b97
https://doi.org/10.1148/radiol.2020202439

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Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement

Autor: Ralf Sprengers, Sarah J. van Riel, Ernst T. Scholten, Bram van Ginneken, Cornelia M. Schaefer-Prokop, Colin Jacobs, Anton Schreuder, Rianne Wittenberg, Onno M. Mets, Bartjan de Hoop, Mathias Prokop, Bram Geurts, Mathilde M. W. Wille

Publikováno v: Radiology: Imaging Cancer, 3(5):e200160. Radiological Society of North America Inc.
Radiology: Imaging Cancer, 3
Radiol Imaging Cancer
Jacobs, C, Schreuder, A, van Riel, S J, Scholten, E T, Wittenberg, R, Wille, M M W, de Hoop, B, Sprengers, R, Mets, O M, Geurts, B, Prokop, M, Schaefer-Prokop, C & van Ginneken, B 2021, ' Assisted versus manual interpretation of low-dose CT scans for lung cancer screening : Impact on lung-RADS agreement ', Radiology: Imaging Cancer, vol. 3, no. 5, e200160 . https://doi.org/10.1148/rycan.2021200160
Radiology: Imaging Cancer, 3, 5

An observer study with seven radiologists showed that using a dedicated CT lung screening viewer (with computer-aided detection, a segmentation algorithm, and automatic Lung Imaging Reporting and Data System [Lung-RADS] categorization) improved inter

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78f197ea07a3c10fa7a97b3960423bb3
https://publica.fraunhofer.de/handle/publica/415913

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Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management

Autor: Bartjan de Hoop, Sarah J. van Riel, Ralf Sprengers, Mathilde M. W. Wille, Colin Jacobs, Bram van Ginneken, Onno M. Mets, Cornelia M. Schaefer-Prokop, Bram Geurts, Mathias Prokop, Ernst T. Scholten, Rianne Wittenberg

Publikováno v: European Radiology, 29, 2, pp. 924-931
European Radiology, 29(2), 924-931. Springer Verlag
European Radiology
van Riel, S J, Jacobs, C, Scholten, E T, Wittenberg, R, Winkler Wille, M M, de Hoop, B, Sprengers, R, Mets, O M, Geurts, B, Prokop, M, Schaefer-Prokop, C & van Ginneken, B 2019, ' Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management ', European Radiology, vol. 29, no. 2, pp. 924-931 . https://doi.org/10.1007/s00330-018-5599-4
European Radiology, 29, 924-931
European radiology, 29(2), 924-931. Springer Verlag

Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screenin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::927e62b7f04912c6031bfc56df945b10
https://hdl.handle.net/2066/201229

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