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Cancers
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March 14, 2026
Improving Lung Cancer Screening Selection: A Comparative Analysis of Risk Models and Traditional Criteria in a Western European General Population
Danrong Zhong, Grigory Sidorenkov, Marcel J W Greuter, et al.
European Radiology Experimental
|
November 20, 2024
An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study
Inge A H van den Berk, Colin Jacobs, Maadrika M N P Kanglie, et al.
BMJ Open
|
July 25, 2025
Beneficial value of [<sup>18</sup>F]FDG PET/CT in the follow-up of patients with stage III non-small cell lung cancer (NVALT31-PET study): study protocol of a multicentre randomised controlled trial
Nicole E Billingy, Cornelia A Verberkt, Idris Bahce, et al.
Plos One
|
November 27, 2013
Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements
Ernst Th Scholten, Bartjan de Hoop, Colin Jacobs, et al.
Radiology. Artificial Intelligence
|
December 6, 2021
Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists
Colin Jacobs, Arnaud A A Setio, Ernst T Scholten, et al.
Radiology
|
September 16, 2025
External Test of a Deep Learning Algorithm for Pulmonary Nodule Malignancy Risk Stratification Using European Screening Data
Noa Antonissen, Kiran Vaidhya Venkadesh, Renate Dinnessen, et al.
Radiology. Artificial Intelligence
|
June 24, 2026
Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge
Dré Peeters, Bogdan Obreja, Noa Antonissen, et al.
Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer
|
October 28, 2018
Predicting Malignancy Risk of Screen-Detected Lung Nodules-Mean Diameter or Volume
Martin Tammemagi, Alex J Ritchie, Sukhinder Atkar-Khattra, et al.
European Journal of Epidemiology
|
March 21, 2023
Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial
Grigory Sidorenkov, Ralph Stadhouders, Colin Jacobs, et al.
The European Respiratory Journal
|
November 29, 2014
Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?
Ernst T Scholten, Pim A de Jong, Bartjan de Hoop, et al.
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Search research articles
Search
Showing results (81-90 of 93) with videos related to
Sort By:
Page
of 10
Cancers
|
March 14, 2026
Improving Lung Cancer Screening Selection: A Comparative Analysis of Risk Models and Traditional Criteria in a Western European General Population
Danrong Zhong, Grigory Sidorenkov, Marcel J W Greuter, et al.
European Radiology Experimental
|
November 20, 2024
An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study
Inge A H van den Berk, Colin Jacobs, Maadrika M N P Kanglie, et al.
BMJ Open
|
July 25, 2025
Beneficial value of [<sup>18</sup>F]FDG PET/CT in the follow-up of patients with stage III non-small cell lung cancer (NVALT31-PET study): study protocol of a multicentre randomised controlled trial
Nicole E Billingy, Cornelia A Verberkt, Idris Bahce, et al.
Plos One
|
November 27, 2013
Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements
Ernst Th Scholten, Bartjan de Hoop, Colin Jacobs, et al.
Radiology. Artificial Intelligence
|
December 6, 2021
Deep Learning for Lung Cancer Detection on Screening CT Scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists
Colin Jacobs, Arnaud A A Setio, Ernst T Scholten, et al.
Radiology
|
September 16, 2025
External Test of a Deep Learning Algorithm for Pulmonary Nodule Malignancy Risk Stratification Using European Screening Data
Noa Antonissen, Kiran Vaidhya Venkadesh, Renate Dinnessen, et al.
Radiology. Artificial Intelligence
|
June 24, 2026
Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge
Dré Peeters, Bogdan Obreja, Noa Antonissen, et al.
Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer
|
October 28, 2018
Predicting Malignancy Risk of Screen-Detected Lung Nodules-Mean Diameter or Volume
Martin Tammemagi, Alex J Ritchie, Sukhinder Atkar-Khattra, et al.
European Journal of Epidemiology
|
March 21, 2023
Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial
Grigory Sidorenkov, Ralph Stadhouders, Colin Jacobs, et al.
The European Respiratory Journal
|
November 29, 2014
Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?
Ernst T Scholten, Pim A de Jong, Bartjan de Hoop, et al.
Page
of 10