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Verfasst von:Heuvelmans, Marjolein A. [VerfasserIn]   i
 van Ooijen, Peter M. A. [VerfasserIn]   i
 Ather, Sarim [VerfasserIn]   i
 Silva, Carlos Francisco [VerfasserIn]   i
 Han, Daiwei [VerfasserIn]   i
 Heußel, Claus Peter [VerfasserIn]   i
 Hickes, William [VerfasserIn]   i
 Kauczor, Hans-Ulrich [VerfasserIn]   i
 Novotny, Petr [VerfasserIn]   i
 Peschl, Heiko [VerfasserIn]   i
 Rook, Mieneke [VerfasserIn]   i
 Rubtsov, Roman [VerfasserIn]   i
 Stackelberg, Oyunbileg von [VerfasserIn]   i
 Tsakok, Maria T. [VerfasserIn]   i
 Arteta, Carlos [VerfasserIn]   i
 Declerck, Jerome [VerfasserIn]   i
 Kadir, Timor [VerfasserIn]   i
 Pickup, Lyndsey [VerfasserIn]   i
 Gleeson, Fergus [VerfasserIn]   i
 Oudkerk, Matthijs [VerfasserIn]   i
Titel:Lung cancer prediction by Deep Learning to identify benign lung nodules
Verf.angabe:Marjolein A. Heuvelmans, Peter M.A. van Ooijen, Sarim Ather, Carlos Francisco Silva, Daiwei Han, Claus Peter Heussel, William Hickes, Hans-Ulrich Kauczor, Petr Novotny, Heiko Peschl, Mieneke Rook, Roman Rubtsov, Oyunbileg von Stackelberg, Maria T. Tsakok, Carlos Arteta, Jerome Declerck, Timor Kadir, Lyndsey Pickup, Fergus Gleeson, Matthijs Oudkerk
E-Jahr:2021
Jahr:31 January 2021
Umfang:4 S.
Teil:volume:154
 year:2021
 month:04
 pages:1-4
 extent:4
Fussnoten:Gesehen am 26.05.2021
Titel Quelle:Enthalten in: Lung cancer
Ort Quelle:Amsterdam [u.a.] : Elsevier, 1985
Jahr Quelle:2021
Band/Heft Quelle:154(2021) vom: Apr., Seite 1-4
ISSN Quelle:1872-8332
Abstract:Introduction - Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. - Methods - The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). - Results - The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. - Conclusion - The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5−15 mm nodules.
DOI:doi:10.1016/j.lungcan.2021.01.027
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.1016/j.lungcan.2021.01.027
 Volltext: https://www.sciencedirect.com/science/article/pii/S0169500221000453
 DOI: https://doi.org/10.1016/j.lungcan.2021.01.027
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Deep learning
 Lung cancer
 Pulmonary nodule
 Screening
K10plus-PPN:1758930691
Verknüpfungen:→ Zeitschrift

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