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Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.
Pubmed ID
34003056 (View this publication on the PubMed website)
Digital Object Identifier
Radiology. 2021 May 18; Pages 204433
Venkadesh KV, Setio AAA, Schreuder A, Scholten ET, Chung K, W Wille MM, Saghir Z, van Ginneken B, Prokop M, Jacobs C
  • From the Department of Medical Imaging, Radboud Institute for Health Sciences, Radboudumc, Nijmegen, the Netherlands (K.V.V., A.A.A.S., A.S., E.T.S., K.C., B.v.G., M.P., C.J.); Department of Digital Technology & Innovation, Siemens Healthineers, Erlangen, Germany (A.A.A.S.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (K.C.); Department of Diagnostic Imaging, Section of Radiology, Nordsjællands Hospital, Hillerød, Denmark (M.M.W.W.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Hellerup, Denmark (Z.S.); and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.).

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected -between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three -cohorts -collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 -malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 -nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts. Results The DL algorithm significantly outperformed the PanCan model in the full cohort (AUC, 0.93 [95% CI: 0.89, 0.96] vs 0.90 [95% CI: 0.86, 0.93]; P = .046). The algorithm performed comparably to thoracic radiologists in cancer-enriched cohorts with both random benign nodules (AUC, 0.96 [95% CI: 0.93, 0.99] vs 0.90 [95% CI: 0.81, 0.98]; P = .11) and size-matched benign nodules (AUC, 0.86 [95% CI: 0.80, 0.91] vs 0.82 [95% CI: 0.74, 0.89]; P = .26). Conclusion The deep learning algorithm showed excellent performance, comparable to thoracic radiologists, for malignancy risk estimation of pulmonary nodules detected at screening CT. This algorithm has the potential to provide reliable and reproducible malignancy risk scores for clinicians, which may help optimize management in lung cancer screening. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Tammemägi in this issue.

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