Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.

Journal: Radiology
Published Date: May 18, 2021

Abstract

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]; = .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]; = .11) and size-matched benign nodules (AUC, 0.86 [95% CI: 0.80, 0.91] vs 0.82 [95% CI: 0.74, 0.89]; = .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 See also the editorial by Tammemägi in this issue.

Authors

  • Kiran Vaidhya Venkadesh
    From the Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands (N.L., C.I.S., L.H.B., M.B., E.C., W.M.v.E., P.K.G., B.G., M.G., N.H., W.H., H.J.H., C.J., R.K., M.K., K.v.L., J.M., M.O., R.S., C. Schaefer-Prokop, S.S., E.T.S., C. Sital, J.T., K.V.V., C.d.V., W.X., B.d.W., M.P., B.v.G.); Department of Radiology, Academic Medical Center, Amsterdam, the Netherlands (L.B.); Thirona, Nijmegen, the Netherlands (J.P.C., E.M.v.R.); Departments of Internal Medicine (T.D.) and Radiology (M.V.), Canisius-Wilhelmina Ziekenhuis, Nijmegen, the Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands (H.A.G.); GROW School of Oncology and Developmental Biology, Maastricht, the Netherlands (H.A.G.); Departments of Biomedical Physics and Engineering and Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands (L.v.H., I.I.); Department of Radiology, Zuyderland Medical Center, Heerlen, the Netherlands (J.K.); Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany (B.L.); Department of Radiology and Nuclear Medicine, Haaglanden Medical Center, The Hague, the Netherlands (T.v.R.V.); Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (C. Schaefer-Prokop, S.S.); and Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (J.L.S.).
  • Arnaud A A Setio
    Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany.
  • Anton Schreuder
    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.).
  • Ernst T Scholten
    From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.).
  • Kaman Chung
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Mathilde M W Wille
    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.).
  • Zaigham Saghir
  • Bram van Ginneken
    Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Mevis, Bremen, Germany.
  • Mathias Prokop
    Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Colin Jacobs
    Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.

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