Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules.

Journal: Radiology
Published Date: Aug 1, 2023

Abstract

Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT examination (AUC, 0.85 [95% CI: 0.78, 0.92; = .002]; and AUC, 0.89 [95% CI: 0.84, 0.95; = .01]) and the PanCan model (AUC, 0.64 [95% CI: 0.53, 0.74; < .001]; and AUC, 0.63 [95% CI: 0.52, 0.74; < .001]). Conclusion A DL algorithm using current and prior low-dose CT examinations was more effective at estimating 3-year malignancy risk of pulmonary nodules than established models that only use a single CT examination. Clinical trial registration nos. NCT00047385, NCT00496977, NCT02837809 © RSNA, 2023 See also the editorial by Horst and Nishino 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.).
  • Tajwar Abrar Aleef
    From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (K.V.V., T.A.A., E.T.S., B.v.G., M.P., C.J.); Robotics and Control Laboratory, The University of British Columbia, Vancouver, Canada (T.A.A.); Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Hellerup, Denmark (Z.S.); Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Z.S.); Section of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy (M.S., N.S.); Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy (M.S., U.P.); and Department of Radiology, University Medical Center Groningen, Groningen, the Netherlands (M.P.).
  • 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.).
  • Zaigham Saghir
  • Mario Silva
    Department of Medicine and Surgery, University of Parma, Parma, Italy.
  • Nicola Sverzellati
    Department of Medicine and Surgery, University of Parma, Parma, Italy.
  • Ugo Pastorino
    Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy.
  • 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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