Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population.

Journal: Radiology. Artificial intelligence
PMID: 40172326

Abstract

Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up chest radiographs. Materials and Methods This single-center, retrospective study analyzed chest radiographs from individuals who underwent health screenings between January 2004 and June 2018. The CXR-Lung-Risk scores from baseline chest radiographs were externally tested for predicting mortality due to lung disease or lung cancer, using competing risk analysis, with adjustments made for clinical factors. The additional value of these risk scores beyond clinical factors was evaluated using the likelihood ratio test. An exploratory analysis was conducted on the CXR-Lung-Risk trajectory over a 3-year follow-up period for individuals in the highest quartile of baseline respiratory disease mortality risk, using a time-series clustering algorithm. Results Among 36 924 individuals (median age, 58 years [IQR, 53-62 years]; 22 352 male), 264 individuals (0.7%) died of respiratory illness, over a median follow-up period of 11.0 years (IQR, 7.8-12.7 years). CXR-Lung-Risk predicted respiratory disease mortality (adjusted hazard ratio [HR] per 5 years: 2.01; 95% CI: 1.76, 2.39; < .001), offering a prognostic improvement over clinical factors ( < .001). The trajectory analysis identified a subgroup with a continuous increase in CXR-Lung-Risk score, which was associated with poorer outcomes (adjusted HR for respiratory disease mortality: 3.26; 95% CI: 1.20, 8.81; = .02) compared with the subgroup with a continuous decrease in CXR-Lung-Risk score. Conclusion The open-source CXR-Lung-Risk model predicted respiratory disease mortality in an Asian cohort, enabling a two-layer risk stratification approach through an exploratory longitudinal analysis of baseline and follow-up chest radiographs. Conventional Radiography, Thorax, Lung, Mediastinum, Heart, Outcomes Analysis © RSNA, 2025 See also commentary by Júdice de Mattos Farina and Kuriki in this issue.

Authors

  • Jong Hyuk Lee
    From the Department of Radiology and Institute of Radiation Medicine, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., J.M.G., C.M.P.); Lunit Incorporated, Seoul, Republic of Korea (S.P.); Department of Radiology, Armed Forces Seoul Hospital, Seoul, Republic of Korea (J.H.L.); Department of Radiology, Seoul National University Boramae Medical Center, Seoul, Republic of Korea (K.N.J.); Department of Radiology, National Cancer Center, Goyang, Republic of Korea (K.Y.L.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (T.H.V., J.H.S.); and Department of Industrial & Information Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea (S.H.).
  • Seung Ho Choi
    Department of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul 139-743, Korea.
  • Hugo J W L Aerts
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Jakob Weiss
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
  • Vineet K Raghu
    Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA.
  • Michael T Lu
    Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston.
  • Jayoun Kim
    Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea.
  • Seungho Lee
    Yonsei-Dongsung Photodynamic Therapy Research Center, Avison Biomedical Research Center, Yonsei University College of Medicine, Seoul, South Korea.
  • Dongheon Lee
    Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea.
  • Hyungjin Kim
    Department of Radiology, Seoul National College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea (H.C., S.H.Y., S.J.P., C.M.P., J.H.L., H. Kim, E.J.H., S.J.Y., J.G.N., C.H.L., J.M.G.); CHESS Center, The First Hospital of Lanzhou University, Lanzhou, China (Q.X., J.L.); Department of Radiology, Seoul National University Bundang Hospital, Gyeonggi-do, Korea (K.H.L.); Department of Internal Medicine, Incheon Medical Center, Incheon, Korea (J.Y.K.); Department of Radiology, Seoul Medical Center, Seoul, Korea (Y.K.L.); Department of Radiology, National Medical Center, Seoul, Korea (H. Ko); Department of Radiology, Myongji Hospital, Gyeonggi-do, Korea (K.H.K.); and Department of Radiology, Chonnam National University Hospital, Gwanju, Korea (Y.H.K.).

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