A comparison of an integrated and image-only deep learning model for predicting the disappearance of indeterminate pulmonary nodules.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: Apr 11, 2025

Abstract

BACKGROUND: Indeterminate pulmonary nodules (IPNs) require follow-up CT to assess potential growth; however, benign nodules may disappear. Accurately predicting whether IPNs will resolve is a challenge for radiologists. Therefore, we aim to utilize deep-learning (DL) methods to predict the disappearance of IPNs.

Authors

  • Jingxuan Wang
    College of Life Science and Bio-engineering, Beijing University of Technology, Beijing 100124, P.R.China.
  • Jiali Cai
    Department of Radiology, Changzheng Hospital of the Navy Medical University, Shanghai, China.
  • Wei Tang
    Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Ivan Dudurych
    Department of Radiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands. i.dudurych@umcg.nl.
  • Marcel van Tuinen
    Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands.
  • Rozemarijn Vliegenthart
    University of Groningen, University Medical Center Groningen, Department of Radiology, Hanzeplein 1, 9713 GZ Groningen, The Netherlands.
  • Peter van Ooijen
    Department of Radiation Oncology, Coordinator Machine Learning Lab, Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.

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