Metastatic Lung Lesion Changes in Follow-up Chest CT: The Advantage of Deep Learning Simultaneous Analysis of Prior and Current Scans With SimU-Net.

Journal: Journal of thoracic imaging
PMID: 39808543

Abstract

PURPOSE: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.

Authors

  • Neta Kenneth Portal
    School of Computer Science and Engineering, The Hebrew University of Jerusalem.
  • Shalom Rochman
    The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 9190401, Israel.
  • Adi Szeskin
    The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 9190401, Israel.
  • Richard Lederman
    Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
  • Jacob Sosna
    Department of Radiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Leo Joskowicz
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.

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