Metastatic Lung Lesion Changes in Follow-up Chest CT
Simultaneous deep learning analysis of metastatic lung lesions in prior and current chest CT scans with SimU-Net yields superior accuracy compared with individual analysis of each scan. Implementation of SimU-Net in the radiological workflow may enhance efficiency by automatically computing key metrics used to evaluate metastatic lung lesions and their temporal changes.
Authors
- Leo Joskowicz (ORCID: https://orcid.org/0000-0002-3010-4770)
- Adi Szeskin (ORCID: https://orcid.org/0000-0002-3397-7964)
- Shalom Rochman
- Jacob Sosna
- Richard J. Lederman
- Neta Kenneth Portal
Institutions
- Hebrew University of Jerusalem (IL)
- Hadassah Medical Center (IL)
Publication Details
- Journal
- Journal of Thoracic Imaging
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1097/rti.0000000000000808
- Citations
- 1
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.42