Thorax‐encompassing multi‐modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study
This is the first study to use multi-modality imaging along with clinical, surgical, and pathological data to predict RFS of NSCLC patients after surgery. The multi-modal DLM better stratified patients into risk groups of poor outcomes when compared to conventional staging and further stratified patients within each staging classification. This model has the potential to assist clinicians in better identifying patients that may benefit from additional therapy.
Authors
- Rahul Nayak (ORCID: https://orcid.org/0000-0003-1652-0417)
- Delphine L. Chen (ORCID: https://orcid.org/0000-0002-0150-5401)
- Perrin E. Romine (ORCID: https://orcid.org/0000-0002-0084-2107)
- Omar Daher
- Sarah A. Mattonen (ORCID: https://orcid.org/0000-0002-8172-1074)
- Jaryd R. Christie (ORCID: https://orcid.org/0000-0001-7063-0401)
- Karen Eddy
- Mohamed Abdelrazek (ORCID: https://orcid.org/0000-0001-5626-8223)
- Richard Malthaner (ORCID: https://orcid.org/0000-0002-3763-4838)
- Mehdi Qiabi (ORCID: https://orcid.org/0000-0001-9428-1904)
- Paul E. Kinahan (ORCID: https://orcid.org/0000-0001-6461-3306)
- Viswam S. Nair (ORCID: https://orcid.org/0000-0001-6376-8154)
Institutions
- Western University (CA)
- University of Washington (US)
- London Health Sciences Centre (CA)
- Fred Hutch Cancer Center (US)
- Swedish Medical Center (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-05-03
- DOI
- https://doi.org/10.1002/mp.17862
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Lawson Health Research Institute
- Cancer Research Society
- London Health Sciences Foundation
- Natural Sciences and Engineering Research Council of Canada
- National Cancer Institute