Differential Radiomics‐Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in <scp>NLST</scp> Cohort
This study underscores the significance of delta radiomic features and balanced datasets to improve lung cancer prediction. While our findings are based on a subsample of NLST data, they provide a valuable foundation for further exploration. Further research is needed to assess the impact of harmonization on imaging-derived models. Future investigations should explore advanced harmonization techniques and additional imaging parameters to develop robust radiomics-based biomarkers of lung cancer risk.
Authors
- Philippe Desprès (ORCID: https://orcid.org/0000-0001-8926-4050)
- Leyla Ebrahimpour (ORCID: https://orcid.org/0000-0001-7243-8296)
- Venkata Manem (ORCID: https://orcid.org/0000-0003-1427-2819)
Institutions
- Emory University (US)
- Institut universitaire de cardiologie et de pneumologie de Québec (CA)
- Centre hospitalier de l'Université Laval (CA)
- Université Laval (CA)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1002/cam4.70359
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Institut de Valorisation des Données
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé
- National Cancer Institute