Radiomics‐based kidney lesion classification: Mitigating batch effect with nested combat harmonization
These findings underscore the potential of radiomics-based machine learning models to enchance diagnostic accuracy and patient management in clinical practice. The use of harmonization techniques, such as, Nested Combat is crucial in achieving reliable and generalizable predictive models for renal oncology.
Authors
- Philippe Desprès (ORCID: https://orcid.org/0000-0001-8926-4050)
- Yannick Lemaréchal (ORCID: https://orcid.org/0000-0002-2494-2831)
- Leyla Ebrahimpour (ORCID: https://orcid.org/0000-0001-7243-8296)
- Venkata Manem (ORCID: https://orcid.org/0000-0003-1427-2819)
- Mohsen Agharazii (ORCID: https://orcid.org/0000-0002-7628-5757)
- Niloofar Ziasaeedi
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Centre hospitalier universitaire de Québec (CA)
- Institut universitaire de cardiologie et de pneumologie de Québec (CA)
- Université Laval (CA)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/mp.18070
- Citations
- 1
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé