Machine Learning–Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer
Phenotypic clustering identified two subgroups with unique baseline characteristics and CRF outcomes. The identification of CRF phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions among patients with cancer.
Authors
- Elisabeth Edvardsen (ORCID: https://orcid.org/0000-0002-9414-6638)
- Michel G. Khouri (ORCID: https://orcid.org/0009-0002-6235-5176)
- Samantha Marie Thomas (ORCID: https://orcid.org/0000-0002-1561-9303)
- Fawaz Alenezi (ORCID: https://orcid.org/0000-0001-8171-2250)
- Tormod S. Nilsen (ORCID: https://orcid.org/0000-0002-9380-2808)
- Lee W. Jones (ORCID: https://orcid.org/0000-0002-3716-8793)
- Robert Novo (ORCID: https://orcid.org/0000-0003-1401-5046)
- Jessica M. Scott (ORCID: https://orcid.org/0000-0002-4845-5800)
- Meghan Michalski
- James E. Herndon
- Kereshmeh Collins
Institutions
- Oslo University Hospital (NO)
- Memorial Sloan Kettering Cancer Center (US)
- Cornell University (US)
- Duke Medical Center (US)
- Norwegian School of Sport Sciences (NO)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1200/cci.24.00031
- Citations
- 1
- Primary Topic
- Cancer survivorship and care
- Type
- article
- Field-Weighted Citation Impact
- 0.28