Machine Learning-Driven Phenogrouping and Cardiorespiratory Fitness Response in Metastatic Breast Cancer.

Journal: JCO clinical cancer informatics
PMID: 39270146

Abstract

PURPOSE: The magnitude of cardiorespiratory fitness (CRF) impairment during anticancer treatment and CRF response to aerobic exercise training (AT) are highly variable. The aim of this ancillary analysis was to leverage machine learning approaches to identify patients at high risk of impaired CRF and poor CRF response to AT.

Authors

  • Robert T Novo
    Memorial Sloan Kettering Cancer Center, New York, NY.
  • Samantha M Thomas
    Department of Biostatistics and Bioinformatics, Duke University, Durham, NC.
  • Michel G Khouri
    Duke University Medical Center, Durham, NC.
  • Fawaz Alenezi
    Duke University Medical Center, Durham, NC.
  • James E Herndon
    Duke University Medical Center, Durham, NC.
  • Meghan Michalski
    Memorial Sloan Kettering Cancer Center, New York, NY.
  • Kereshmeh Collins
    Memorial Sloan Kettering Cancer Center, New York, NY.
  • Tormod Nilsen
    Institute of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway.
  • Elisabeth Edvardsen
    Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo 0806, Norway.
  • Lee W Jones
  • Jessica M Scott

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