ComBat Harmonization for MRI Radiomics: Impact on Nonbinary Tissue Classification by Machine Learning.

Journal: Investigative radiology
Published Date: Sep 1, 2023

Abstract

OBJECTIVES: The aims of this study were to determine whether ComBat harmonization improves multiclass radiomics-based tissue classification in technically heterogeneous MRI data sets and to compare the performances of 2 ComBat variants.

Authors

  • Doris Leithner
    From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.
  • Rachel B Nevin
    From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.
  • Peter Gibbs
    Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Michael Weber
    Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
  • Ricardo Otazo
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • H Alberto Vargas
    Department of Radiology, Body Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Marius E Mayerhoefer

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