Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.

Journal: Radiology
Published Date: May 17, 2022

Abstract

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.

Authors

  • Chaya S Moskowitz
  • Mattea L Welch
    Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. The Techna Institute for the Advancement of Technology for Health, Toronto, Ontario, Canada. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
  • Michael A Jacobs
    The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Brenda F Kurland
    From the Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd Floor, New York, NY, NY 10017 (C.S.M.); Cancer Digital Intelligence Program, University Health Network, Toronto, ON, Canada (M.L.W.); The Russell H. Morgan Department of Radiology and Radiological Science and Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (M.A.J.); ERT, Pittsburgh, Pa (B.F.K.); and School of Computing, Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada (A.L.S.).
  • Amber L Simpson
    Queen's University, Kingston, ON, CANADA.

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