Radiomics and Deep Learning Prediction of Immunotherapy-Induced Pneumonitis From Computed Tomography.

Journal: JCO clinical cancer informatics
PMID: 39977708

Abstract

PURPOSE: Primary barriers to application of immune checkpoint inhibitor (ICI) therapy for cancer include severe side effects (such as potentially life threatening pneumonitis [PN]), which can cause the discontinuation of treatment. Predicting which patients may develop PN while on ICI would improve both safety and potential efficacy because treatments could be safely administered for longer or discontinued before severe toxicity.

Authors

  • David S Smith
    Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN.
  • Levente Lippenszky
    Science and Technology Organization, Artificial Intelligence & Machine Learning, GE HealthCare, Budapest, Hungary.
  • Michele L LeNoue-Newton
    Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
  • Neha M Jain
    Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
  • Kathleen F Mittendorf
    Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
  • Christine M Micheel
    Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
  • Patrick A Cella
    Pharmaceutical Diagnostics, GE HealthCare, Chalfont St Giles, United Kingdom.
  • Jan Wolber
    Pharmaceutical Diagnostics, GE HealthCare, Chalfont St Giles, United Kingdom.
  • Travis J Osterman
    Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee.

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