Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy.

Journal: International journal of radiation oncology, biology, physics
PMID: 33539966

Abstract

PURPOSE: Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation pneumonitis (RP) and local control (LC)-in stage III non-small cell lung cancer (NSCLC) patients. Unlike normal tissue complication probability/tumor control probability models that use dosimetric information solely, our proposed models consider complex interactions among multiomics information including positron emission tomography (PET) radiomics, cytokines, and miRNAs. Additional time-to-event information is also used in the actuarial prediction.

Authors

  • Sunan Cui
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Randall K Ten Haken
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Issam El Naqa
    Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida.

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