Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
PMID: 30992302

Abstract

PURPOSE: Radiation pneumonitis is an important adverse event in patients with non-small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2.

Authors

  • Hao Yu
    Shanghai Key Lab of Trustworthy Computing, East China Normal University, Shanghai, China.
  • Huanmei Wu
    Temple University College of Public Health, Philadelphia, PA.
  • Weili Wang
    University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio.
  • Shruti Jolly
    b Department of Radiation Oncology , University of Michigan , Ann Arbor , MI , USA.
  • Jian-Yue Jin
    University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio.
  • Chen Hu
    Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Feng-Ming Spring Kong
    University Hospitals/Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio. kong0001@hku.hk fxk132@case.edu.

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