Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.

Journal: JAMA network open
PMID:

Abstract

IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (NSCLC) is lacking.

Authors

  • Jiangdian Song
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Liaoning, Shenyang, 110819, China.
  • Lu Wang
    Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.
  • Nathan Norton Ng
    Department of Radiology, School of Medicine Stanford University, Stanford, California.
  • Mingfang Zhao
    Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
  • Jingyun Shi
    Dept of Respiratory Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Ning Wu
    Department of Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado, USA 80401.
  • Weimin Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Zaiyi Liu
    Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
  • Kristen W Yeom
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.