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Accounting for EGFR Mutations in Epidemiologic Analyses of Non-Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data.

Sabine SchmidMei JiangM Catherine BrownAline FaresMiguel GarciaJoelle SorianoMei DongSera ThomasTakashi KohnoLeticia Ferro LealNancy DiaoJuntao XieZhichao WangDavid ZaridzeIvana HolcatovaJolanta LissowskaBeata ŚwiątkowskaDana MatesMilan SavicAngela S WenzlaffCurtis C HarrisNeil E CaporasoHongxia MaGuillermo Fernandez-TardónMatthew J BarnettGary GoodmanMichael P A DaviesMónica Pérez-RíosFiona TaylorEric Jeffrey DuellBen SchoettkerHermann BrennerAngeline AndrewAngela CoxAlberto Ruano-RaviñaJohn K FieldLoic Le MarchandYing WangChu ChenAdonina TardónSanjay SheteMatthew B SchabathHongbing ShenMaria Teresa LandiBrid M RyanKendra L SchwartzLihong QiLori C SakodaPaul J BrennanPing YangJie ZhangDavid Chistopher ChristianiRui Manuel Vieira ReisKouya ShiraishiRayjean J HungWei XuGeoffrey Liu
Published in: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology (2022)
The proposed method is generalizable in the common occurrence in which EGFR-status data are missing.
Keyphrases
  • small cell lung cancer
  • epidermal growth factor receptor
  • electronic health record
  • tyrosine kinase
  • big data
  • risk assessment
  • cell therapy
  • data analysis
  • machine learning
  • mesenchymal stem cells
  • deep learning