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Identifying occult maternal malignancies from 1.93 million pregnant women undergoing noninvasive prenatal screening tests.

Xing JiJia LiYonghua HuangPi-Lin SungYuying YuanQiang LiuYan ChenJia JuYafeng ZhouShujia HuangFang ChenYuan HanWen YuanCheng FanQiang ZhaoHaitao WuSuihua FengWeiqiang LiuZhihua LiJingsi ChenMin ChenHong YaoLi ZengTao MaShushu FanJinman ZhangKa Yiu YuenSo Hin ChengIrene Wing Shan ChikNien-Tzu LiuJianyu ZhuSiyuan LinJeremy CaoSteve TongZhiyuan ShanWenyan LiMohammad Reza HekmatMasoud GarshasbiJavier SuelaYaima TorresJuan C CigudosaF J Pérez RuizLaura RodríguezMónica GarcíaJanez BernikEva TravenUršula RešNataša TulChing-Fong TsengDepeng ZhaoLuming SunQiong PanLi ShenMengyao DaiYuying WangJian WangHuanming YangYe YinTao DuanBaosheng ZhuMahesh ChoolaniXin JinYingwei ChenMao Mao
Published in: Genetics in medicine : official journal of the American College of Medical Genetics (2019)
The CDP algorithm can diagnose occult maternal malignancies with a reasonable PPV in multiple chromosomal aneuploidies-positive pregnant women in NIPS tests. This performance can be further improved by incorporating findings for plasma tumor markers.
Keyphrases
  • pregnant women
  • pregnancy outcomes
  • birth weight
  • machine learning
  • deep learning
  • copy number
  • weight gain
  • physical activity
  • body mass index