Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy.

Journal: BMC medicine
PMID: 36008835

Abstract

BACKGROUND: Accurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of expertise and reliable medical history. This study aimed to develop a deep learning (DL) radiomics model based on B-mode and color Doppler ultrasound images for assisting radiologists to improve their diagnoses of the etiology of unexplained CLA.

Authors

  • Yangyang Zhu
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Zheling Meng
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Xiao Fan
    Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.
  • Yin Duan
    Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou, China.
  • Yingying Jia
    Department of Ultrasound, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China.
  • Tiantian Dong
    Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.
  • Yanfang Wang
    Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, 730030, China.
  • Juan Song
    Chongqing Metropolitan College of Science and Technology, Yongchuan, Chongqing 402167, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Fang Nie
    Department of Ultrasound, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, China.

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