Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours.

Journal: Biomedical engineering online
Published Date:

Abstract

BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).

Authors

  • Yangchun Du
    Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, 530021, Nanning, China.
  • Yanju Xiao
    Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China.
  • Wenwen Guo
    Department of Pathology, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China.
  • Jinxiu Yao
    Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China.
  • Tongliu Lan
    Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
  • Sijin Li
    Shanxi Medical University, Taiyuan 030009, PR China; Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Taiyuan 030001, PR China; Collaborative Innovation Center for Molecular Imaging, Taiyuan 030001, PR China. Electronic address: lisjnm123@163.com.
  • Huoyue Wen
    Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
  • Wenying Zhu
    Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; National Engineering Laboratory for Agri-product Quality Traceability, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China.
  • Guangling He
    Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
  • Hongyu Zheng
    Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China. Gxzhenghongyu12@163.com.
  • Haining Chen
    Department of Orthopedics, Affiliated Hospital of Jiangsu University, Zhenjiang Jiangsu, 212001, P.R.China.