Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study.

Journal: The Lancet. Digital health
Published Date:

Abstract

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer.

Authors

  • Guangyao Cai
    National Clinical Research Center for Obstetrics and Gynaecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynaecology and Obstetrics, Tongji Hospital, Wuhan, China.
  • Fangjun Huang
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Yue Gao
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Jianhua Chi
    Cancer Biology Research Center, Key Laboratory of Chinese Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
  • Jincheng Xie
    School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China.
  • Linghong Zhou
    Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
  • Yanling Feng
    Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • He Huang
  • Ting Deng
    Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Yun Zhou
    MOE Key Lab of Environmental and Occupational Health, School of Public Health, Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430030, China.
  • Chuyao Zhang
    Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Xiaolin Luo
    Department of Gynecologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Xing Xie
    Microsoft Research, China. Electronic address: xing.xie@microsoft.com.
  • Qinglei Gao
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
  • Xin Zhen
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Jihong Liu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.