Development and Validation of Machine Learning-based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
Published Date:

Abstract

PURPOSE: Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning-based model to estimate the malignant probability of MPNs to guide decision-making.

Authors

  • Kezhong Chen
    Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Yuntao Nie
    Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Samina Park
    Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Yangming Zhang
    Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Yuan Liu
    Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
  • Bengang Hui
    Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, China.
  • Lixin Zhou
    Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Xun Wang
    College of Computer Science and Technology, China University of Petroleum, Dongying, China.
  • Qingyi Qi
    Department of Radiology, Peking University People's Hospital, Beijing, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Guannan Kang
    Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Yuqing Huang
    Department of Thoracic Surgery, Beijing Haidian Hospital, Beijing, China.
  • Yingtai Chen
    Department of Thoracic Surgery, Beijing Aerospace General Hospital, Beijing, China.
  • Jiabao Liu
    Department of Thoracic Surgery, First Hospital of Shijiazhuang, Shijiazhuang, China.
  • Jian Cui
    Department of Thoracic Surgery, Beijing Chuiyangliu Hospital, Beijing, China.
  • Mingru Li
    Department of Thoracic Surgery, Aerospace 731 Hospital, Beijing, China.
  • In Kyu Park
    Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
  • Chang Hyun Kang
    Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
  • Haifeng Shen
    Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
  • Yingshun Yang
    Department of Thoracic Surgery, Beijing Haidian Hospital, Beijing, China.
  • Tian Guan
    Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science, South China Agricultural University, Guangzhou, 510642, China.
  • Yaxiao Zhang
    Department of Thoracic Surgery, First Hospital of Shijiazhuang, Shijiazhuang, China.
  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Young Tae Kim
    Department of Obstetrics and Gynecology, Institute of Women's Medical Life Science, Yonsei University College of Medicine, Seoul, Korea. ytkchoi@yuhs.ac.
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.