Ultrasound-based artificial intelligence model for prediction of Ki-67 proliferation index in soft tissue tumors.

Journal: Academic radiology
PMID: 39406581

Abstract

RATIONALE AND OBJECTIVES: To investigate the value of deep learning (DL) combined with radiomics and clinical and imaging features in predicting the Ki-67 proliferation index of soft tissue tumors (STTs).

Authors

  • Xinpeng Dai
    Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China.
  • Haiyong Lu
    Department of Ultrasound, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China (H.L.). Electronic address: 707176422@qq.com.
  • Xinying Wang
    Institute of Artificial Intelligence and Marine Robotics, School of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China. Electronic address: wxy1202@dlmu.edu.cn.
  • Yujia Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Jiangnan Zang
    Hebei Medical University, Shijiazhuang, Hebei, China.
  • Zongjie Liu
    Department of Ultrasound, Hebei Medical University Third Hospital, Hebei, China.
  • Tao Sun
    Janssen Research & Development, LLC, Raritan, NJ, USA.
  • Feng Gao
    Department of Statistics, UCLA, Los Angeles, CA 90095, USA.
  • Xin Sui
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.

Keywords

External Resources

Popular Topics
Recent Journals