Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To evaluate the performance of a deep learning radiomic nomogram (DLRN) model at predicting tumor relapse in patients with soft tissue sarcomas (STS) who underwent surgical resection.

Authors

  • Shunli Liu
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
  • Weikai Sun
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
  • Shifeng Yang
    Department of Radiology, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, Shandong, P.R. China.
  • Lisha Duan
    Department of Radiology, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, Hebei, China.
  • Chencui Huang
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Jingxu Xu
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Feng Hou
    Department of Pathology, The Affiliated Hospital of Qingdao University, Shandong, China.
  • Dapeng Hao
    Department of Radiology, The Affiliated Hospital of Qingdao University, Shinan Jiangsu 16 Rd, Qingdao, Shandong 266003, China.
  • Tengbo Yu
    Department of Sports Medicine, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China. ytb8912@163.com.
  • Hexiang Wang
    Department of Radiology, The Affiliated Hospital of Qingdao University, Shinan Jiangsu 16 Rd, Qingdao, Shandong 266003, China.