3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279).

Journal: International journal of radiation oncology, biology, physics
PMID: 34229050

Abstract

PURPOSE: To develop and validate a pretreatment computed tomography (CT)-based deep-learning (DL) model for predicting the treatment response to concurrent chemoradiation therapy (CCRT) among patients with locally advanced thoracic esophageal squamous cell carcinoma (TESCC).

Authors

  • Xiaoqin Li
    Department of Pulmonary and Critical Care Medicine, Fujian Provincial Hospital, Fuzhou, China.
  • Han Gao
    Zhejiang Construction Investment Environment Engineering Co, Ltd., Hangzhou, 310013, PR China.
  • Jian Zhu
  • Yong Huang
    State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection of Ministry Education, Guangxi Normal University, Guilin 541004, China.
  • Yongbei Zhu
  • Wei Huang
    Shaanxi Institute of Flexible Electronics, Northwestern Polytechnical University, 710072 Xi'an, China.
  • Zhenjiang Li
    Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China; Shandong Medical Imaging and Radiotherapy Engineering Center (SMIREC), Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
  • Kai Sun
    Department of Materials Science and Engineering, Jinan University.
  • Zhenyu Liu
    School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jie Tian
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Baosheng Li
    Department of Radiation Oncology, Shandong Cancer Hospital, Shandong University, Jinan, 250117, China.

Keywords

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