Prediction of esophageal fistula in radiotherapy/chemoradiotherapy for patients with advanced esophageal cancer by a clinical-deep learning radiomics model : Prediction of esophageal fistula in radiotherapy/chemoradiotherapy patients.

Journal: BMC medical imaging
PMID: 39558242

Abstract

BACKGROUND: Esophageal fistula (EF), a rare and potentially fatal complication, can be better managed with predictive models for personalized treatment plans in esophageal cancers. We aim to develop a clinical-deep learning radiomics model for effectively predicting the occurrence of EF.

Authors

  • Yuxin Zhang
    State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology , Sichuan University , Chengdu 610041 , People's Republic of China.
  • Xu Cheng
    Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University, Beijing  100084, China.
  • Xianli Luo
    Department of Radiology, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
  • Ruixia Sun
    Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, P.R. China.
  • Xiang Huang
    Novozymes North America, Inc., Durham, North Carolina, United States of America.
  • Lingling Liu
    The Department of Radiology, The General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
  • Min Zhu
    Department of Infectious Diseases, Affiliated Taizhou Hospital of Wenzhou Medical University, No.50 Ximeng Road, Taizhou, 317000, China.
  • Xueling Li
    School of Biomedical Engineering, Anhui Medical University, Hefei, 230032, China. xlli@cmpt.ac.cn.

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