AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions.

Journal: International journal of radiation oncology, biology, physics
Published Date: Dec 29, 2023

Abstract

PURPOSE: Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy.

Authors

  • Yanhua Duan
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Jiyong Wang
    Shanghai Pulse Medical Technology Inc, Shanghai, China.
  • Puyu Wu
    Verisk Information Technology Ltd, Shanghai, China.
  • Yan Shao
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Hua Chen
    Management College, Beijing Union University, Beijing, China.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Hongbin Cao
    Department of Radiation Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Hengle Gu
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Aihui Feng
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Ying Huang
    Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Southwest Medical University Luzhou, Sichuan, China.
  • Zhenjiong Shen
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yang Lin
    Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China.
  • Qing Kong
    Institute of Modern Physics, Fudan University, Shanghai, China.
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Hongxuan Li
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Xiaolong Fu
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Zhangru Yang
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: aries8703@163.com.
  • Xuwei Cai
    Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: birdhome2000@163.com.
  • Zhiyong Xu

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