Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.

Journal: Radiation oncology (London, England)
PMID: 38956690

Abstract

BACKGROUND AND PURPOSE: Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings.

Authors

  • Jianhao Geng
    Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Xin Sui
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
  • Rongxu Du
    Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Jialin Feng
    Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Ruoxi Wang
    School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China. Electronic address: wangrx1910@buaa.edu.cn.
  • Meijiao Wang
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing Cancer Hospital & Institute Beijing, Beijing, China.
  • Kaining Yao
    Department of Radiation Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Qi Chen
    Department of Gastroenterology, Jining First People's Hospital, Jining, China.
  • Lu Bai
    College of Chemical Engineering, Department of Pharmaceutical Engineering, Northwest University, Taibai North Road 229, Xi'an 710069, Shaanxi, China.
  • Shaobin Wang
    MedMind Technology Co., Ltd., Beijing 100080, China.
  • Yongheng Li
    Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Xiangmin Hu
    Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China. hu.xiangmin@bit.edu.cn.
  • Yi Du
    Computer Network Information Center, Chinese Academy of Sciences, Beijing, China.

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