Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma.

Journal: International journal of radiation oncology, biology, physics
PMID: 35304306

Abstract

PURPOSE: Radiation dermatitis (RD) is a common, unpleasant side effect of patients receiving radiation therapy. In clinical practice, the severity of RD is graded manually through visual inspection, which is labor intensive and often leads to large interrater variations. To overcome these shortcomings, this study aimed to develop an automatic RD assessment based on deep learning (DL) techniques that could efficiently assist the RD severity classification in clinical application.

Authors

  • Ruiyan Ni
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Ta Zhou
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Ge Ren
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
  • Yuanpeng Zhang
  • Dongrong Yang
    Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
  • Victor C W Tam
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Wan Shun Leung
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Hong Ge
    Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China.
  • Shara W Y Lee
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
  • Jing Cai
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.

Keywords

External Resources

Popular Topics
Recent Journals