Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.

Journal: Radiation oncology (London, England)
PMID: 33632248

Abstract

BACKGROUND: In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians' workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients.

Authors

  • Seung Yeun Chung
    Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea.
  • Jee Suk Chang
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea. Electronic address: changjeesuk@yuhs.ac.
  • Min Seo Choi
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
  • Yongjin Chang
    CorelineSoft, Co, Ltd, Seoul, Korea.
  • Byong Su Choi
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
  • Jaehee Chun
    Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Ki Chang Keum
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
  • Jin Sung Kim
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.
  • Yong Bae Kim
    Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals