Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

Journal: PloS one
PMID: 39083552

Abstract

PURPOSE: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer.

Authors

  • Sangwoon Jeong
    Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
  • Wonjoong Cheon
    Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea.
  • Sungjin Kim
    Department of Radiation Oncology, Samsung Medical Center, Seoul, Korea.
  • Won Park
    Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Youngyih Han
    Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul 06351, Republic of Korea. Electronic address: youngyih@skku.edu.

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