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  • [Radiat Oncol] Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

    연세의대, 울산의대 / 변화경, 장지석*, 정진홍*

  • 출처
    Radiat Oncol
  • 등재일
    2021 Oct 14
  • 저널이슈번호
    16(1):203.
  • 내용

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    Abstract
    Purpose: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts.

    Methods: Eleven experts from two institutions delineated nine OARs in 10 cases of adjuvant radiotherapy after breast-conserving surgery. Autocontours were then provided to the experts for correction. Overall, 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR were analyzed. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to compare the degree of agreement between the best manual contour (chosen by an independent expert committee) and each autocontour, corrected autocontour, and manual contour. Higher DSCs and lower HDs indicated a better geometric overlap. The amount of time reduction using the autocontouring system was examined. User satisfaction was evaluated using a survey.

    Results: Manual contours, corrected autocontours, and autocontours had a similar accuracy in the average DSC value (0.88 vs. 0.90 vs. 0.90). The accuracy of autocontours ranked the second place, based on DSCs, and the first place, based on HDs among the manual contours. Interphysician variations among the experts were reduced in corrected autocontours, compared to variations in manual contours (DSC: 0.89-0.90 vs. 0.87-0.90; HD: 4.3-5.8 mm vs. 5.3-7.6 mm). Among the manual delineations, the breast contours had the largest variations, which improved most significantly with the autocontouring system. The total mean times for nine OARs were 37 min for manual contours and 6 min for corrected autocontours. The results of the survey revealed good user satisfaction.

    Conclusions: The autocontouring system had a similar performance in OARs as that of the experts' manual contouring. This system can be valuable in improving the quality of breast radiotherapy and reducing interphysician variability in clinical practice.

     


    Affiliations

    Hwa Kyung Byun  1 , Jee Suk Chang  2 , Min Seo Choi  1 , Jaehee Chun  1 , Jinhong Jung  3 , Chiyoung Jeong  4 , Jin Sung Kim  1 , Yongjin Chang  5 , Seung Yeun Chung  1   6 , Seungryul Lee  7 , Yong Bae Kim  1
    1 Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
    2 Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. changjeesuk@yuhs.ac.
    3 Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea. jung.jinhong@amc.seoul.kr.
    4 Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
    5 Coreline Soft, Co., Ltd, Seoul, South Korea.
    6 Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea.
    7 Yonsei University College of Medicine, Seoul, South Korea.

  • 키워드
    Autocontouring; Breast; Organs at risk; Radiotherapy.
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