방사선종양학

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  • 2024년 10월호
    [Int J Radiat Oncol Biol Phys .] Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases유방 방사선치료 계획시 딥러닝 기반 자동 컨투어링 활용

    연세의대 / 이병민, 이익재*, 장지석*

  • 출처
    Int J Radiat Oncol Biol Phys .
  • 등재일
    2024 Aug 1
  • 저널이슈번호
    119(5):1579-1589. doi: 10.1016/j.ijrobp.2024.02.041.
  • 내용

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    Abstract
    Purpose: This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments.

    Methods and materials: The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95).

    Results: We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection.

    Conclusions: In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.

     

     

    Affiliations

    Byung Min Lee 1, Jin Sung Kim 2, Yongjin Chang 3, Seo Hee Choi 2, Jong Won Park 2, Hwa Kyung Byun 2, Yong Bae Kim 2, Ik Jae Lee 4, Jee Suk Chang 5
    1Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea.
    2Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
    3Coreline Soft, Seoul, Republic of Korea.
    4Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address: ikjae412@yuhs.ac.
    5Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address: changjeesuk@yuhs.ac.

  • 편집위원

    이 연구는 유방암의 보조적방사선치료 2천례 이상의 많은 case를 대상으로 딥러닝 기반 automatic contouring의 성능을 평가한 것입니다. 방사선치료 시 contouring 작업은 상당한 시간과 노력이 필요한 작업이기 때문에 contouring 과정이 자동으로 수행된다면 임상의 입장에서 효율성과 함께 편의성이 증대될 것은 확실합니다. 다만, auto-contouring의 정확도에 대한 보증이 반드시 전제되어야 한다는 점에서 본 연구에서 수행한 결과가 임상 적용의 가능성을 평가하는데 중요한 의미가 있다고 생각됩니다.

    덧글달기2024-10-04 15:38:21

  • 편집위원2

    Deep learning을 이용하여 이전의 환자 case에서 방사선 치료 계획은 예측할 수 있는 패턴을 찾아내는 연구를 보고함.

    덧글달기2024-10-04 16:56:14

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