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  • [Radiat Oncol.] Atlas-based Auto-Segmentation for Postoperative Radiotherapy Planning in Endometrial and Cervical Cancers

    [Radiat Oncol.] Atlas-based Auto-Segmentation for Postoperative Radiotherapy Planning in Endometrial and Cervical Cancers

    연세의대 / 김나리, 장지석,김용배, 김진성*

  • 출처
    Radiat Oncol.
  • 등재일
    2020 May 13
  • 저널이슈번호
    15(1):106. doi: 10.1186/s13014-020-01562-y.
  • 내용

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    Abstract
    Background: Since intensity-modulated radiation therapy (IMRT) has become popular for the treatment of gynecologic cancers, the contouring process has become more critical. This study evaluated the feasibility of atlas-based auto-segmentation (ABAS) for contouring in patients with endometrial and cervical cancers.

    Methods: A total of 75 sets of planning CT images from 75 patients were collected. Contours for the pelvic nodal clinical target volume (CTV), femur, and bladder were carefully generated by two skilled radiation oncologists. Of 75 patients, 60 were randomly registered in three different atlas libraries for ABAS in groups of 20, 40, or 60. ABAS was conducted in 15 patients, followed by manual correction (ABASc). The time required to generate all contours was recorded, and the accuracy of segmentation was assessed using Dice's coefficient (DC) and the Hausdorff distance (HD) and compared to those of manually delineated contours.

    Results: For ABAS-CTV, the best results were achieved with groups of 60 patients (DC, 0.79; HD, 19.7 mm) and the worst results with groups of 20 patients (DC, 0.75; p = 0.012; HD, 21.3 mm; p = 0.002). ABASc-CTV performed better than ABAS-CTV in terms of both HD and DC (ABASc [n = 60]; DC, 0.84; HD, 15.6 mm; all p < 0.017). ABAS required an average of 45.1 s, whereas ABASc required 191.1 s; both methods required less time than the manual methods (p < 0.001). Both ABAS-Femur and simultaneous ABAS-Bilateral-femurs showed satisfactory performance, regardless of the atlas library used (DC > 0.9 and HD ≤10.0 mm), with significant time reduction compared to that needed for manual delineation (p < 0.001). However, ABAS-Bladder did not prove to be feasible, with inferior results regardless of library size (DC < 0.6 and HD > 40 mm). Furthermore, ABASc-Bladder required a longer processing time than manual contouring to achieve the same accuracy.

    Conclusions: ABAS could help physicians to delineate the CTV and organs-at-risk (e.g., femurs) in IMRT planning considering its consistency, efficacy, and accuracy.

     

     

     

    Affiliations

    Nalee Kim  1   2 , Jee Suk Chang  1 , Yong Bae Kim  1 , Jin Sung Kim  3
    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, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.
    3 Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. JINSUNG@yuhs.ac.

  • 키워드
    Auto segmentation; Computer-assisted radiotherapy planning; Gynecologic cancer; Intensity-modulated radiation therapy; Radiotherapy.
  • 연구소개
    부인암 환자의 방사선 치료에서 보편적으로 사용되는 세기 조절 방사선 치료에서 중요한 요소 중 하나인 구획화의 자동화 기법을 연구한 논문입니다. Atlas library를 생성하여, 새로운 환자에 대입하여 치료 용적과 정상 장기의 자동 구획화를 살펴보았고, 약간의 수정을 더할 경우, 사람 단독으로 구획화할 때 보다 짧은 시간 내에 비슷한 결과를 낼 수 있음을 알 수 있었습니다. 본 연구는 인공지능을 활용한 연구를 본격적으로 수행하기 전에 기본적으로 수행해본 연구로, 병원에서 사용하고 있는 현재의 Atlas based segmentation의 장단점에 대해 분석해 본 결과입니다. 따라서, 방사선 치료의 자동구획화에 관심있는 연구자들에게 소개 및 도움이 될 만한 좋은 정보라 생각합니다.
  • 편집위원

    Auto Segmentation 여전히 핫 이슈이며 임상에서의 필요성으로 인하여 많은 관심을 불러일으킵니다.

    2020-07-02 14:44:32

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