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  • [Anticancer Res.] Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks.

    KIRAMS / 차유진, 장원일*

  • 출처
    Anticancer Res.
  • 등재일
    2018 Sep
  • 저널이슈번호
    38(9):5437-5445. doi: 10.21873/anticanres.12875.
  • 내용

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    Abstract
    BACKGROUND:
    It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether a convolutional neural network (CNN)-based radiomics model which learned computer tomography (CT) image features with minimal preprocessing, could predict early response of BM to radiosurgery.

    MATERIALS AND METHODS:
    Tumor images of 110 BM post stereotactic-radiosurgery (SRS) (within 3 months) were assessed (Response Evaluation Criteria in Solid Tumor, version 1.1) as responders (complete or partial response) or non-responders (stable or progressive disease). Datasets were axial planning CT images containing the tumor center, and the tumor response. Datasets were randomly assigned to training, validation, or evaluation groups repeatedly, to create 50 dataset combinations that were classified into five groups of 10 different dataset combinations with the same evaluation datasets. The CNN learned using training-group images and labels. Validation datasets were used to choose the model that best classified evaluation images as responders or non-responders.

    RESULTS:
    Of 110 tumors, 57 were classified as responders, and 53 as non-responders. The area under the receiver operating characteristic curve (AUC) of each CNN model for 50 dataset combinations ranged from 0.602 [95% confidence interval (CI)=36.5-83.9%] to 0.826 [95% CI, 64.3-100%]. The AUC of ensemble models, which averaged prediction results of 10 individual models within the same group, ranged from 0.761 (95% CI=55.2-97.1%) to 0.856 (95% CI=68.2-100%).

    CONCLUSION:
    A CNN-based ensemble radiomics model accurately predicted SRS responses of unlearned BM images. Thus, CNN models are able to predict SRS prognoses from small datasets.

     


    Author information

    Cha YJ1,2, Jang WI3, Kim MS1, Yoo HJ1, Paik EK1, Jeong HK1, Youn SM4.
    1
    Department of Radiation Oncology, Korea Institute of Radiological & Medical Sciences, Seoul, Republic of Korea.
    2
    Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
    3
    Department of Radiation Oncology, Korea Institute of Radiological & Medical Sciences, Seoul, Republic of Korea zzang11@kirams.re.kr.
    4
    Department of Neurosurgery, Korea Institute of Radiological & Medical Sciences, Seoul, Republic of Korea.

  • 키워드
    Brain metastases; convolutional neural networks; machine learning; radiomics; radiosurgery
  • 편집위원

    CNN-based radiomics model을 사용해서 영상의학적 특성으로 SRS에 대한 반응을 예측하는 본 연구는, 인공지능을 활용함으로써 최신 연구동향을 반영하며 앞으로 여러 질환에서 활용도가 높다는 점에서 주목할 만한 연구로 생각됩니다.

    2018-10-22 18:44:13

  • 편집위원2

    본 연구는 최근 각광받고 있는 convolution neural network (CNN)을 사용하여 CT 영상에 대한 분석/머신런닝을 통하여 전이 뇌암에 대한 방사선수술 결과를 예측하는 연구로서 연구 주제와 내용이 매우 시의 적절하다고 생각합니다.

    2018-10-22 18:50:05

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