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  • [Med. Phys] 딥러닝 기반 자동 방사선치료계획Deep learning enables automatic radiotherapy planning

    Fudan University / Weigang Hu*

  • 출처
    Med. Phys
  • 등재일
    2018 Nov 01
  • 저널이슈번호
    Volume46, Issue1 January 2019 P
  • 내용

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    Abstract
    Purpose
    To develop an automated treatment planning strategy for external beam intensity‐modulated radiation therapy (IMRT), including a deep learning‐based three‐dimensional (3D) dose prediction and a dose distribution‐based plan generation algorithm.
    Methods and Materials
    A residual neural network‐based deep learning model is trained to predict a dose distribution based on patient‐specific geometry and prescription dose. A total of 270 head‐and‐neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation.
    Results
    Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose–volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV70.4, but the difference is only 0.5% which is still clinically acceptable.
    Conclusion
    This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution‐based optimization. It is a promising approach for realizing automated treatment planning in the future.

     

    Author information

    Jiawei Fan, Jiazhou Wang, Zhi Chen, Chaosu Hu, Zhen Zhang, Weigang Hu

  • 편집위원

    방사선치료계획은 많은 단계와 그 복잡성과 다양성 때문에 자동치료계획시스템을 구성하기 까다로운 분야이기는 하지만, 그렇기 때문에 더욱 딥러닝기반으로 자동치료계획시스템을 만들고자하는 요구가 많은 분야이기도 하다. 기존의 방법을 자동화하는 것이 아닌, 딥러닝기반으로 최종산물을 바로 얻는 기법이 참신하였고, 임상 검증이 충분히 이루어지길 바란다.

    2019-02-22 17:33:52

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