Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study.

@article{Zhen2017DeepCN,
  title={Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study.},
  author={Xin Zhen and Jiawei Chen and Zichun Zhong and Brian A Hrycushko and Linghong Zhou and Steve Jiang and Kevin Albuquerque and Xuejun Gu},
  journal={Physics in medicine and biology},
  year={2017},
  volume={62 21},
  pages={8246-8263}
}
Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non… CONTINUE READING
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