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2020, Vol. 29(6): 441-445 DOI: 10.3760/cma.j.cn113030-20191112-00472 |
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Prediction of deep learning-based radiomic features for neoadjuvant radiochemotherapy response in locally advanced rectal cancer |
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Li Ning1, Sharon Qi2, Feng Lingling3, Tang Yuan1, Li Yexiong1, Ren Ye1, Fang Hui1, Tang Yu1, Chen Bo1, Lu Ningning1, Jing Hao1, Qi Shunan1, Wang Shulian1, Liu Yueping1, Song Yongwen1, Jin Jing1 |
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1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China;
2Department of Radiation Oncology, University of California Los Angeles Medical Center, Los Angeles 90095, USA;
3Department of Radiation Oncology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China |
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Received 2019-11-12 Revised null |
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Supporting info |
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