Prediction of deep learning-based radiomic features for neoadjuvant radiochemotherapy response in locally advanced rectal cancer
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
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
Abstract：Objective To evaluate the effectiveness of deep learning (DL)-based radiomic features extracted from pre-treatment diffusion-weighted magnetic resonance images (DWI) for predicting neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC). Methods Forty-three patients receiving nCRT from 2016 to 2017 were included. All patients received DWI before nCRT and total mesorectal excision surgery 6-12 weeks after completion of nCRT. The patient-cohort was split into the responder group (n=22) and the non-responder group (n=21) based on the post-nCRT response assessed by postoperative pathology, MRI or colonoscopy. DL-based radiomic features were extracted from the apparent diffusion coefficient map of the DWI using a pre-trained convolution neural network, respectively. Least absolute shrinkage and selection operator-Logistic regression models were constructed using extracted radiomic features for predicting treatment response. The model performance was evaluated with repeated 20 times stratified 4-fold cross-validation using receiver operating characteristic (ROC) curves.Results The model established with DL-based radiomic features achieved the mean area under the ROC curve of 0.73(SE, 0.58-0.80). Conclusion DL-based radiomic features extracted from pre-treatment DWI achieve high accuracy for predicting nCRT response in patients with LARC.
Li Ning,Sharon Qi,Feng Lingling et al. Prediction of deep learning-based radiomic features for neoadjuvant radiochemotherapy response in locally advanced rectal cancer[J]. Chinese Journal of Radiation Oncology, 2020, 29(6): 441-445.
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