1.Prediction of deep learning-based radiomic features for neoadjuvant radiochemotherapy response in locally advanced rectal cancer
Ning LI ; Qi SHARON ; Lingling FENG ; Yuan TANG ; Yexiong LI ; Ye REN ; Hui FANG ; Yu TANG ; Bo CHEN ; Ningning LU ; Hao JING ; Shunan QI ; Shulian WANG ; Yueping LIU ; Yongwen SONG ; Jing JIN
Chinese Journal of Radiation Oncology 2020;29(6):441-445
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.
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