期刊
CLINICAL CANCER RESEARCH
卷 25, 期 14, 页码 4271-4279出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-18-3065
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类别
资金
- National Natural Science Foundation of China [81230056, 81402516, 81771924, 81501616, 81227901, 81572658]
- National Science and Technology Pillar Program [2014BAI09B10]
- Natural Science Foundation of Guangdong Province [2017A030312003]
- Health and Medical Collaborative Innovation Project of Guangzhou City, China [201400000001]
- Program of Introducing Talents of Discipline to Universities [B14035]
- Innovation Team Development Plan of the Ministry of Education [IRT_17R110]
- Beijing Natural Science Foundation [L182061]
- National Key R&D Program of China [2017YFA0205200, 2017YFC1308700, 2017YFC1309100]
- Youth Innovation Promotion Association CAS [2017175]
Purpose: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC). Experimental Design: We constructed radiomics signatures and nomogram for predicting disease-free survival (DFS) based on the extracted features from PET and CT images in a training set (n = 470), and then validated it on a test set (n = 237). Harrell's concordance indices (C-index) and time-independent receiver operating characteristic (ROC) analysis were applied to evaluate the discriminatory ability of radiomics nomogram, and compare radiomics signatures with plasma Epstein-Barr virus (EBV) DNA. Results: A total of 18 features were selected to construct CT-based and PET-based signatures, which were significantly associated with DFS (P < 0.001). Using these sig-natures, we proposed a radiomics nomogram with a C-index of 0.754 [95% confidence interval (95% CI), 0.709-0.800] in the training set and 0.722 (95% CI, 0.652-0.792) in the test set. Consequently, 206 (29.1%) patients were stratified as high-risk group and the other 501 (70.9%) as low-risk group by the radiomics nomogram, and the corresponding 5-year DFS rates were 50.1% and 87.6%, respectively (P < 0.0001). High-risk patients could benefit from IC while the low-risk could not. Moreover, radiomics nomogram performed significantly better than the EBV DNA-based model (C-index: 0.754 vs. 0.675 in the training set and 0.722 vs. 0.671 in the test set) in risk stratification and guiding IC. Conclusions: Deep learning PET/CT-based radiomics could serve as a reliable and powerful tool for prognosis prediction and may act as a potential indicator for individual IC in advanced NPC.
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