Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma

作者：Hao Peng, Di Dong, Meng-Jie Fang, Lu Li, Ling‐Long Tang, Lei Chen, Wen-Fei Li, Yan‐Ping Mao, Wei Fan, Li-Zhi Liu, Li Tian, Ai-Hua Lin, Ying Sun, Jie Tian, Jun Ma · 发表于：Clinical Cancer Research · 年份：2019 · DOI：10.1158/1078-0432.ccr-18-3065 · 被引用次数：345 · 研究领域：Head and Neck Cancer Studies、Radiomics and Machine Learning in Medical Imaging、Cancer Genomics and Diagnostics

Abstract 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 signatures, 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...