%A Shen,Hesong %A Wang,Yu %A Liu,Daihong %A Lv,Rongfei %A Huang,Yuanying %A Peng,Chao %A Jiang,Shixi %A Wang,Ying %A He,Yongpeng %A Lan,Xiaosong %A Huang,Hong %A Sun,Jianqing %A Zhang,Jiuquan %D 2020 %J Frontiers in Oncology %C %F %G English %K Radiomics,prediction,Progression-free survival,nasopharyngeal carcinoma,Magnetic Resonance Imaging %Q %R 10.3389/fonc.2020.00618 %W %L %M %P %7 %8 2020-May-12 %9 Original Research %# %! nomogram with pre-treatment EBV DNA %* %< %T Predicting Progression-Free Survival Using MRI-Based Radiomics for Patients With Nonmetastatic Nasopharyngeal Carcinoma %U https://www.frontiersin.org/articles/10.3389/fonc.2020.00618 %V 10 %0 JOURNAL ARTICLE %@ 2234-943X %X Objectives: This study aimed to explore the predictive value of MRI-based radiomic model for progression-free survival (PFS) in nonmetastatic nasopharyngeal carcinoma (NPC).Methods: A total of 327 nonmetastatic NPC patients [training cohort (n = 230) and validation cohort (n = 97)] were enrolled. The clinical and MRI data were collected. The least absolute shrinkage selection operator (LASSO) and recursive feature elimination (RFE) were used to select radiomic features. Five models [Model 1: clinical data, Model 2: overall stage, Model 3: radiomics, Model 4: radiomics + overall stage, Model 5: radiomics + overall stage + Epstein–Barr virus (EBV) DNA] were constructed. The prognostic performances of these models were evaluated by Harrell's concordance index (C-index). The Kaplan–Meier method was applied for the survival analysis.Results: Model 5 incorporating radiomics, overall stage, and EBV DNA yielded the highest C-indices for predicting PFS in comparison with Model 1, Model 2, Model 3, and Model 4 (training cohorts: 0.805 vs. 0.766 vs. 0.749 vs. 0.641 vs. 0.563, validation cohorts: 0.874 vs. 0.839 vs. 836 vs. 0.689 vs. 0.456). The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group.Conclusions: The model incorporating radiomics, overall stage, and EBV DNA showed better performance for predicting PFS in nonmetastatic NPC patients.