A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma - Liu, Lu; Pei, Wei; Liao, Hai; Wang, Qiang; Gu, Donglian; Liu, Lijuan; Su, Danke; Jin, Guanqiao (Frontiers in Oncology, 2022)
Purpose: To develop and validate a radiomics model based on magnetic resonance imaging(MRI) for predicting the progression-free survival of nasopharyngeal carcinoma(NPC) after induction chemotherapy(IC). Materials and Methods: This cohort consists of 288 patients with clinical-pathologically confirmed NPC, which collected from January 2015 to December 2018. All NPC patients were randomly divided into two cohorts: training(n=202) and validation(n=86). Extracting and selecting radiomics features from the MRI images of NPC patients before IC. The patients were classified into high- and low-risk groups according to the median of the Radscores. The significant imaging features and clinical variables in the univariate analysis were constructed for progression-free survival (PFS) using the multivariate Cox regression model. Kaplan-Meier survival analysis and log-rank test were performed for survival analysis and evaluating each model's stratification ability. Results: EBV DNA before treatment was an independent predictor for PFS (p < 0.05). Based on the pyraidiomic platform, we extracted 1,316 texture parameters in total. Finally 16 texture features were used to build model. The clinical-radiomics based model had good prediction capability for PFS, with C-index of 0.827. The survival curve showed that the high-risk group yielded a lower PFS than the low-risk group. Conclusion: This research presents a nomogram that merge the radiomics signature, clinical feature of plasma Epstein-Barr virus (EBV) DNA load , which may improve the ability of preoperative prediction of progression-free survival and facilitate individualization of treatment in patients with NPC before induction chemotherapy.