A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma

Abstract

Purpose: This paper aimed to establish and verify 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 was collected from January 2015 to December 2018. All NPC patients were randomly divided into two cohorts: training (n=202) and validation (n=86). Radiomics features from the MRI images of NPC patients were extracted and selected before IC. The patients were classified into high- and low-risk groups according to the median of 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. A survival analysis was performed using Kaplan-Meier with log-rank test and then each model's stratification ability was evaluated.

Results: Epstein-Barr virus (EBV) DNA before treatment was an independent predictor for PFS (p < 0.05). Based on the pyradiomic platform, we extracted 1,316 texture parameters in total. Finally, 16 texture features were used to build the model. The clinical radiomics-based model had good prediction capability for PFS, with a C-index of 0.827. The survival curve revealed that the PFS of the high-risk group was poorer than that of the low-risk group.

Conclusion: This research presents a nomogram that merges the radiomics signature and the clinical feature of the plasma EBV DNA load, which may improve the ability of preoperative prediction of progression-free survival and facilitate individualization of treatment in NPC patients before IC.

Keywords: induction chemotherapy; magnetic resonance imaging; nasopharyngeal carcinoma; radiomics; survival models.

Figure 1

(A) A radiomic nomogram integrating the radiomic signature and EBV DNA. (B) The calibration curves in the training cohort. (C) The calibration curves in the validation cohort.

Figure 2

Stratified analyses were performed to estimate progression-free survival in the training cohort and the validation cohort; (A) K-M curves of Radscore in train cohort. (B) K-M curves of Radscore in test cohort. High-risk patients show a lower progression-free survival rate than low-risk patients, p <0.05.