Multimodality radiomics for tumor prognosis in nasopharyngeal carcinoma

Abstract

Background: The prognosis of nasopharyngeal carcinoma (NPC) is challenging due to late-stage identification and frequently undetectable Epstein-Barr virus (EBV) DNA. Incorporating radiomic features, which quantify tumor characteristics from imaging, may enhance prognosis assessment.

Purpose: To investigate the predictive power of radiomic features on overall survival (OS), progression-free survival (PFS), and distant metastasis-free survival (DMFS) in NPC.

Materials and methods: A retrospective analysis of 183 NPC patients treated with chemoradiotherapy from 2010 to 2019 was conducted. All patients were followed for at least three years. The pretreatment CT images with contrast medium, MR images (T1W and T2W), as well as gross tumor volume (GTV) contours, were used to extract radiomic features using PyRadiomics v.2.0. Robust and efficient radiomic features were chosen using the intraclass correlation test and univariate Cox proportional hazard regression analysis. They were then combined with clinical data including age, gender, tumor stage, and EBV DNA level for prognostic evaluation using Cox proportional hazard regression models with recursive feature elimination (RFE) and were optimized using 20 repetitions of a five-fold cross-validation scheme.

Results: Integrating radiomics with clinical data significantly enhanced the predictive power, yielding a C-index of 0.788 ± 0.066 to 0.848 ± 0.079 for the combined model versus 0.745 ± 0.082 to 0.766 ± 0.083 for clinical data alone (p<0.05). Multimodality radiomics combined with clinical data offered the highest performance. Despite the absence of EBV DNA, radiomics integration significantly improved survival predictions (C-index ranging from 0.770 ± 0.070 to 0.831 ± 0.083 in combined model versus 0.727 ± 0.084 to 0.734 ± 0.088 in clinical model, p<0.05).

Conclusions: The combination of multimodality radiomic features from CT and MR images could offer superior predictive performance for OS, PFS, and DMFS compared to relying on conventional clinical data alone.

Fig 1. Boxplots of C-indices.

(A) overall survival (OS), (B) progression-free survival (PFS), (C) distant metastasis-free survival (DMFS) in training set and (D) OS, (E) PFS, (F) DMFS in test set of the clinical, combination of clinical and CT-based radiomic, and combination of clinical and CT- and MRI-based radiomics models.

Fig 2. Line plots of C-indices of models with and without Epstein-Barr Virus (EBV) value.

(A) overall survival (OS), (B) progression-free survival (PFS), and (C) distant metastasis-free survival (DMFS).

Fig 3. Scatter plots representing the correlation between the risk scores of the combined model.

(A) Overall Survival (OS), (B) Progression-Free Survival (PFS), and (C) Distant Metastasis-Free Survival (DMFS) either with or without Epstein Barr Virus (EBV). The strong correlation in each plot underscores the similar predictive performance of the two models across all three survival outcomes.