Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study

Abstract

Background: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC.

Methods: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes.

Results: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity.

Conclusions: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Keywords: Nasopharyngeal necrosis; Radiomics; Re-radiotherapy; Recurrent nasopharyngeal carcinoma.

Fig. 1

Clinical characteristics of post-radiation nasopharyngeal necrosis. A Typical endoscopic and magnetic resonance imaging manifestations of post-radiation nasopharyngeal necrosis (PRNN), along with the associated symptoms and complications. B Incidences of PRNN following re-radiotherapy in 625 patients with locally recurrent nasopharyngeal carcinoma (LRNPC) recruited from the Sun Yat-sen University Cancer Centre. Most events occurred within 1 year of re-radiotherapy. C Kaplan–Meier curve showing that PRNN significantly impaired overall survival (OS). D PRNN and massive bleeding are leading causes of death for re-irradiated patients with LRNPC

Fig. 2

Study flowchart. A Multicentre cohorts of patients with locally recurrent nasopharyngeal carcinoma (LRNPC) who were treated with re-radiotherapy. B Radiomics modelling for the prediction of post-radiation nasopharyngeal necrosis (PRNN) based on pre-treatment head and neck magnetic resonance imaging (MRI). SYSUCC Sun Yat-sen University Cancer Centre, CHCAMS Cancer Hospital Chinese Academy of Medical Sciences, FUSCC Fudan University Shanghai Cancer Centre, NHSMU Nanfang Hospital of Southern Medical University, ROI region-of-interest

Fig. 3

Performance of the radiomic model in predicting post-radiation nasopharyngeal necrosis. A Visualization of the six radiomic features included in the signature. The changes in the individual radiomic features in patients with the highest and lowest probability of 1-year post-radiation nasopharyngeal necrosis (PRNN) are presented. B Output of the radiomic signature for each patient in the training cohort. C Receiver operating characteristic curves of the radiomic signature to predict 1-year PRNN in the training, internal validation, and external validation cohorts. D Comparison of the radiomic and clinical models to predict 1-year PRNN in the training cohort. E Calibration curve of the PRNN radiomic signature. The significance of calibration was analyzed using Hosmer–Lemeshow goodness-of-fit tests, with the P-value shown. F Clinical utility of the PRNN radiomic signature to guide re-radiotherapy based on decision curve analysis. With the threshold probability set at > 20%, using the radiomic signature to predict PRNN adds more benefit than either the treat-all-patients strategy or the treat-none strategy; the radiomic signature produces more net benefits than the clinical model. AUC area under the receiver operating characteristic curve

Fig. 4

Risk stratification of post-radiation nasopharyngeal necrosis using the radiomic signature. A–C Cumulative incidences of post-radiation nasopharyngeal necrosis (PRNN) in the training (A), internal validation (B), and external validation (C) cohorts. D–E Cumulative PRNN incidences in patients with small (D) and large (E) tumors. Hazard ratios (HRs) were estimated using univariate Cox regression analyses. GTV gross tumor volume, CI confidence interval

Fig. 5

Biological processes associated with the radiomic signature for predicting post-radiation nasopharyngeal necrosis. A Expression of the top 50 positively and negatively ranked genes for the radiomic signature based on 29 cases (7 high-risk vs 22 low-risk patients) recruited from the Sun Yat-sen University Cancer Centre. The genes were clustered based on the correlation distance metric. B Significantly enriched gene sets from the Gene ontology (GO) collection using ClusterProfiler of the ranked genes. C GO-based fibroblast associated processes enriched with the radiomics signature. D GO-based vascularity associated processes enriched with the radiomics signature. E Heatmap of the GO-based single-sample enrichment scores in individual samples. F Spearman rank correlation coefficient matrix of the GO-based single-sample enrichment scores and the individual radiomic features included in the signature. *Statistical significance (P < 0.05). G Visualization of association between the shape feature (Sphericity) extracted from pre-treatment magnetic resonance imaging (MRI) and tissue fibrosis. H Visualization of association between the texture feature (Run Entropy) extracted from pre-treatment MRI and tissue vascularity. NES normalized enrichment score, EC endothelial cell, VEGFR vascular endothelial growth factor receptor, FGFR fibroblast growth factor receptor