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Combing MRI Perfusion and F-FDG PET/CT Metabolic Biomarkers Helps Predict Survival in Advanced Nasopharyngeal Carcinoma: A Prospective Multimodal Imaging Study

Overview
Journal Cancers (Basel)
Publisher MDPI
Specialty Oncology
Date 2021 Apr 3
PMID 33800542
Citations 9
Authors
Affiliations
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Abstract

We prospectively investigated the prognostic value of imaging parameters for nasopharyngeal carcinoma (NPC) using dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), and 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography (F-FDG PET)/computed tomography (CT). Patients with stage III-IVb NPC underwent F-FDG PET/CT, DCE-MRI, and DWI before treatment. Kaplan-Meier and Cox-regression analyses were used to assess associations of PET and MRI imaging biomarkers with overall survival (OS) and recurrence-free survival (RFS). We used independent prognosticators to establish prognostic models; model performance was examined using Harrell's concordance index (c-index). Sixty-one patients were available for analysis, as 13 patients died and 20 experienced recurrence. Total lesion glycolysis (TLG) ( = 0.002) from PET/CT and the initial area under the curve (iAUC) ( = 0.036) from DCE-MRI were identified as independent prognosticators of OS; Epstein-Barr virus (EBV) DNA ( = 0.027), the extracellular volume fraction () ( = 0.027) from DCE-MRI, and TLG/iAUC ( = 0.025) were significant predictors of RFS. The c-indices of the prognostic models incorporating TLG + iAUC in predicting OS and incorporating EBV DNA + + TLG/iAUC in predicting RFS were 0.79 and 0.76, respectively. These were significantly higher than the corresponding c-indices of the TNM staging system ( = 0.047 and 0.025, respectively); they were also higher than those of models with only MRI or PET biomarkers. In conclusion, the combination of pretreatment DCE-MRI and F-FDG PET/CT imaging biomarkers helps survival prediction in advanced NPC. Integrating MRI perfusion with PET metabolism and plasma EBV information may aid clinicians in planning the optimal personalized management strategy.

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