Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure

SIMPLE SUMMARY: Pre-treatment (TX) prediction of the risk of locoregional failure (LRF) will allow for TX individualization in patients with nasopharyngeal carcinoma (NPC). The aim of the present study was to identify whether the quantitative metrics from pre-TX non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced can predict patients with LRF in NPC. Cumulative incidence (CI) analysis and Fine-Gray (FG) proportional subhazards modeling were conducted in a sample of 29 NPC patients considering death as a competing risk. NGIVIM and FXR derived quantitative metric values from primary tumors classified the patients with and without LRF in NPC. The CI analysis and FG modeling results suggest that the quantitative metrics obtained from DW- and DCE-MRI may improve LRF patients’ prediction in NPC. ABSTRACT: The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (D, D*, f, and K) models. The metrics K(trans), v(e), and τ(i) were estimated using the FXR model. Cumulative incidence (CI) analysis and Fine-Gray (FG) modeling were performed considering death as a competing risk. Mean v(e) values were significantly different between patients with and without LRF (p = 0.03). Mean f values showed a trend towards the difference between the groups (p = 0.08). Histograms exhibited inter primary tumor heterogeneity. The CI curves showed significant differences for the dichotomized cutoff value of ADC ≤ 0.68 × 10(−3) (mm(2)/s), D ≤ 0.74 × 10(−3) (mm(2)/s), and f ≤ 0.18 (p < 0.05). τ(i) ≤ 0.89 (s) cutoff value showed borderline significance (p = 0.098). FG’s modeling showed a significant difference for the K cutoff value of ≤0.86 (p = 0.034). Results suggest that the role of pre-TX NGIVIM DW- and FXR DCE-MRI-derived metrics for predicting LRF in NPC than alone.