Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers

The purpose of this study was to identify the optimal tracer kinetic model from dynamic contrast-enhanced T(1)‐weighted magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model (2CXM), compartmental tissue uptake model (CTUM), extended Tofts model (ETM), and standard Tofts model (TM) were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way Analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUC’s were compared. ETM performed the best with the lowest AICc and highest weights (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of K(trans) in PTCs with aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78±0.29 vs. 0.34±0.18 min(−1), P=0.005). From ROC analysis, cut-off values of K(trans), v(e), and v(p) which discriminated between PTCs with and without ETE were determined at 0.45 min(−1), 0.28, and 0.014, respectively. The sensitivities and specificities were 86% and 82% (K(trans)), 71% and 82% (v(e)), and 86% and 55% (v(p)). Their respective AUC’s were 0.90, 0.71, and 0.71. We conclude the ETM K(trans) has shown potential to classify tumors with and without aggressive ETE in patients with PTC.