Toward Developing Pharmacokinetic Response Criteria to Chemoradiation in Head and Neck Cancer Patients Using Dynamic Contrast-Enhanced MRI.

摘要

Purpose: The purpose of this study was to assess the feasibility of using dynamic contrast-enhanced MRI to monitor early treatment-induced changes in pharmacokinetic (PK) parameters in head and neck cancer patients. The intrinsic variability of three parameters (Ktrans, v e, and iAUC60) without treatment intervention was measured and compared to the treatment-induced variability.;Materials and Methods: Twenty patients were imaged while undergoing chemoradiation therapy (CRT) for head and neck malignancies. The imaging protocol included two baseline scans one week apart (B1, B2), and a third scan after 1-2 weeks of chemoradiation (ETX - early treatment). The images were acquired on a 1.5T scanner in the coronal plane with a temporal resolution of 10 sec. A population-averaged arterial input function was calculated from plasma concentration curves in both the left and the right carotids of each patient at each time point (B1, B2, ETX). The statistical significance of using a left/right AIF or a time-point-specific AIF was evaluated using Bland-Altman plots. To further ensure the correct calculation of PK parameters, the accuracy of the flip angles produced by the MR scanner was measured in phantoms and a volunteer. PK analysis was performed in iCAD (Nashua, NH) based on the modified Tofts model. This study focuses on two PK parameters used in the Tofts model (Ktrans, ve), and one semi-quantitative parameter that was also calculated in iCAD using an uptake integral approach (iAUC60). Ktrans, ve, and iAUC60 were averaged over regions of interest (ROIs), some of which covered primary tumors, and others of which covered known nodal metastases. Bland-Altman plots were used to describe the intrinsic variability in each parameter between the two baseline scans. The coefficient of repeatability (CR) between the baseline values was determined from the Bland-Altman plots and compared to the magnitude of the observed treatment-induced changes.;Results: The plasma parameters for the population-averaged AIF were a1 = 27.1135 kg/liter, a2 = 17.6486 kg/liter, m1 = 11.7525 min-1, and m2 = 0.2054 min -1. The use of a left- or right-sided AIF was determined to be unnecessary, as it did not give statistically different PK parameters than the population-averaged AIF. The use of a time-point-specific AIF was not necessary in most cases, though it may give more accurate results when Ktrans values are > 1 min-1. The flip angle tests revealed high inaccuracies at a flip angle of 5°, so flip angles ≤ 5° were not used in PK analysis. The intrinsic variability of Ktrans, ve, and iAUC60 was very high. For nodes, the CRs from the B1-B2 Bland-Altman plots were 0.725 min-1 for Ktrans, 0.315 for ve, and 18.15 mM-sec for iAUC60. The fractions of node ROIs which showed treatment-induced changes greater than the CR were 3 out of 14 for Ktrans, 3 out of 17 for ve, and 7 out of 17 for iAUC60. For primaries, the CRs were 1.385 min-1 for K trans, 0.305 for ve, and 62.85 mM-sec for iAUC60. The fractions of primary ROIs which showed treatment-induced changes greater than the CR were 0 out of 9 for Ktrans, 1 out of 11 for ve , and 2 out of 12 for iAUC60.;Conclusions: A population-averaged AIF for head and neck was generated that accounts for differences in right vs. left carotids, day-to-day AIF fluctuations, and treatment-induced AIF changes. It is not necessary to use a side-specific or a time-point-specific AIF. When Ktrans is greater than 1 min-1, PK parameter accuracy may be improved with the use of a time-point-specific AIF. Using the average AIF, large intrinsic fluctuations were observed in ROI-averaged values of Ktrans, ve, and iAUC60, making these parameters poor evaluators of early treatment response in head and neck cancer. Nodes were slightly more likely than primaries to show significant treatment-induced changes. Overall, the use of averaged MR-based PK parameters to assess early treatment response is limited and challenging. An analysis of voxel-based variability might be better suited to this task.