The present study determines the feasibility of generating an average arterial input function (Avg-AIF) from a limited population of patients with neck nodal metastases to be used for pharmacokinetic modeling of dynamic contrast-enhanced MRI (DCE-MRI) data in clinical trials of larger populations.
Twenty patients (mean age 50 years [range 27–77 years]) with neck nodal metastases underwent pretreatment DCE-MRI studies with a temporal resolution of 3.75 to 7.5 sec on a 1.5T clinical MRI scanner. Eleven individual AIFs (Ind-AIFs) met the criteria of expected enhancement pattern and were used to generate Avg-AIF. Tofts model was used to calculate pharmacokinetic DCE-MRI parameters. Bland-Altman plots and paired Student t-tests were used to describe significant differences between the pharmacokinetic parameters obtained from individual and average AIFs.
Ind-AIFs obtained from eleven patients were used to calculate the Avg-AIF. No overall significant difference (bias) was observed for the transfer constant (Ktrans) measured with Ind-AIFs compared to Avg-AIF (p = 0.20 for region-of-interest (ROI) analysis and p = 0.18 for histogram median analysis). Similarly, no overall significant difference was observed for interstitial fluid space volume fraction (ve) measured with Ind-AIFs compared to Avg-AIF (p = 0.48 for ROI analysis and p = 0.93 for histogram median analysis). However, the Bland-Altman plot suggests that as Ktrans increases, the Ind-AIF estimates tend to become proportionally higher than the Avg-AIF estimates.
We found no statistically significant overall bias in Ktrans or ve estimates derived from Avg-AIF, generated from a limited population, as compared with Ind-AIFs.
However, further study is needed to determine whether calibration is needed across the range of Ktrans. The Avg-AIF obtained from a limited population may be used for pharmacokinetic modeling of DCE-MRI data in larger population studies with neck nodal metastases. Further validation of the Avg-AIF approach with a larger population and in multiple regions is desirable.