Arterial input functions in dynamic contrast-enhanced magnetic resonance imaging: which model performs best when assessing breast cancer response?

Abstract

Objective: To evaluate the performance of six models of population arterial input function (AIF) in the setting of primary breast cancer and neoadjuvant chemotherapy (NAC). The ability to fit patient dynamic contrastenhanced MRI (DCE-MRI) data, provide physiological plausible data and detect pathological response was assessed. Methods: Quantitative DCE-MRI parameters were calculated for 27 patients at baseline and after 2 cycles of NAC for 6 AIFs. Pathological complete response detection was compared with change in these parameters from a reproduction cohort of 12 patients using the Bland-Altman approach and receiver-operating characteristic analysis. Results: There were fewer fit failures pre-NAC for all models, with the modified Fritz-Hansen having the fewest pre-NAC (3.6%) and post-NAC (18.8%), contrasting with the femoral artery AIF (19.4% and 43.3%, respectively).Median transfer constant values were greatest for the Weinmann function and also showed greatest reductions with treatment (268%). Reproducibility (r) was the lowest for the Weinmann function (r 5 249.7%), with other AIFs ranging from r 5 227.8 to 239.2%. Conclusion: Using the best performing AIF is essential to maximize the utility of quantitative DCE-MRI parameters in predicting response to NAC treatment. Applying our criteria, the modified Fritz-Hansen and cosine bolus approximated Parker AIF models performed best. The Fritz-Hansen and biexponential approximated Parker AIFs performed less well, and the Weinmann and femoral artery AIFs are not recommended. Advances in knowledge: We demonstrate that using the most appropriate AIF can aid successful prediction of response to NAC in breast cancer.

Pharmacokinetic Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging at 7T for Breast Cancer Diagnosis and Characterization

“…We chose to use the Tofts model over other possibilities such as model-free parameters as the majority of previously published papers have utilized the Tofts model to allow comparison to previously published data and to enable reproduction of our findings by others using the same freeware OSIRIX software and the DCE-Tool plugin (Pixmeo SARL, Geneva, Switzerland). Quantitative DCE-MRI analysis was performed using PK modeling according to the Tofts model using the modified Fritz–Hansen arterial input functions (AIF) [ 33 ] in order to calculate the following quantitative kinetic parameters: forward volume transfer constant (k trans , min –1 ), reverse volume transfer constant (k ep , min –1 ), and extravascular extracellular space volume per unit volume of tissue in % (v e ) [ 6 , 32 ]. The modified Fritz–Hansen AIF was used as it maximizes the utility of quantitative DCE-MRI in breast tissue [ 33 ].…”

“…Quantitative DCE-MRI analysis was performed using PK modeling according to the Tofts model using the modified Fritz–Hansen arterial input functions (AIF) [ 33 ] in order to calculate the following quantitative kinetic parameters: forward volume transfer constant (k trans , min –1 ), reverse volume transfer constant (k ep , min –1 ), and extravascular extracellular space volume per unit volume of tissue in % (v e ) [ 6 , 32 ]. The modified Fritz–Hansen AIF was used as it maximizes the utility of quantitative DCE-MRI in breast tissue [ 33 ].…”

The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

“…One aspect of this problems is presented in the example shown in Figure 3 , where the use of image-driven methods based on multiple-flip angles produces a parametric map of a tumor with different contrast compared to the one produced with the Fritz–Hansen population based AIF [ 12 ]. This issue has several implications, including for the accuracy of assessing breast cancer response to neoadjuvant chemotherapy [ 13 ].…”

Preclinical Molecular Imaging for Precision Medicine in Breast Cancer Mouse Models

“…There are different ways of estimating AIF; for instance, direct sampling of AIF is possible with arterial blood sampling, but it is considered invasive for patients and very challenging in small rodents. Another way is AIF estimation based on MR images (image-derived AIF), which is noninvasive, but it is time consuming in postprocessing; it can only be performed by placing ROIs on large vessels, such as the aorta, and higher doses of contrast agents are required [56]. The AIF can also be calculated by evaluating the contrast agent concentration in reference tissues; for instance, by placing an ROI on thigh muscles, whose perfusion rate, extraction fraction, and extracellular volume are known [57].…”

“…The AIF can also be calculated by evaluating the contrast agent concentration in reference tissues; for instance, by placing an ROI on thigh muscles, whose perfusion rate, extraction fraction, and extracellular volume are known [57]. However, the most common method remains the population-based AIF drawn from scientific literature [56]. The Brix model estimates the kinetic parameters directly from relative signal intensity curves, which allows reducing errors, particularly in murine models, from image-derived AIF.…”