Arterial Input Functions in Dynamic Contrast-enhanced Magnetic Resonance Imaging: Which Model Performs Best when Assessing Breast Cancer Response?

Overview

Journal Br J Radiol

Publisher Oxford University Press

Specialty Radiology

Date 2016 May 18

PMID 27187599

Citations 8

Authors

David K Woolf

N Jane Taylor

Andreas Makris

Nina Tunariu

David J Collins

Sonia P Li

Mei-Lin Ah-See

Mark Beresford

Anwar R Padhani

Affiliations

Soon will be listed here.

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 contrast-enhanced 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 (-68%). Reproducibility (r) was the lowest for the Weinmann function (r = -49.7%), with other AIFs ranging from r = -27.8 to -39.2%.

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.

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