title: 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 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%.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  We demonstrate that using the most appropriate AIF can aid successful prediction of response to NAC in breast cancer.
authors.affiliation: 1 Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK.
authors.forename: David K
authors.initials: DK
authors.lastname: Woolf
authors.affiliation: 2 Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, UK.
authors.forename: N Jane
authors.initials: NJ
authors.lastname: Taylor
authors.affiliation: 1 Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK.
authors.forename: Andreas
authors.initials: A
authors.lastname: Makris
authors.affiliation: 3 CR UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, UK.
authors.forename: Nina
authors.initials: N
authors.lastname: Tunariu
authors.affiliation: 3 CR UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, UK.
authors.forename: David J
authors.initials: DJ
authors.lastname: Collins
authors.affiliation: 1 Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK.
authors.forename: Sonia P
authors.initials: SP
authors.lastname: Li
authors.affiliation: 1 Breast Cancer Research Unit, Mount Vernon Cancer Centre, Northwood, UK.
authors.forename: Mei-Lin
authors.initials: ML
authors.lastname: Ah-See
authors.affiliation: 4 Breast Unit, Royal United Hospital Bath, Bath, UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Beresford
authors.affiliation: 2 Paul Strickland Scanner Centre, Mount Vernon Hospital, Northwood, UK.
authors.forename: Anwar R
authors.initials: AR
authors.lastname: Padhani
mesh_terms: D000328:Adult; D001158:Arteries / Q000473:pathology*; D001940:Breast / Q000098:blood supply* / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy; D003287:Contrast Media; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D015233:Models, Statistical; D020360:Neoadjuvant Therapy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA