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.
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.
Authors: James A d'Arcy; David J Collins; Anwar R Padhani; Simon Walker-Samuel; John Suckling; Martin O Leach Journal: Radiographics Date: 2006 Mar-Apr Impact factor: 5.333
Authors: G J Parker; J Suckling; S F Tanner; A R Padhani; P B Revell; J E Husband; M O Leach Journal: J Magn Reson Imaging Date: 1997 May-Jun Impact factor: 4.813
Authors: Sonia P Li; N Jane Taylor; Andreas Makris; Mei-Lin W Ah-See; Mark J Beresford; J James Stirling; James A d'Arcy; David J Collins; Anwar R Padhani Journal: Radiology Date: 2010-09-21 Impact factor: 11.105
Authors: Susan M Galbraith; Martin A Lodge; N Jane Taylor; Gordon J S Rustin; Søren Bentzen; J James Stirling; Anwar R Padhani Journal: NMR Biomed Date: 2002-04 Impact factor: 4.044
Authors: Keith N Ogston; Iain D Miller; Simon Payne; Andrew W Hutcheon; Tarun K Sarkar; Ian Smith; A Schofield; Steven D Heys Journal: Breast Date: 2003-10 Impact factor: 4.380
Authors: Rong Zhou; Stephen Pickup; Thomas E Yankeelov; Charles S Springer; Jerry D Glickson Journal: Magn Reson Med Date: 2004-08 Impact factor: 4.668
Authors: Qihao Zhang; Pascal Spincemaille; Michele Drotman; Christine Chen; Sarah Eskreis-Winkler; Weiyuan Huang; Liangdong Zhou; John Morgan; Thanh D Nguyen; Martin R Prince; Yi Wang Journal: Magn Reson Imaging Date: 2021-11-06 Impact factor: 2.546
Authors: Matthew Mouawad; Heather Biernaski; Muriel Brackstone; Michael Lock; Anat Kornecki; Olga Shmuilovich; Ilanit Ben-Nachum; Frank S Prato; R Terry Thompson; Stewart Gaede; Neil Gelman Journal: J Digit Imaging Date: 2020-08-03 Impact factor: 4.056
Authors: M F Fiordelisi; L Auletta; L Meomartino; L Basso; G Fatone; M Salvatore; M Mancini; A Greco Journal: Contrast Media Mol Imaging Date: 2019-09-22 Impact factor: 3.161
Authors: R Elena Ochoa-Albiztegui; Varadan Sevilimedu; Joao V Horvat; Sunitha B Thakur; Thomas H Helbich; Siegfried Trattnig; Elizabeth A Morris; Jeffrey S Reiner; Katja Pinker Journal: Cancers (Basel) Date: 2020-12-14 Impact factor: 6.639