Elizabeth A M O'Flynn1, David Collins2, James D'Arcy3, Maria Schmidt4, Nandita M de Souza5. 1. Clinical Magnetic Resonance Group, Cancer Research UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, United Kingdom. Electronic address: Elizabeth.OFlynn@icr.ac.uk. 2. Clinical Magnetic Resonance Group, Cancer Research UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, United Kingdom. Electronic address: david.collins@icr.ac.uk. 3. Clinical Magnetic Resonance Group, Cancer Research UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, United Kingdom. Electronic address: james.darcy@icr.ac.uk. 4. Clinical Magnetic Resonance Group, Cancer Research UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, United Kingdom. Electronic address: maria.schmidt@rmh.nhs.uk. 5. Clinical Magnetic Resonance Group, Cancer Research UK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, United Kingdom. Electronic address: Nandita.desouza@icr.ac.uk.
Abstract
OBJECTIVE: To prospectively evaluate individual functional MRI metrics for the early prediction of pathological complete response (pCR) to neo-adjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND METHODS: Thirty-two women (median age 52 years; range 32-71 years) with biopsy proven breast cancer due to receive neo-adjuvant anthracycline and/or taxane-based chemotherapy were prospectively recruited following local research ethics committee approval and written informed consent. Breast MRI was performed prior to and after two cycles of NAC and pCR was assessed after surgery. The enhancement fraction (EF), tumour volume, initial area under the gadolinium curve (IAUGC), pharmacokinetic parameters (K(trans), kep and ve), the apparent diffusion coefficient (ADC) and R2* values, along with the percentage change in these parameters after two cycles were evaluated according to pCR status using an independent samples t-test. The area under the receiver operating characteristics curve (AUC) was calculated for each parameter. Linear discriminant analysis (LDA) determined the most important parameter in predicting pCR. RESULTS: A reduction in the EF (-41% ± 38%) and tumour volume (-80% ± 25%) after 2 cycles of NAC were significantly greater in those achieving pCR (p=0.025, p=0.011 respectively). A reduction in the EF of 7% after 2 cycles of NAC identified those more likely to achieve pCR (AUC 0.76). AUC changes in other parameters were tumour volume (0.77), IAUGC (0.64), K(trans) (0.60), kep (0.68), ve (0.58), ADC (0.69) and R2* (0.41). CONCLUSION: In a multi-parametric MRI model, the decrease in a non-model based vascular parameter the enhancement fraction as well as the tumour volume are the most important early predictors of pCR in breast cancer.
OBJECTIVE: To prospectively evaluate individual functional MRI metrics for the early prediction of pathological complete response (pCR) to neo-adjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND METHODS: Thirty-two women (median age 52 years; range 32-71 years) with biopsy proven breast cancer due to receive neo-adjuvant anthracycline and/or taxane-based chemotherapy were prospectively recruited following local research ethics committee approval and written informed consent. Breast MRI was performed prior to and after two cycles of NAC and pCR was assessed after surgery. The enhancement fraction (EF), tumour volume, initial area under the gadolinium curve (IAUGC), pharmacokinetic parameters (K(trans), kep and ve), the apparent diffusion coefficient (ADC) and R2* values, along with the percentage change in these parameters after two cycles were evaluated according to pCR status using an independent samples t-test. The area under the receiver operating characteristics curve (AUC) was calculated for each parameter. Linear discriminant analysis (LDA) determined the most important parameter in predicting pCR. RESULTS: A reduction in the EF (-41% ± 38%) and tumour volume (-80% ± 25%) after 2 cycles of NAC were significantly greater in those achieving pCR (p=0.025, p=0.011 respectively). A reduction in the EF of 7% after 2 cycles of NAC identified those more likely to achieve pCR (AUC 0.76). AUC changes in other parameters were tumour volume (0.77), IAUGC (0.64), K(trans) (0.60), kep (0.68), ve (0.58), ADC (0.69) and R2* (0.41). CONCLUSION: In a multi-parametric MRI model, the decrease in a non-model based vascular parameter the enhancement fraction as well as the tumour volume are the most important early predictors of pCR in breast cancer.
Authors: Beatriz E Adrada; Rosalind Candelaria; Stacy Moulder; Alastair Thompson; Peng Wei; Gary J Whitman; Vicente Valero; Jennifer K Litton; Lumarie Santiago; Marion E Scoggins; Tanya W Moseley; Jason B White; Elizabeth E Ravenberg; Wei T Yang; Gaiane M Rauch Journal: Cancer Date: 2021-04-20 Impact factor: 6.921
Authors: Shelley Henderson; Colin Purdie; Caroline Michie; Andrew Evans; Richard Lerski; Marilyn Johnston; Sarah Vinnicombe; Alastair M Thompson Journal: Eur Radiol Date: 2017-05-18 Impact factor: 5.315
Authors: John Virostko; Allison Hainline; Hakmook Kang; Lori R Arlinghaus; Richard G Abramson; Stephanie L Barnes; Jeffrey D Blume; Sarah Avery; Debra Patt; Boone Goodgame; Thomas E Yankeelov; Anna G Sorace Journal: J Med Imaging (Bellingham) Date: 2017-11-24
Authors: Shelley A Henderson; Nazleen Muhammad Gowdh; Colin A Purdie; Lee B Jordan; Andrew Evans; Tracy Brunton; Alastair M Thompson; Sarah Vinnicombe Journal: Br J Radiol Date: 2018-05-02 Impact factor: 3.039
Authors: Ella F Jones; Deep K Hathi; Rita Freimanis; Rita A Mukhtar; A Jo Chien; Laura J Esserman; Laura J Van't Veer; Bonnie N Joe; Nola M Hylton Journal: Cancers (Basel) Date: 2020-06-09 Impact factor: 6.575