Multi-parametric MRI in the early prediction of response to neo-adjuvant chemotherapy in breast cancer: Value of non-modelled parameters. Issue 4 (April 2016)

• Record Type: Journal Article
• Authors: O'Flynn, Elizabeth AM; Collins, David; D'Arcy, James; Schmidt, Maria; de Souza, Nandita M
• Is Part Of: European journal of radiology. Volume 85:Issue 4(2016)
• Page Start: 837
• Page End: 842
• Publication Date: 2016-04
• Subjects: Enhancement fraction -- Predicting pCR -- Multi-parametric MRI; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2016.02.006
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: A decrease in tumour volume of 71% after 2 cycles of NAC can predict pCR with a sensitivity of 74% and specificity of 77% (AUC = 0.77). A decrease in the enhancement fraction is the best metric for predicting pCR after 2 cycles of neoadjuvant chemotherapy. Non-contrast enhanced-derived parameters such as the ADC and R2* were not useful in determining pCR after 2 cycles of NAC in breast cancer. The enhancement fraction can be used as a surrogate semi-quantitative imaging biomarker to predict pCR to NAC at an early time point. Use of a multi-parametric MRI model to assess early response to breast cancer emphasises the importance of the change in vascular parameters. 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.