Ultrafast dynamic contrast-enhanced breast MRI: association with pathologic complete response in neoadjuvant treatment of breast cancer

Objectives: The purpose of this study was to investigate whether pretreatment kinetic features from ultrafast DCE-MRI are associated with pathological complete response (pCR) in patients with invasive breast cancer and according to immunohistochemistry (IHC) subtype. Methods: Between August 2018 and June 2019, 256 consecutive breast cancer patients (mean age, 50.2 years; range, 25–86 years) who underwent both ultrafast and conventional DCE-MRI and surgery following neoadjuvant chemotherapy were included. DCE-MRI kinetic features were obtained from pretreatment MRI data. Time-to-enhancement, maximal slope (MS), and volumes at U1 and U2 (U1, time point at which the lesion starts to enhance; U2, subsequent time point after U1) were derived from ultrafast MRI. Logistic regression analysis was performed to identify factors associated with pCR. Results: Overall, 41.4% of all patients achieved pCR. None of the kinetic features was associated with pCR when including all cancers. Among ultrafast DCE-MRI kinetic features, a lower MS (OR, 0.982; p = 0.040) was associated with pCR at univariable analysis in hormone receptor (HR)–positive cancers. In triple-negative cancers, a higher volume ratio U1/U2 was associated with pCR at univariable (OR, 11.787; p = 0.006) and multivariable analysis (OR, 14.811; p = 0.005). Among conventional DCE-MRI kinetic features, a lower peak enhancement (OR, 0.993; p = 0.031) and a lower percentage of washout (OR, 0.904; p = 0.039) was associated with pCR only in HR-positive cancers at univariable analysis. Conclusions: A higher volume ratio of U1/U2 derived from ultrafast DCE-MRI was independently associated with pCR in triple-negative invasive breast cancer. Key Points: • The ratio of tumor volumes obtained at the first (U1) and second time points (U2) of enhancement was independently associated with pCR in triple-negative invasive breast cancers. • Ultrafast MRI has the potential to improve accuracy in predicting treatment response and personalizing therapy.