Radiomics for dynamic contrast-enhanced (DCE) breast MRI have shown promise in the diagnosis of breast cancer as
applied to conventional DCE-MRI protocols. Here, we investigate the potential of using such radiomic features in the
diagnosis of breast cancer applied on ultrafast breast MRI in which images are acquired every few seconds. The dataset
consisted of 64 lesions (33 malignant and 31 benign) imaged with both ‘conventional’ and ultrafast DCE-MRI. After
automated lesion segmentation in each image sequence, we calculated 38 radiomic features categorized as describing
size, shape, margin, enhancement-texture, kinetics, and enhancement variance kinetics. For each feature, we calculated
the 95% confidence interval of the area under the ROC curve (AUC) to determine whether the performance of each
feature in the task of distinguishing between malignant and benign lesions was better than random guessing.
Subsequently, we assessed performance of radiomic signatures in 10-fold cross-validation repeated 10 times using a
support vector machine with as input all the features as well as features by category. We found that many of the features
remained useful (AUC>0.5) for the ultrafast protocol, with the exception of some features, e.g., those designed for latephase
kinetics such as the washout rate. For ultrafast MRI, the radiomics enhancement-texture signature achieved the
best performance, which was comparable to that of the kinetics signature for ‘conventional’ DCE-MRI, both achieving
AUC values of 0.71. Radiomic developed for ‘conventional’ DCE-MRI shows promise for translation to the ultrafast
protocol, where enhancement texture appears to play a dominant role.