Electronic removal of lesions for more robust BPE scoring on breast DCE-MRI

During radiologists’ visual assessment of background parenchymal enhancement (BPE) on dynamic contrast enhanced (DCE)-MR images, presence of a tumor may erroneously inflate the BPE estimation due to angiogenesis within the tumor. With a dataset of 426 MRIs, we present an automated method to segment breasts, electronically remove the influence of lesion presence, and calculate scores to estimate BPE levels. A U-Net was trained for breast segmentation from maximum intensity projection (MIP) images. Next, fuzzy c-means (FCM) clustering was used to segment the lesions from the breast DCE-MRIs, and the lesion volume was removed to create MIP images without the influence of the lesion. U-Net outputs were applied to create MIP images of both breasts, affected breasts, and unaffected breasts before and after lesion removal. On an independent test set, a statistically significant trend was found between the radiologist BPE ratings and the calculated BPE scores for all breast regions (Kendall correlation, p < 0.001). Receiver operating characteristic (ROC) analysis was performed to determine the predictive value of the computed scores from each breast region in the binary tasks of classifying Minimal vs. Marked and Low vs. High BPE relative to a radiologist rating. Scores from all breast regions performed significantly better than guessing (p < 0.025 from the z-test) with BPE scores of the affected breast after lesion removal performing the best (AUC = 0.87). Results demonstrate the potential for an automatic BPE prediction from breast DCE-MR without the influence of lesion enhancement.