Multi-parametric MRI (mp-MRI) radiomics can distinguish breast mass effectively, but requires breast lesion segmentation first, which is subjective and laborious for radiologists. To overcome this problem, we combined nnUnet and radiomics analysis as an automatic model for breast lesion classification. In the test cohort, the breast lesion segmentation model achieved mean dice of 0.835, and the classification model achieved an AUC of 0.891. We found that the nnU-Net can delineatey lesions accurately based on dynamic contrast-enhanced (DCE, TWIST-VIBEs)), and mp-MRI radiomics features extracted from the auto-segmented lesions can be used to classfy breast lesions accurately.
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