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Abstract #3659

Combine nnU-Net and radiomics for automated classification of breast lesion using mp-MRI

Jing Zhang1, Chenao Zhan2, Xu Yan3, Yang Song3, Yihao Guo4, Tao Ai2, and Guang Yang1
1East China Normal University, Shanghai key lab of magnetic resonance, shanghai, China, 2Tongji Medical College, Huazhong University of Science and Technology, Department of Radiology, Tongji Hospital, Wuhan, Hubei Province, China, 3Siemens Healthcare, MR Scientific Marketing, shanghai, China, 4Siemens Healthcare, MR Collaboration, Guangzhou, China


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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