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

Synopsis

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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