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Abstract #0596

Differential Diagnosis of Benign and Malignant Breast Lesions Based on DCE-MRI by Using Radiomics and Deep Learning with Five Different Networks

Jiejie Zhou1, Yang Zhang2, Kai-Ting Chang2, Peter Chang2, Daniel Chow2, Ouchen Wang3, Meihao Wang1, and Min-Ying Lydia Su2

1Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 2University of California, Irvine, CA, United States, 3Department of Thyroid and Breast Surgery, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China

A total of 152 patients receiving breast MRI for diagnosis were analyzed, including 93 patients with 103 malignant cancers, and 59 patients with 73 benign lesions. Three DCE parametric maps corresponding to early wash-in, maximum, and wash-out were generated. Radiomics analysis based on texture and intensity histogram, and deep learning using 5 networks, were performed for differential diagnosis. The accuracy of radiomics was 0.80, and the accuracy of deep learning varied in the range of 0.79-0.94 depending on the network. The smallest bounding box containing the tumor with small amount of per-tumor tissue has the highest diagnostic accuracy.

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