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Abstract #2321

Comparison of Breast Cancer Diagnostic Performance Using Radiomics Models Built Based on Features Extracted from DCE-MRI and Mammography

Jiejie Zhou1, Yang Zhang2, Kyoung Eun Lee3, Jeon-Hor Chen2, Xiaxia He1, Nina Xu1, Shuxin Ye1, Ouchen Wang1, Jiance Li1, Yezhi Lin4, Meihao Wang1, and Min-Ying Su2
1First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 2University of California, Irvine, CA, United States, 3Inje University Seoul Paik Hospital, Seoul, Korea, Republic of, 4Wenzhou Medical University, Wenzhou, China

A total of 89 patients receiving both DCE-MRI and mammography were analyzed, including 56 malignant and 33 benign lesions. The 3D tumor mask on MRI was generated using computer algorithms. A total of 99 texture and histogram features were extracted from three DCE parameters maps. The suspicious area on mammography was outlined using MRI findings as guidance, and a similar radiomics method was applied to extract features from the mass and the margin. Random forest was applied to select features for building diagnostic models. The overall accuracy was 0.80 for MRI, 0.75 for mammography, and improved to 0.85 when combined.

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