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Abstract #0566

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning with Consideration of Peri-Tumor Tissue

Jiejie Zhou1, Yang Zhang2, Kai-Ting Chang3, Kyoung Eun Lee4, Ouchen Wang1, Jiance Li1, Yezhi Lin5, Zhifang Pan5, Peter Chang3, Daniel Chow3, Meihao Wang1, and Min-Ying Su3
1First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 2University of California, Irvine, CA, United States, 3University of California, Irvine, Irvine, CA, United States, 4Inje University Seoul Paik Hospital, Seoul, Korea, Republic of, 5Wenzhou Medical University, Wenzhou, China

A total of 91 malignant/62 benign lesions were used for training, and 48 malignant/26 benign lesions for independent testing. Deep learning with ResNet50 were performed for differential diagnosis. To investigate the contribution of peri-tumor tissue, the tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. For per-lesion diagnosis, The accuracy was 91% for smallest bounding box, 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the highest accuracy was 89% for the smallest bounding box.

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