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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