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Abstract #0246

MRI-based deep learning radiomics can predict HER2 expression and disease-free survival in breast cancer

Wenjie Tang1, Yuan Guo1, Siyi Chen1, Bingsheng Huang2, Xiaotong Xie2, Mingyu Wang2, Yongzhou Xu3, Kuiming Jiang4, and Xinhua Wei1
1Guangzhou First People's Hospital, Guangzhou, China, 2Shenzhen University, Shenzhen, China, 3Philips Healthcare, Guangzhou, China, 4Guangdong Women and Children Hospital, Guangzhou, China

Synopsis

Keywords: Radiomics, Breast, HER2 expressing; MRI; Deep learning; PrognosisTo the best of our knowledge, our study is the first to non-invasively assess human epidermal growth factor receptor 2 (HER2) status, especially HER2-low-positive status in breast cancer. In this study, a deep learning radiomics (DLR) model based on contrast-enhanced MRI was constructed and showed high and stable performance in predicting HER2 status in both the training and validation cohorts, and the predicted status was an independently significant predictor of disease-free survival (DFS) in HER2-low-positive/HER2-zero breast cancers. The DLR model showed prospects as a computer-aided diagnostic tool to help more accurately identify HER2-low-positive breast cancers, thereby guiding patient treatment strategies.

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