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Abstract #3526

Differentiation of Breast Cancer Molecular Subtypes on DCE-MRI by Using Convolutional Neural Network with Transfer Learning

Yang Zhang1, Yezhi Lin1,2, Siwa Chan3, Jeon-Hor Chen1,4, Jiejie Zhou2, Daniel Chow1, Peter Chang1, Meihao Wang2, and Min-Ying Su1
1Department of Radiological Science, University of California, Irvine, CA, United States, 2Department of Radiology, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, 3Department of Medical Imaging, Taichung Tzu-Chi Hospital, Taichung, Taiwan, 4Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

A total of 244 patients were analyzed, 99 in Training, 83 in Testing-1 and 62 in Testing-2. Patients were classified into 3 molecular subtypes: TN, HER2+ and (HR+/HER2-). Deep learning using CNN and Convolutional Long Short Term Memory (CLSTM) were implemented. The mean accuracy in Training dataset evaluated using 10-fold cross-validation was higher using CLSTM (0.91) than CNN (0.79). When the developed model was applied to testing datasets, the accuracy was very low, 0.4-0.5. When transfer learning was applied to re-tune the model using one testing dataset, it could greatly improve accuracy in the other dataset from 0.4-0.5 to 0.8-0.9.

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