Abstract #3526

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

Yang Zhang¹, Yezhi Lin^1,2, Siwa Chan³, Jeon-Hor Chen^1,4, 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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