DCE-MRI Breast Tumor Biomarker Analysis Using Convolutional Neural Network

• Main Authors: Hung-Yi Hsu, 徐宏毅
• Other Authors: Ruey-Feng Chang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/8769wf

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 105 === Breast cancer is the women leading cause of the death. In the recent years, doctors will develop different treatment plan according to molecular biomarkers of breast cancer. There are several biomarkers which were used in treatment strategy and prognosis estimation, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Breast cancer expresses negative in ER, PR, and HER2 is known as triple negative breast cancer (TNBC). TNBC had bad efficacy in medicine therapy. In this paper, the combination of handcraft feature and feature maps extracted by Convolutional Neural Network (CNN) is used to predict the statuses of ER, PR, HER2, and TNBC on breast dynamic contrast-enhance magnetic resonance imaging (DCE-MRI). We developed a different model derived from general CNN model to predict biomarkers of tumors. This model not only used image data as input but jointed handcraft features into CNN model, named as the combined model. The combined model which used both image data and handcraft features as input providing diversity of features in CNN model training. Therefore, we got better accuracy on molecular biomarker prediction. Generally, the combined model had the best performance in accuracy for HER2 classification. In the experiment of ER classification, 102 biopsy-proved tumors with 59 ER positive tumors and 43 ER negative tumors were used to evaluate the performance of the combined models. Its accuracy, sensitivity, specificity, and Az value are up to 74.5% (76/102), 79.7% (47/59), 67.4% (29/43), and 0.7382. For PR classification, there are 102 biopsy-proved tumors with 38 PR positive tumors and 64 PR negative tumors. Its accuracy, sensitivity, specificity, and Az value are up to 72.5% (74/102), 47.3% (18/38), 87.5% (56/64), and 0.6472. For HER2 classification, there are 102 biopsy-proved tumors with 38 HER2 positive tumors and 64 HER2 negative tumors. Its accuracy, sensitivity, specificity, and Az value are up to 84.3% (86/102), 85.1% (40/47), 83.6% (46/55), and 0.8492. For TNBC classification, there are 102 biopsy-proved tumors with 22 triple negative tumors and 80 non-triple negative tumors. Its accuracy, sensitivity, specificity, and Az value are up to 78.4% (80/102), 45.5% (10/22), 87.5% (70/80), and 0.7098. In summary, our combined model, which combined CNN and handcrafts features, improved the performance of identifying molecular biomarkers in breast cancer. Keywords: DCE-MRI, breast, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Triple negative, CNN, handcraft feature