Preoperative Prediction of Axillary Lymph Node Metastasis Status in Breast Ultrasound Using Deep Convolution Neural Network

• Main Authors: Chung-Chih Shih, 施忠池
• Other Authors: Ruey-Feng Chang
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/6r2eh9

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 107 === Breast cancer is the second leading cause of cancer death in women. Despite the improvement in cancer therapy, metastatic breast cancer still leads to a high mortality rate. Accurate prediction and early detection of breast cancer metastasis status are important for the prognosis of breast cancer patients. Axillary lymph nodes (ALN) status is an important indicator in assessing cancer staging and deciding the treatment strategy for patients with breast cancer. Consequently, we developed a novel computer-aided prediction (CAP) system based on convolutional neural networks (CNN) using breast ultrasound to distinguish the ALN status. At first, the Mask R-CNN model was used to detect the tumor location and segment the tumor region from the whole US image. After obtained the tumor region, we extracted the peritumoral region and used the DenseNet model to predict the ALN status. In the experiments, 153 patients with breast tumor composed of 59 cases with ALN metastasis and 94 cases without ALN metastasis, used to evaluate our proposed method. According to results, the best prediction performance was using tumor region with the peritumoral region (3mm), the accuracy, sensitivity, specificity, and AUC are 81.05% (124/153), 81.36% (48/59) and 80.85% (76/94), and 0.8054. In summary, the proposed CAP model combines the primary tumor and peritumoral region, which can be an effective method to prediction the ALN status in patients with breast cancer.