| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === In recent years, breast cancer has become the most common female cancer in the world, and it is also one of main common cause of death in modern times. Chemotherapy is one of the common methods of treatment breast cancer. Not only used before surgery to shrink the tumor (neo-adjuvant therapy) and post-operative adjuvant therapy, it is also used to control the cancer metastasis. Therefore, the early prediction of chemotherapy response would provide a great help in the treatment evaluation of breast cancer. On the other hand, recent studies have shown that tumors usually need more vessels than normal cells to provide sufficient nutrients for growth and survival. It means that the growth of vessels is high correlation with the growth of tumors. In addition, the vessel information has been widely used in the diagnosis of breast lesions and therapy response evaluation of chemotherapy. Hence, the main purpose of this study is to extract the vessel features from the 3-D MRI to evaluate the response of neo-adjuvant chemotherapy and analyze whether the vessel information can predict the final treatment response or not. There are three main parts in this study, including breast segmentation, vessel extraction and vessel feature extraction. First, the template-base breast segmentation algorithm is used to segment the breast region from the 3-D MRI. In order to avoid the skin being mistaken extracted as vessel, the breast skin would be excluded before vessel extraction. The line filter based on Hessian matrix is used to extract the vessels inside the segmented breast region in this study. After the vessel extraction, the vessels skeletons are obtained from the vessels by thinning algorithm and then transformed into tree structure in order to quantify the vessels. Total 17 vessel features including 11 morphological and 6 tortuous features are used in this study. The change ratio of vessel features between pre-chemotherapy and early stage of chemo treatment is utilized as indicators to evaluate the treatment response. That is, the vessel features is used to predict the final treatment response (i.e. the response of late stage of chemo treatment). In addition, diagnostic performance of vessel features method are compared with the change ratio of tumor size method and combined the change ratio of vessel features and tumor size method. This experiment evaluates total 31 breast cancer cases, including 14 positive response and 17 negative response cases. The accuracy, sensitivity and specificity for the vessel features in breast harboring tumor are 77.42% (24/31), 82.35% (14/17) and 71.43% (10/14), respectively. The accuracy, sensitivity and specificity for the tumor size method are 80.65% (25/31), 76.47% (13/17) and 85.71% (12/14), respectively. The combined vessel features and tumor size can provide the best diagnostic performance with accuracy, sensitivity and specificity improved to 83.87% (26/31), 88.24% (15/ 17), and 78.57% (11/14), respectively. The experimental results show that the tumor size method could provide a better specificity and the vessel features method can provide a better sensitivity. Furthermore, the combined method could provide the best diagnostic performance. It means that the vessel features could improve the diagnostic performance and might provide the capability of predicting the final chemotherapy response.
|
|---|
