| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 100 === The liver cancer causing the death of over one million people in the world each year. Is the common seen malignant tumor. According to the statistics of the Department of health, the “liver cancer” has been reelected to the first or second place of the top ten leading causes of cancer death in Taiwan in the past 20 years. Such a high mortality rate is mainly due to the high recurrence rate of liver cancer. Reduction of the probability of cancer recurrence can significantly reduce the chance of death of patients after primary treatment. The goal of this research is to identify those patients who more like to be subject to recurrence. Different treatments or appropriate additional cares may be then applied to the patients to reduce their probability of recurrence, and extended patients survival rate.
Most of the assessment of liver cancer prognosis depend on the results of biopsy, such as the size, shape, degree of tumor differentiation, with or without the capsule, as well as invasion of the peripheral vascular tissue, and other postoperative indicators. The recent years, many medical imaging tools, with better resolutions faster speed and better resolution of tumor characteristics have been introduced, the rule of image detection has been changed from a simple diagnostic tool, to a prediction tool that can reflect tumor biological characteristics and predict the treatment effect. These tumor images are non-invasive, lower costs, and become the first line of the health examination diagnostic tools. In particular, if we can infer the prognosis outlook of the patient by the image characteristics it will be important information for the surgeon to perform the operations.
In this study, in cooperation with the National Taiwan University Hospital, we collect images of liver cancer patients with Power Doppler ultrasonic and dynamic imaging computed tomography to analyze tumor characteristics. Blood flow and different levels of brightness change will be quantified to reflect the biological characteristics of the tumor. The quantitative values will better used to indicators of patient prognosis factors.
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