Pancreatic Cancer Detection by Patch-Based Computed Tomography Radiomics
碩士 === 國立臺灣大學 === 資料科學學位學程 === 107 === Pancreatic cancer (PC) is the most lethal cancer and the fourth leading cause of cancer deaths in the United States. Radiomics is a methodology that extracts quantitative statistics and features from medical images to decode the phenotype of tissues. The purpos...
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| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/6wc632 |
| Summary: | 碩士 === 國立臺灣大學 === 資料科學學位學程 === 107 === Pancreatic cancer (PC) is the most lethal cancer and the fourth leading cause of cancer deaths in the United States. Radiomics is a methodology that extracts quantitative statistics and features from medical images to decode the phenotype of tissues. The purpose of this study is to develop a machine learning model to differentiate PC from healthy pancreas on contrast-enhanced computed tomography (CT) using radiomic features and then investigate the important features. With a region in interest (ROI), we sample several overlapping patches. A total of 91 radiomic features were extracted of each patch and subject to a machine learning model to perform classification. We select 11 important features at last. Our model can accurately detect PC by using these 11 important features and is a potential computer-aided diagnosis tool.
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