Article: Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images.
2024 Volume 16, Issue 6
Abstract: Liver malignancies, particularly hepatocellular carcinoma and metastasis, stand as prominent contributors to cancer mortality. Much of the data from abdominal computed tomography images remain underused by radiologists. This study explores the ... ...
Abstract | Liver malignancies, particularly hepatocellular carcinoma and metastasis, stand as prominent contributors to cancer mortality. Much of the data from abdominal computed tomography images remain underused by radiologists. This study explores the application of machine learning in differentiating tumor tissue from healthy liver tissue using radiomics features. Preoperative contrast-enhanced images of 94 patients were used. A total of 1686 features classified as first-order, second-order, higher-order, and shape statistics were extracted from the regions of interest of each patient's imaging data. Then, the variance threshold, the selection of statistically significant variables using the Student's |
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Language | English |
Publishing date | 2024-03-14 |
Publishing country | Switzerland |
Document type | Journal Article |
ZDB-ID | 2527080-1 |
ISSN | 2072-6694 |
ISSN | 2072-6694 |
DOI | 10.3390/cancers16061158 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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