Texture-based classification of liver fibrosis using MRI
Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a r...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Published: |
John Wiley and Sons Inc.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/35735 |
| _version_ | 1848754576206856192 |
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| author | House, M. Bangma, S. Thomas, M. Gan, E. Ayonrinde, Oyekoya Adams, L. Olynyk, John St Pierre, T. |
| author_facet | House, M. Bangma, S. Thomas, M. Gan, E. Ayonrinde, Oyekoya Adams, L. Olynyk, John St Pierre, T. |
| author_sort | House, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages. |
| first_indexed | 2025-11-14T08:42:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-35735 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:42:36Z |
| publishDate | 2015 |
| publisher | John Wiley and Sons Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-357352017-09-13T15:31:38Z Texture-based classification of liver fibrosis using MRI House, M. Bangma, S. Thomas, M. Gan, E. Ayonrinde, Oyekoya Adams, L. Olynyk, John St Pierre, T. Purpose: To investigate the ability of texture analysis of MRI images to stage liver fibrosis. Current noninvasive approaches for detecting liver fibrosis have limitations and cannot yet routinely replace biopsy for diagnosing significant fibrosis. Materials and Methods: Forty-nine patients with a range of liver diseases and biopsy-confirmed fibrosis were enrolled in the study. For texture analysis all patients were scanned with a T2-weighted, high-resolution, spin echo sequence and Haralick texture features applied. The area under the receiver operating characteristics curve (AUROC) was used to assess the diagnostic performance of the texture analysis. Results: The best mean AUROC achieved for separating mild from severe fibrosis was 0.81. The inclusion of age, liver fat and liver R2 variables into the generalized linear model improved AUROC values for all comparisons, with the F0 versus F1-4 comparison the highest (0.91). Conclusion: Our results suggest that a combination of MRI measures, that include selected texture features from T2-weighted images, may be a useful tool for excluding fibrosis in patients with liver disease. However, texture analysis of MRI performs only modestly when applied to the classification of patients in the mild and intermediate fibrosis stages. 2015 Journal Article http://hdl.handle.net/20.500.11937/35735 10.1002/jmri.24536 John Wiley and Sons Inc. restricted |
| spellingShingle | House, M. Bangma, S. Thomas, M. Gan, E. Ayonrinde, Oyekoya Adams, L. Olynyk, John St Pierre, T. Texture-based classification of liver fibrosis using MRI |
| title | Texture-based classification of liver fibrosis using MRI |
| title_full | Texture-based classification of liver fibrosis using MRI |
| title_fullStr | Texture-based classification of liver fibrosis using MRI |
| title_full_unstemmed | Texture-based classification of liver fibrosis using MRI |
| title_short | Texture-based classification of liver fibrosis using MRI |
| title_sort | texture-based classification of liver fibrosis using mri |
| url | http://hdl.handle.net/20.500.11937/35735 |