Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis
Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and non...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Canadian Journal of Gastroenterology and Hepatology |
| Online Access: | http://dx.doi.org/10.1155/2021/6677821 |
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| author | Ru Zhao Xi-Jun Gong Ya-Qiong Ge Hong Zhao Long-Sheng Wang Hong-Zhen Yu Bin Liu |
| author_facet | Ru Zhao Xi-Jun Gong Ya-Qiong Ge Hong Zhao Long-Sheng Wang Hong-Zhen Yu Bin Liu |
| author_sort | Ru Zhao |
| collection | DOAJ |
| description | Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance. |
| format | Article |
| id | doaj-art-3ade9e49d7284d80943640a36a0f74cd |
| institution | Kabale University |
| issn | 2291-2789 2291-2797 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
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| series | Canadian Journal of Gastroenterology and Hepatology |
| spelling | doaj-art-3ade9e49d7284d80943640a36a0f74cd2025-02-03T06:06:50ZengWileyCanadian Journal of Gastroenterology and Hepatology2291-27892291-27972021-01-01202110.1155/2021/66778216677821Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver FibrosisRu Zhao0Xi-Jun Gong1Ya-Qiong Ge2Hong Zhao3Long-Sheng Wang4Hong-Zhen Yu5Bin Liu6Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei 230022, Anhui, ChinaDepartment of Radiology, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, Anhui, ChinaGE Healthcare China, Pudong New Town, No. 1, Huatuo Road, Shanghai 210000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, Anhui, ChinaDepartment of Radiology, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, Anhui, ChinaDepartment of Pathology, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei 230601, Anhui, ChinaDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei 230022, Anhui, ChinaPurpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.http://dx.doi.org/10.1155/2021/6677821 |
| spellingShingle | Ru Zhao Xi-Jun Gong Ya-Qiong Ge Hong Zhao Long-Sheng Wang Hong-Zhen Yu Bin Liu Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis Canadian Journal of Gastroenterology and Hepatology |
| title | Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis |
| title_full | Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis |
| title_fullStr | Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis |
| title_full_unstemmed | Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis |
| title_short | Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis |
| title_sort | use of texture analysis on noncontrast mri in classification of early stage of liver fibrosis |
| url | http://dx.doi.org/10.1155/2021/6677821 |
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