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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997774/ https://www.ncbi.nlm.nih.gov/pubmed/33791254 http://dx.doi.org/10.1155/2021/6677821 |
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author | Zhao, Ru Gong, Xi-Jun Ge, Ya-Qiong Zhao, Hong Wang, Long-Sheng Yu, Hong-Zhen Liu, Bin |
author_facet | Zhao, Ru Gong, Xi-Jun Ge, Ya-Qiong Zhao, Hong Wang, Long-Sheng Yu, Hong-Zhen Liu, Bin |
author_sort | Zhao, Ru |
collection | PubMed |
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 | Online Article Text |
id | pubmed-7997774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-79977742021-03-30 Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis Zhao, Ru Gong, Xi-Jun Ge, Ya-Qiong Zhao, Hong Wang, Long-Sheng Yu, Hong-Zhen Liu, Bin Can J Gastroenterol Hepatol Research Article 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. Hindawi 2021-03-18 /pmc/articles/PMC7997774/ /pubmed/33791254 http://dx.doi.org/10.1155/2021/6677821 Text en Copyright © 2021 Ru Zhao et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhao, Ru Gong, Xi-Jun Ge, Ya-Qiong Zhao, Hong Wang, Long-Sheng Yu, Hong-Zhen Liu, Bin Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997774/ https://www.ncbi.nlm.nih.gov/pubmed/33791254 http://dx.doi.org/10.1155/2021/6677821 |
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