Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T
Purpose. To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis. Materials and Methods. In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent...
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doaj-60bd2b8e34c14a4d8659a3578cd971082020-11-24T23:41:34ZengHindawi LimitedBioMed Research International2314-61332314-61412015-01-01201510.1155/2015/387653387653Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0TTakeshi Yokoo0Tanya Wolfson1Keiko Iwaisako2Michael R. Peterson3Haresh Mani4Zachary Goodman5Christopher Changchien6Michael S. Middleton7Anthony C. Gamst8Sameer M. Mazhar9Yuko Kono10Samuel B. Ho11Claude B. Sirlin12Departments of Radiology, University of California, San Diego, CA 92103, USAComputational and Applied Statistics Laboratory, San Diego Supercomputer Center, University of California, San Diego, CA 92093, USADepartments of Radiology, University of California, San Diego, CA 92103, USADepartments of Pathology, University of California, San Diego, CA 92103, USADepartment of Pathology, Penn State Hershey Medical Center, Hershey, PA 17033, USACenter for Liver Diseases, Inova Fairfax Hospital, Falls Church, VA 22042, USADepartments of Radiology, University of California, San Diego, CA 92103, USADepartments of Radiology, University of California, San Diego, CA 92103, USAComputational and Applied Statistics Laboratory, San Diego Supercomputer Center, University of California, San Diego, CA 92093, USADepartments of Medicine, University of California, San Diego, CA 92103, USADepartments of Radiology, University of California, San Diego, CA 92103, USADepartments of Medicine, University of California, San Diego, CA 92103, USADepartments of Radiology, University of California, San Diego, CA 92103, USAPurpose. To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis. Materials and Methods. In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent CCE liver MRI following intravenous administration of superparamagnetic iron oxides (ferumoxides) and gadolinium DTPA (gadopentetate dimeglumine). The image texture of the liver was quantified in regions-of-interest by calculating 165 texture features. Liver biopsy specimens were stained with Masson trichrome and assessed qualitatively (METAVIR fibrosis score) and quantitatively (% collagen stained area). Using L1 regularization path algorithm, two texture-based multivariate linear models were constructed, one for quantitative and the other for quantitative histology prediction. The prediction performance of each model was assessed using receiver operating characteristics (ROC) and correlation analyses. Results. The texture-based predicted fibrosis score significantly correlated with qualitative (r=0.698, P<0.001) and quantitative (r=0.757, P<0.001) histology. The prediction model for qualitative histology had 0.814–0.976 areas under the curve (AUC), 0.659–1.000 sensitivity, 0.778–0.930 specificity, and 0.674–0.935 accuracy, depending on the binary classification threshold. The prediction model for quantitative histology had 0.742–0.950 AUC, 0.688–1.000 sensitivity, 0.679–0.857 specificity, and 0.696–0.848 accuracy, depending on the binary classification threshold. Conclusion. CCE MRI and texture analysis may permit noninvasive assessment of liver fibrosis.http://dx.doi.org/10.1155/2015/387653 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Takeshi Yokoo Tanya Wolfson Keiko Iwaisako Michael R. Peterson Haresh Mani Zachary Goodman Christopher Changchien Michael S. Middleton Anthony C. Gamst Sameer M. Mazhar Yuko Kono Samuel B. Ho Claude B. Sirlin |
spellingShingle |
Takeshi Yokoo Tanya Wolfson Keiko Iwaisako Michael R. Peterson Haresh Mani Zachary Goodman Christopher Changchien Michael S. Middleton Anthony C. Gamst Sameer M. Mazhar Yuko Kono Samuel B. Ho Claude B. Sirlin Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T BioMed Research International |
author_facet |
Takeshi Yokoo Tanya Wolfson Keiko Iwaisako Michael R. Peterson Haresh Mani Zachary Goodman Christopher Changchien Michael S. Middleton Anthony C. Gamst Sameer M. Mazhar Yuko Kono Samuel B. Ho Claude B. Sirlin |
author_sort |
Takeshi Yokoo |
title |
Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T |
title_short |
Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T |
title_full |
Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T |
title_fullStr |
Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T |
title_full_unstemmed |
Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T |
title_sort |
evaluation of liver fibrosis using texture analysis on combined-contrast-enhanced magnetic resonance images at 3.0t |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2015-01-01 |
description |
Purpose. To noninvasively assess liver fibrosis using combined-contrast-enhanced (CCE) magnetic resonance imaging (MRI) and texture analysis. Materials and Methods. In this IRB-approved, HIPAA-compliant prospective study, 46 adults with newly diagnosed HCV infection and recent liver biopsy underwent CCE liver MRI following intravenous administration of superparamagnetic iron oxides (ferumoxides) and gadolinium DTPA (gadopentetate dimeglumine). The image texture of the liver was quantified in regions-of-interest by calculating 165 texture features. Liver biopsy specimens were stained with Masson trichrome and assessed qualitatively (METAVIR fibrosis score) and quantitatively (% collagen stained area). Using L1 regularization path algorithm, two texture-based multivariate linear models were constructed, one for quantitative and the other for quantitative histology prediction. The prediction performance of each model was assessed using receiver operating characteristics (ROC) and correlation analyses. Results. The texture-based predicted fibrosis score significantly correlated with qualitative (r=0.698, P<0.001) and quantitative (r=0.757, P<0.001) histology. The prediction model for qualitative histology had 0.814–0.976 areas under the curve (AUC), 0.659–1.000 sensitivity, 0.778–0.930 specificity, and 0.674–0.935 accuracy, depending on the binary classification threshold. The prediction model for quantitative histology had 0.742–0.950 AUC, 0.688–1.000 sensitivity, 0.679–0.857 specificity, and 0.696–0.848 accuracy, depending on the binary classification threshold. Conclusion. CCE MRI and texture analysis may permit noninvasive assessment of liver fibrosis. |
url |
http://dx.doi.org/10.1155/2015/387653 |
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