Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T
Joint Authors
Peterson, Michael R.
Yokoo, Takeshi
Wolfson, Tanya
Iwaisako, Keiko
Goodman, Zachary
Changchien, Christopher
Middleton, Michael S.
Gamst, Anthony C.
Mazhar, Sameer M.
Kono, Yuko
Ho, Samuel B.
Sirlin, Claude B.
Mani, Haresh
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yokoo, Takeshi& Wolfson, Tanya& Iwaisako, Keiko& Peterson, Michael R.& Mani, Haresh& Goodman, Zachary…[et al.]. 2015. Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T. BioMed Research International،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1055283
Modern Language Association (MLA)
Yokoo, Takeshi…[et al.]. Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T. BioMed Research International No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1055283
American Medical Association (AMA)
Yokoo, Takeshi& Wolfson, Tanya& Iwaisako, Keiko& Peterson, Michael R.& Mani, Haresh& Goodman, Zachary…[et al.]. Evaluation of Liver Fibrosis Using Texture Analysis on Combined-Contrast-Enhanced Magnetic Resonance Images at 3.0T. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1055283
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055283