Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Issue 3 (March 2016)

• Record Type: Journal Article
• Authors: Daginawala, Naznin; Li, Baojun; Buch, Karen; Yu, HeiShun; Tischler, Brian; Qureshi, Muhammad Mustafa; Soto, Jorge A.; Anderson, Stephan
• Is Part Of: European journal of radiology. Volume 85:Issue 3(2016)
• Page Start: 511
• Page End: 517
• Publication Date: 2016-03
• Subjects: Image post processing -- Texture analysis -- Liver -- Cirrhosis -- Computed tomography; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2015.12.009
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: To determine the ability of texture analyses of contrast-enhanced CT images for distinguishing between varying degrees of hepatic fibrosis in patients with chronic liver disease using histopathology as the reference standard. Materials and methods: Following IRB approval, 83 patients who underwent contrast enhanced 64-MDCT of the abdomen and pelvis in the portal venous phase between 12/2005 and 01/2013 and who had a liver biopsy within 6 months of the CT were included. An in-house developed, MATLAB-based texture analysis program was employed to extract 41 texture features from each of 5 axial segmented volumes of liver. Using the Ishak fibrosis staging scale, histopathologic grades of hepatic fibrosis were correlated with texture parameters after stratifying patients into three analysis groups, comparing Ishak scales 0–2 with 3–6, 0–3 with 4–6, and 0–4 with 5–6. To assess the utility of texture features, receiver operating characteristic (ROC) curves were constructed and the area under the curve (AUC) was used to determine the performance of each feature in distinguishing between normal/low and higher grades of hepatic fibrosis. Results: A total of 19 different texture features with 7 histogram features, one grey level co-occurrence matrix, 6 gray level run length, 1 Laws feature, and 4 gray level gradient matrix demonstrated statistically significant differences for discriminating between fibrosis groupings. The highest AUC values fell in the range of fair performance for distinguishing between different fibrosis groupings. Conclusion: These findings suggest that texture-based analyses of contrast-enhanced CT images offer a potential avenue toward the non-invasive assessment of liver fibrosis.