Purpose

To determine the risk of nephrogenic systemic fibrosis (NSF) in a cohort of patients with chronic liver disease.

Materials and methods

This retrospective, Institutional Review Board (IRB)-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant study was performed at a single tertiary liver center. The study cohort comprised 1167 patients with chronic liver disease followed in a liver clinic and exposed to gadolinium-based contrast agents (GBCAs) between February 2004 and October 2007. A retrospective review of medical records was performed. For each patient, data were collected on demographics, history of GBCA exposure, presence of purported risk factors for NSF, and histopathological evidence of NSF.

Results

Of the 1167 patients with chronic liver disease, 58% (n = 678) had cirrhosis. The patients had a total of 2421 separate GBCA exposures. Fifty-five percent (n = 646) had a single exposure, 19% (n = 218) had two exposures, and 26% (n = 303) had three or more exposures. Seventy-two percent (n = 843) of patients had renal insufficiency, 25 patients (2.1%) had hepatorenal syndrome, 80 patients (6.8%) were in the perioperative liver transplant period, and 49 patients (4.2%) had one or more additional risk factors for NSF. None of the 1167 patients developed NSF.

Conclusion

Chronic liver disease does not appear to be a significant risk factor for NSF.

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 L 1 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.