Multiparametric CT for Noninvasive Staging of Hepatitis C Virus-Related Liver Fibrosis: Correlation With the Histopathologic Fibrosis Score

Abstract

Objective: The objective was to develop a multiparametric CT algorithm to stage liver fibrosis in patients with chronic hepatitis C virus (HCV) infection.

Materials and methods: Abdominal CT and laboratory measures in 469 patients with HCV (340 men and 129 women; mean age, 50.1 years) were compared against the histopathologic Metavir fibrosis reference standard (F0, n = 49 patients; F1, n = 69 patients; F2, n = 102 patients; F3, n = 76 patients; F4, n = 173 patients). From the initial candidate pool, nine CT and two laboratory measures were included in the final assessment (CT-based features: hepatosplenic volumetrics, texture features, liver surface nodularity [LSN] score, and linear CT measurements; laboratory-based measures: Fibrosis-4 [FIB-4] score and aspartate transaminase-to-platelets ratio index [APRI]). Univariate logistic regression and multivariate logistic regression were performed with ROC analysis, proportional odds modeling, and probabilities.

Results: ROC AUC values for the model combining all 11 parameters for discriminating significant fibrosis (≥ F2), advanced fibrosis (≥ F3), and cirrhosis (F4) were 0.928, 0.956, and 0.972, respectively. For all nine CT-based parameters, these values were 0.905, 0.936, and 0.972, respectively. Using more simplified panels of two, three, or four parameters yielded good diagnostic performance; for example, a two-parameter model combining only LSN score with FIB-4 score had ROC AUC values of 0.886, 0.915, and 0.932, for significant fibrosis, advanced fibrosis, and cirrhosis. The LSN score performed best in the univariate analysis.

Conclusion: Multiparametric CT assessment of HCV-related liver fibrosis further improves performance over the performance of individual parameters. An abbreviated panel of LSN score and FIB-4 score approached the diagnostic performance of more exhaustive panels. Results of the abbreviated panel compare favorably with elastography, but this approach has the advantage of retrospective assessment using preexisting data without planning.

Keywords: CT; cirrhosis; hepatitis C virus; liver; liver fibrosis.

Figure 1.. Depiction of CT-based parameters for assessing hepatic fibrosis in 51-year-old with HCV and biopsy-proven F3 fibrosis.

A. Transverse (axial) CT image in portal venous phase shows morphologic changes of advanced fibrosis, including relative enlargement of the left lateral segment, mild surface nodularity, fissural widening, and splenomegaly. B. Transverse CT image for liver segmentation for texture analysis shows fine filtration (upper right), medium filtration (lower left), and coarse filtration (lower right). Only three texture variables were included in the multi-parametric analysis. C. Same image from (A) shows cross-sectional representation of hepatosplenic volumetric analysis, including liver segmentation into Couinaud segments I-III (blue) and IV-VIII (green), and splenic segmentation (orange). D. Same image from (A) now shows process for deriving the liver surface nodularity (LSN) score, which involves tracing along the left anterior liver surface with a broad stroke. E. Magnified image at same level (including inset image with even more magnification) shows how the LSN tool compares the actual detected liver surface (in green) against a smoothed polynomial line (spline, in red). This is repeated at multiple levels and averaged to derive the final LSN score. Note: The periportal space and left:right portal vein ratio are not depicted.

Figure 2.. ROC curves from the multi-parametric analysis for all CT input variables and for LSN/FIB-4 combination, according to significant fibrosis (≥F2), advanced fibrosis (≥F3), and cirrhosis (=F4).

Top Row: ROC curves for predicting fibrosis using all 9 CT parameters. AUC values for significant fibrosis, advanced fibrosis, and cirrhosis were 0.905, 0.936, and 0.972, respectively. Bottom Row: ROC AUC values for the limited combination of LSN score and FIB-4 were 0.886, 0.915, and 0.932, respectively. Only mild drop off in performance is seen, despite using only two complementary parameters (one CT value and one lab value).

Figure 3.. Example case using the simplified LSN score + FIB-4 model: 52-year-old man with biopsy proven F3 fibrosis from chronic HCV

A. CT image in portal venous phase shows LSN score in process. The final LSN score was 2.79. FIB-4 in this patient was 8.15 B. The probability equation for having advanced fibrosis is shown. When plugging in the values for this patient, the probability is 1.0, indicating the patient almost certainly will have advanced fibrosis or cirrhosis. Boxplots of probabilities is shown for the entire cohort.