Integrated quantitative susceptibility and R2 * mapping for evaluation of liver fibrosis: An ex vivo feasibility study

Abstract

To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R₂ * mapping using corrections for the effects of liver iron. Liver fibrosis is characterized by excessive accumulation of collagen and other extracellular matrix proteins. While collagen increases R₂ * relaxation, measures of R₂ * for fibrosis are confounded by liver iron, which may be present in the liver over a wide range of concentrations. The diamagnetic collagen contribution to susceptibility values measured by QSM is much less than the contribution of highly paramagnetic iron. In 19 ex vivo liver explants with and without fibrosis, QSM (χ), R₂ * and proton density fat fraction (PDFF) maps were constructed from multiecho gradient-recalled echo (mGRE) sequence acquisition at 3 T. Median parameter values were recorded and differences between the MRI parameters in nonfibrotic vs. advanced fibrotic/cirrhotic samples were evaluated using Mann-Whitney U tests and receiver operating characteristic analyses. Logistic regression with stepwise feature selection was employed to evaluate the utility of combined MRI measurements for detection of fibrosis. Median R₂ * increased in fibrotic vs. nonfibrotic liver samples (P = .041), while differences in χ and PDFF were nonsignificant (P = .545 and P = .395, respectively). Logistic regression identified the combination of χ and R₂ * significant for fibrosis detection (logit [prediction] = -8.45 + 0.23 R₂ * - 28.8 χ). For this classifier, a highly significant difference between nonfibrotic vs. advanced fibrotic/cirrhotic samples was observed (P = .002). The model exhibited an AUC of 0.909 (P = .003) for detection of advanced fibrosis/cirrhosis, which was substantially higher compared with AUCs of the individual parameters (AUC 0.591-0.784). An integrated QSM and R₂ * analysis of mGRE 3 T imaging data is promising for noninvasive diagnostic assessment of liver fibrosis.

Keywords: liver fibrosis; magnetic resonance imaging; quantitative susceptibility mapping.

Figure 1.

Boxplots of (a) R₂*, (b) χ, (c) PDFF and (d) logistic regression model prediction values in samples with no fibrosis and samples with advanced fibrosis cirrhosis. Note - The p-value of a Mann-Whitney U test comparing the groups is displayed on top of each plot.

Figure 2.

Heatmap of the likelihood of presence of advanced fibrosis or cirrhosis predicted by the logistic regression model as function of R₂* and χ. The data points of the non-fibrotic (white) and advanced fibrotic/cirrhotic (black) samples are superimposed on the map.

Figure 3.

R₂*, χ, PDFF and model prediction maps in a cross-sectional slice through a non-fibrotic liver sample (top) and a cirrhotic liver sample (bottom). The area around the vasculature was eroded from the masks used for the model prediction. Individual MRI parameters showed subtle changes between the two samples (non-fibrotic: median R₂* 56 s⁻¹, median χ 0.29 ppm, median PDFF 3%; cirrhotic median R₂*64 s⁻¹, median χ 0.13 ppm, median PDFF 3%). The model prediction showed substantially higher likelihood of cirrhosis for the cirrhotic sample vs. non-fibrotic sample (prediction 0.86 vs. 0.02). Masson’s trichrome staining confirmed lack of fibrosis in the top sample and cirrhosis in the bottom sample. Prussian Blue staining showed lack of iron deposition in the non-fibrotic sample and scattered Kupffer cell iron deposition in the cirrhotic sample.

Figure 4.

ROC curves for differentiation between samples without liver fibrosis vs. samples with advanced fibrosis or cirrhosis for R₂*, χ, PDFF and the logistic regression model combining R₂* and χ.