Integrated Quantitative Susceptibility And R-2* Mapping For Evaluation Of Liver Fibrosis: An Ex Vivo Feasibility Study

To develop a method for noninvasive evaluation of liver fibrosis, we investigated the differential sensitivities of quantitative susceptibility mapping (QSM) and R-2* 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 R-2* relaxation, measures of R-2* 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 (chi), R-2* 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 R-2* increased in fibrotic vs. nonfibrotic liver samples (P= .041), while differences in chi and PDFF were nonsignificant (P= .545 andP= .395, respectively). Logistic regression identified the combination of chi and R-2* significant for fibrosis detection (logit [prediction] = -8.45 + 0.23 R-2* - 28.8 chi). 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 R-2* analysis of mGRE 3 T imaging data is promising for noninvasive diagnostic assessment of liver fibrosis.