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ECR 2015 / C-0222
Diagnostic value of MRI proton density fat fraction for assessing liver steatosis in chronic viral C hepatitis
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Congress: ECR 2015
Poster No.: C-0222
Type: Scientific Exhibit
Keywords: Tissue characterisation, Metabolic disorders, Infection, Technical aspects, Diagnostic procedure, Computer Applications-General, Ultrasound, MR, MR physics, Liver, Abdomen
Authors: R. Piccazzo1, F. Paparo1, L. Bacigalupo1, M. Revelli1, L. Cevasco1, L.-P. Rollandi1, A. Galletto2, G. A. Rollandi1; 1Genoa/IT, 2Borgio Verezzi/IT
DOI:10.1594/ecr2015/C-0222

Results

Four patients were excluded due to severe motion/respiratory artifacts in their MRIs, precluding an accurate measurement of PDFF. The resulting cohort of 77 patients with chronic C hepatitis included 43/77 (55.8%) males and 34/77 (44.2%) females with a mean age of 51.31±11.27 (from 18 to 81) years and a mean BMI of 22.39±2.27 (from 18.43 to 27). Seventy–one/77 patients (92.2%) presented detectable serum HCV–RNA levels (above the detection threshold of 15 IU/mL of our method), while 6/77 patients (7.8%) were in sustained virological response. In this latter subgroup, the standard treatment with peginterferon and ribavirin was stopped at least 18 months before the time of inclusion. Demographic, clinical and laboratory characteristics of patients are summarized in (Table 1). The mean MRI PDFF of our cohort of patients, expressed in percentage units, was 11.76±4.73 with a median of 5.87 (from 0.7 to 17.01). The mean liver T2* value was 30.33±5.98 ms with a median of 31.32 ms (from 16.36 to 43.6 ms). We did not find patients with a histological steatosis of grade 3 (S3), and hemosiderin deposits were found in 4 patients. In addition, T2* values were not indicative of hepatic iron overload of clinical significance (i.e. below the threshold of 6.3 ms) in any patient. Therefore, we were not able to assess the diagnostic performance of MRI PDFF for the detection of severe steatosis (i.e. grade S3, >66% fat–containing hepatocytes), and the potential confounding effect of iron overload on MRI PDFF measurements. On the other hand, we introduced T2* values in the partial correlation model in order to verify their influence on the correlation between MRI PDFF and histological FF.
Correlation and subgroup analysis

The correlation of the mean MRI PDFF value with the histological FF was moderate (r=0.624, 95%CI for rho 0.465 to 0.744, p<0.0001), while the correlation of the median MRI PDFF value with the histological FF was strong (r=0.754, 95%CI for rho 0.637 to 0.836, p<0.0001). The median MRI PDFF values for each steatosis grade were: 4.3 (0.7–10.09) for S0; 10.4 (3.7–16.2) for S1; 13.5 (8.4–17.01) for S2 (p<0.05) (Figures 2 and 3). Stratifying the cohort of patients according to the METAVIR stages of parenchymal fibrosis, the median MRI PDFF values resulted significantly different among different subgroups (p<0.05 with the Kruskal–Wallis test). The post–hoc analysis showed that the median MRI PDFF in the F4 subgroup was significantly lower than in the other subgroups of patients (p<0.05) (Table 2). Stratifying the cohort of patients according to the METAVIR stages of necroinflammatory activity, the Kruskal–Wallis test did not reveal a significant difference among the median MRI PDFF values of the four subgroups of patients (p>0.05) (Figure 4). Box–and–whisker plots for MRI PDFF measurements in relation to each grade of steatosis, fibrosis and necroinflammatory activity are shown in Figure 4.

Diagnostic accuracy of MRI PDFF

The diagnostic accuracy of MRI PDFF evaluated by AUC–ROC analysis was 0.926 (standard error 0.0354, 95%CI 0.843 to 0.973) for S≥1 and 0.929 (standard error 0.0363, 95%CI 0.847 to 0.975) for S=2. The best MRI PDFF cut–off value to differentiate between S0 vs. S1–S2 patients was 6.87, showing a sensitivity of 87.10% (95%CI 70.2–96.4), a specificity of 97.83 (95%CI 88.5–99.9%), a positive predictive value (PPV) of 96.4% (95%CI 81.7–99.9) and a negative predictive value (NPV) of 91.8% (95%CI 80.4–97.7) (Figure 5A). The best MRI PDFF cut-off value to differentiate between S0–S1 vs. S2 patients was 11.08, showing a sensitivity of 87.5% (95%CI 47.3–99.7), a specificity of 88.41% (95%CI 78.4–94.9), a positive predictive value (PPV) of 46.7% (95%CI 20.5–74.3) and a negative predictive value (NPV) of 98.4% (95%CI 91.3–100) (Figure 5B).

Influence of confounding variables on MRI PDFF measurements

The correlation between MRI PDFF and histological FF was strong even in a partial correlation model, using TE liver stiffness values (expressed in kPa) and T2* decay (expressed in ms) as covariates (r=0.775, p<0.0001).
The multiple regression analysis showed that only steatosis grade at histology and histological FF were factors independently associated to the median MRI PDFF (Table 3).

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