Quantitative imaging: quantification of liver shape on CT using the statistical shape model to evaluate hepatic...

To investigate the usefulness of the statistical shape model (SSM) for the quantification of liver shape to evaluate hepatic fibrosis.

Ninety-one subjects (45 men and 46 women; age range, 20-75 years) were included in this retrospective study: 54 potential liver donors and 37 patients with chronic liver disease. The subjects were classified histopathologically according to the fibrosis stage as follows: F0 (n = 55); F1 (n = 6); F2 (3); F3 (n = 1); and F4 (n = 26). Each subject underwent contrast-enhanced computed tomography (CT) using a 64-channel scanner (0.625-mm slice thickness). An abdominal radiologist manually traced the liver boundaries on every CT section using an image workstation; the boundaries were used for subsequent analyses. An SSM was constructed by the principal component analysis of the subject data set, which defined a parametric model of the liver shapes. The shape parameters were calculated by fitting SSM to the segmented liver shape of each subject and were used for the training of a linear support vector regression (SVR), which classifies the liver fibrosis stage to maximize the area under the receiver operating characteristic curve (AUC). SSM/SVR models were constructed and were validated in a leave-one-out manner. The performance of our technique was compared to those of two previously reported types of caudate-right lobe ratios (C/RL-m and C/RL-r).

In our SSM/SVR models, the AUC values for the classification of liver fibrosis were 0.96 (F0 vs. F1-4), 0.95 (F0-1 vs. F2-4), 0.96 (F0-2 vs. F3-4), and 0.95 (F0-3 vs. F4). These values were significantly superior to AUC values using the C/RL-m or C/RL-r ratios (P < .005).

SSM was useful for estimating the stage of hepatic fibrosis by quantifying liver shape.