Development of a quantitative method for the diagnosis of cirrhosis.

OBJECTIVE

To develop a novel non-invasive, quantitative approach utilizing computed tomography scans to predict cirrhosis.

MATERIALS AND METHODS

A total of 105 patients (54 cirrhosis and 51 normal) who had CT scans within 6 months of a liver biopsy or were identified through a Trauma registry were included in this study. Patients were randomly divided into the training set (n = 81) and the validation set (n = 24). Each liver was segmented in a semi-automated fashion from a computed tomography scan using Mimics software. The resulting liver surfaces were saved as a stereo lithography mesh into an Oracle database, and analyzed in MATLAB(®) for morphological markers of cirrhosis.

RESULTS

The best predictive model for diagnosing cirrhosis consisted of liver slice-bounding box slice ratio, the dimensions of the liver bounding box, liver slice area, slice perimeter, surface volume and adjusted surface area. With this model, we calculated an area under the receiver operating characteristic curve of 0.90 for the training set, and 0.91 for the validation set. For comparison, we calculated an area under the receiver operating characteristic curve of 0.70 for our dataset when we used the lab value based aspartate aminotransferase-platelet ratio index, another reported model for predicting cirrhosis. Last, by combining the aspartate aminotransferase-platelet ratio index and our model, we obtained an area under the receiving operating characteristic of 0.95.

CONCLUSION

This study shows "proof of concept" that quantitative image analysis of livers on computed tomography scans may be utilized to predict cirrhosis in the absence of a liver biopsy.