Prediction of Liver Function Based on DCE-CT

Abstract

Liver function analysis is crucial for staging and treating chronic liver diseases (CLD). Despite CLD being one of the most prevalent diseases of our time, research regarding liver in the Medical Image Computing community is often focused on diagnosing and treating CLD’s long term effects such as the occurance of malignancies, e.g. hepatocellular carcinoma. The Child-Pugh (CP) score is a surrogate for liver function used to quantify liver cirrhosis, a common CLD, and consists of 3 disease progression stages A, B and C. While a correlation between CP and liver specific contrast agent uptake for dynamic conrast enhanced (DCE)-MRI has been found, no such correlation has been shown for DCE-CT scans, which are more commonly used in clinical practice. Using a transfer learning approach, we train a CNN for prediction of CP based on DCE-CT images of the liver alone. Agreement between the achieved CNN based scoring and ground truth CP scores is statistically significant, and a rank correlation of 0.43, similar to what is reported for DCE-MRI, was found. Subsequently, a statistically significant CP classifier with an overall accuracy of 0.57 was formed by employing clinically used cutoff values.