Prediction of Liver Function Based on DCE-CT

  • Oliver RippelEmail author
  • Daniel Truhn
  • Johannes Thüring
  • Christoph Haarburger
  • Christiane K. Kuhl
  • Dorit Merhof
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


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.


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Oliver Rippel
    • 1
    Email author
  • Daniel Truhn
    • 2
  • Johannes Thüring
    • 2
  • Christoph Haarburger
    • 1
  • Christiane K. Kuhl
    • 2
  • Dorit Merhof
    • 1
  1. 1.Institute of Imaging & Computer VisionRWTH Aachen UniversityAachenDeutschland
  2. 2.Department of Diagnostic and Interventional RadiologyUniversity Hospital AachenAachenDeutschland

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