Quantitative detection of cirrhosis: towards the development of computer-assisted detection method

J Digit Imaging. 2014 Oct;27(5):601-9. doi: 10.1007/s10278-014-9696-x.


There are distinct morphologic features of cirrhosis on CT examinations; however, such impressions may be subtle or subjective. The purpose of this study is to build a computer-aided diagnosis (CAD) method to help radiologists with this diagnosis. One hundred sixty-seven abdominal CT examinations were randomly divided into training (n = 88) and validation (n = 79) sets. Livers were analyzed for morphological markers of cirrhosis and logistic regression models were created. Using the area under curve (AUC) for model performance, the best model had 0.89 for the training set and 0.85 for the validation set. For radiology reports, sensitivity of reporting cirrhosis was 0.45 and specificity 0.99. Using the predictive model adjunctively, radiologists' sensitivity increased to 0.63 and specificity slightly decreased to 0.97. This study demonstrates that quantifying morphological features in livers may be utilized for diagnosing cirrhosis and for developing a CAD method for it.

MeSH terms

  • Area Under Curve
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Liver / diagnostic imaging
  • Liver Cirrhosis / diagnostic imaging*
  • Observer Variation
  • ROC Curve
  • Radiology / education
  • Radiology / methods
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Tomography, X-Ray Computed / methods*