title: Development of a quantitative method for the diagnosis of cirrhosis.
abstract: OBJECTIVE
  To develop a novel non-invasive, quantitative approach utilizing computed tomography scans to predict cirrhosis.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
authors: Hannu
authors: H
authors: Huhdanpaa
authors: Christopher
authors: C
authors: Douville
authors: Kerry
authors: K
authors: Baum
authors: Venkat N
authors: VN
authors: Krishnamurthy
authors: Sven
authors: S
authors: Holcombe
authors: Binu
authors: B
authors: Enchakalody
authors: Lu
authors: L
authors: Wang
authors: Stewart C
authors: SC
authors: Wang
authors: Grace L
authors: GL
authors: Su
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001219:Aspartate Aminotransferases / Q000097:blood; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D008103:Liver Cirrhosis / Q000097:blood / Q000000981:diagnostic imaging*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010976:Platelet Count; D011237:Predictive Value of Tests; D012372:ROC Curve; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*
chemical_list: D001219:Aspartate Aminotransferases