title: Use of analytic morphomics of liver, spleen, and body composition to identify patients at risk for cirrhosis.
abstract: OBJECTIVE
  A diagnosis of cirrhosis can be made on the basis of findings from imaging studies, but these are subjective. Analytic morphomics uses computational image processing algorithms to provide precise and detailed measurements of organs and body tissues. We investigated whether morphomic parameters can be used to identify patients with cirrhosis.
  
  
  METHODS
  In a retrospective study, we performed analytic morphomics on data collected from 357 patients evaluated at the University of Michigan from 2004 to 2012 who had a liver biopsy within 6 months of a computed tomography scan for any reason. We used logistic regression with elastic net regularization and cross-validation to develop predictive models for cirrhosis, within 80% randomly selected internal training set. The other 20% data were used as internal test set to ensure that model overfitting did not occur. In validation studies, we tested the performance of our models on an external cohort of patients from a different health system.
  
  
  RESULTS
  Our predictive models, which were based on analytic morphomics and demographics (morphomics model) or analytic morphomics, demographics, and laboratory studies (full model), identified patients with cirrhosis with area under the receiver operating characteristic curve (AUROC) values of 0.91 and 0.90, respectively, compared with 0.69, 0.77, and 0.76 for aspartate aminotransferase-to-platelet ratio, Lok Score, and FIB-4, respectively, by using the same data set. In the validation set, our morphomics model identified patients who developed cirrhosis with AUROC value of 0.97, and the full model identified them with AUROC value of 0.90.
  
  
  CONCLUSIONS
  We used analytic morphomics to demonstrate that cirrhosis can be objectively quantified by using medical imaging. In a retrospective analysis of multi-protocol scans, we found that it is possible to identify patients who have cirrhosis on the basis of analyses of preexisting scans, without significant additional risk or cost.
authors.affiliation: Veterans Administration Ann Arbor Healthcare System Center, Ann Arbor, Michigan; Department of Radiology, University of Michigan, Ann Arbor, Michigan.
authors.forename: Venkat
authors.initials: V
authors.lastname: Krishnamurthy
authors.affiliation: Department of Surgery, University of Michigan, Ann Arbor, Michigan; Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan.
authors.forename: Sampath
authors.initials: S
authors.lastname: Ethiraj
authors.affiliation: Department of Surgery, University of Michigan, Ann Arbor, Michigan; Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan.
authors.forename: Binu
authors.initials: B
authors.lastname: Enchakalody
authors.affiliation: Veterans Administration Ann Arbor Healthcare System Center, Ann Arbor, Michigan; Department of Medicine, University of Michigan, Ann Arbor, Michigan; Veterans Administration Center for Clinical Management Research, Ann Arbor, Michigan.
authors.forename: Akbar K
authors.initials: AK
authors.lastname: Waljee
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
authors.forename: Lu
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Surgery, University of Michigan, Ann Arbor, Michigan; Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan.
authors.forename: Stewart C
authors.initials: SC
authors.lastname: Wang
authors.affiliation: Veterans Administration Ann Arbor Healthcare System Center, Ann Arbor, Michigan; Morphomics Analysis Group, University of Michigan, Ann Arbor, Michigan; Department of Medicine, University of Michigan, Ann Arbor, Michigan. Electronic address: gsu@umich.edu.
authors.forename: Grace L
authors.initials: GL
authors.lastname: Su
mesh_terms: D000328:Adult; D000368:Aged; D001706:Biopsy; D001823:Body Composition; D015331:Cohort Studies; D005260:Female; D006651:Histocytochemistry; D006785:Hospitals, University; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000473:pathology*; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000473:pathology*; D008297:Male; D008824:Michigan; D008875:Middle Aged; D012189:Retrospective Studies; D018570:Risk Assessment; D013154:Spleen / Q000473:pathology*; D014057:Tomography, X-Ray Computed
keywords: Advanced Technology; Fibrosis Progression; Noninvasive Markers; Prognostic Factor
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