Use of analytic morphomics of liver, spleen, and body composition to identify patients at risk for cirrhosis

Abstract

Background & aims: 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.

Keywords: Advanced Technology; Fibrosis Progression; Noninvasive Markers; Prognostic Factor.

Figure 1.

(A) Example of identification of spinal vertebral levels that serve as anatomic reference system for each patient. (B) Example of fascial envelope (yellow line) and skin outline (red line) that are generated for each patient. (C) Example of the paraspinus muscles (outlined in yellow) defined automatically after delineation of paraspinus lateral seams at specified vertebra points that is processed in each patient. (D) Example of MATLAB based 3-D image viewer graphical user interface (GUI) generated for each segmented liver.

Figure 2.

(A) Example of bounding box of segmented liver stored in database with X, Y, and Z dimensions. (B) Example of minimal bounding box of segmented liver where the box is oriented to achieve the smallest possible bounding box and the associated X, Y, and Z dimensions. (C) Example of slice of liver that contains bifurcation of main portal vein (*) represents measured X axis distance between lateral-most liver edge and portal vein bifurcation point (LATERAL2PORTBIFURPT_XLENMM). # represents X axis distance between portal vein bifurcation to caudate lobe (PORTBIFURPT2CAUDATE_XLENMM). (D) Example of L3 relative body size measurements. Yellow line delineates fascial area (FASCIAAREA.L3). Measured area between red line (skin outline) and yellow line delineates subcutaneous fat area (SUBCUTFATAREA.L3). ** represents measured distance from anterior surface of vertebral body to anterior midline fascia (VB2FASCIA).

Figure 3.

Receiver operating characteristic curves for final models with University of Michigan cohort.