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Abstract

Objectives: Our objective was to assess the accuracy of computed tomography volumetry in estimating liver volume in steatotic patients. Materials and Methods: We divided 641 liver donors (mean age 27 years, 71% male) into 4 groups ac cording to the extent of steatosis on predonation biopsy: with < 5% comprising group 1, 5% to 9% comprising group 2, 10% to 19% comprising group 3, and ≥ 20% comprising group 4. Graft mass estimation error (%) was calculated as follows: [(computed tomographymeasured volume minus graft weight)/graft weight] × 100. We obtained estimation errors, intraclass correlation coefficients, and Pearson correlation coefficients across the groups. Results: Baseline alanine aminotransferase and gammaglutamyltranspeptidase values were signi ficantly correlated with extent of steatosis. Mean graft weight and computed tomography-measured volume were 725.9 g and 741.2 mL. Mean estimation errors were comparable (1.5% for group 1, 2.7% for group 2, 3.0% for group 3, and 3.9% for group 4; P = .77). In multivariate linear regression, estimation error was inversely correlated with high-density lipoprotein cholesterol (P = .008). Overall, there was an excellent agreement between measured volume and actual weight, with intraclass correlation coefficients over 0.85 and Pearson correlation coefficients over 0.70 in all groups (P < .001).Conclusions: Preoperative computed tomography volumetry is an accurate tool for estimating volume regardless of the extent of steatosis.

CT Volumetry of Convoluted Objects—A Simple Method Using Volume Averaging

“…Other published methods have shown variations in accuracy. For instance, the percent error reported from using the planimetry approach was between 1% and 19% [1,2]. Manios et al [10] reported an error ranging from 5% to 9% using the random marking method.…”

“…Since the introduction of computed tomography (CT) in the 1970s, there have been numerous methods proposed to estimate the volume of organs or tissues from CT images. The manual planimetric method of tracing the tissue's boundary is perhaps the simplest approach but has its weaknesses [1][2][3][4]: it is tedious, prone to inter-user variation, has difficulty in identifying unsharp interfaces and is often inaccurate [5][6][7]. Other, more sophisticated approaches include:…”