Novel measures for the diagnosis of hepatic steatosis using contrast-enhanced computer tomography images. Issue 160 (March 2023)

• Record Type: Journal Article
• Authors: Prinz, Sebastian; Murray, Jacob M.; Strack, Christian; Nattenmüller, Johanna; Pomykala, Kelsey L.; Schlemmer, Heinz-Peter; Badde, Stephanie; Kleesiek, Jens
• Is Part Of: European journal of radiology. Issue 160(2023)
• Publication Date: 2023-03
• Subjects: AUROC Area under the receiver operating characteristic curve -- CT computed tomography -- CECT contrast-enhanced CT -- FLD fatty liver disease -- HU Hounsfield Units -- NECT non-enhanced CT -- ROC receiver operating characteristic -- ROIs regions of interest -- VNC virtual non-contrast; Hepatic steatosis -- Computed tomography -- Automated segmentation; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2023.110708
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: HU value distributions across voxels could be surrogate markers for liver-fat-content. ROIs based on automatic organ segmentation lead to comparable results as manual ROIs. Implementation of automated methods can be a valuable tool for FLD screening. Abstract: Purpose: Hepatic steatosis is often diagnosed non-invasively. Various measures and accompanying diagnostic thresholds based on contrast-enhanced CT and virtual non-contrast images have been proposed. We compare these established criteria to novel and fully automated measures. Method: CT data sets of 197 patients were analyzed. Regions of interest (ROIs) were manually drawn for the liver, spleen, portal vein, and aorta to calculate four established measures of liver-fat. Two novel measures capturing the deviation between the empirical distributions of HU measurements across all voxels within the liver and spleen were calculated. These measures were calculated with both manual ROIs and using fully automated organ segmentations. Agreement between the different measures was evaluated using correlational analysis, as well as their ability to discriminate between fatty and healthy liver. Results: Established and novel measures of fatty liver were at a high level of agreement. Novel methods were statistically indistinguishable from the established ones when taking established diagnostic thresholds or physicians' diagnoses as ground truth and this high performance level persisted for automatically selected ROIs. Conclusion: Automatically generated organ segmentations led to comparable results as manual ROIs, suggesting that the implementation of automated methods can prove to be a valuable tool for incidental diagnosis. Differences in the distribution of HU measurements across voxels between liver and spleen can serve as surrogate markers for the liver-fat-content. Novel measures do not exhibit a measurable disadvantage over established methods based on simpler measures such as across-voxel averages in a population with low incidence of fatty liver.