# Quantification of hepatic steatosis on post-contrast computed tomography scans using artificial intelligence tools

## TL;DR

This study shows that artificial intelligence can accurately measure liver fat from post-contrast CT scans, enabling better detection of liver disease.

## Contribution

The study introduces a novel method to estimate liver fat using post-contrast CT scans with AI, which was previously only possible with non-contrast scans.

## Key findings

- Automated liver and spleen segmentation using AI is highly accurate compared to manual measurements.
- Liver attenuation alone in post-contrast CT scans can reliably detect moderate-to-severe liver steatosis.
- Correction equations for post-contrast liver attenuation enable accurate estimation of liver fat levels.

## Abstract

Early detection of steatotic liver disease (SLD) is critically important. In clinical practice, hepatic steatosis is frequently diagnosed using computed tomography (CT) performed for unrelated clinical indications. An equation for estimating magnetic resonance proton density fat fraction (MR-PDFF) using liver attenuation on non-contrast CT exists, but no equivalent equation exists for post-contrast CT. We sought to (1) determine whether an automated workflow can accurately measure liver attenuation, (2) validate previously identified optimal thresholds for liver or liver-spleen attenuation in post-contrast studies, and (3) develop a method for estimating MR-PDFF (FF) on post-contrast CT.

The fully automated TotalSegmentator ‘total’ machine learning model was used to segment 3D liver and spleen from non-contrast and post-contrast CT scans. Mean attenuation was extracted from liver (L) and spleen (S) volumes and from manually placed regions of interest (ROIs) in multi-phase CT scans of two cohorts: derivation (n = 1740) and external validation (n = 1044). Non-linear regression was used to determine the optimal coefficients for three phase-specific (arterial, venous, delayed) increasing exponential decay equations relating post-contrast L to non-contrast L. MR-PDFF was estimated from non-contrast CT and used as the reference standard.

The mean attenuation for manual ROIs versus automated volumes were nearly perfectly correlated for both liver and spleen (r > .96, p < .001). For moderate-to-severe steatosis (L < 40 HU), the density of the liver (L) alone was a better classifier than either liver-spleen difference (L-S) or ratio (L/S) on post-contrast CTs. Fat fraction calculated using a corrected post-contrast liver attenuation measure agreed with non-contrast FF > 15% in both the derivation and external validation cohort, with AUROC between 0.92 and 0.97 on arterial, venous, and delayed phases.

Automated volumetric mean attenuation of liver and spleen can be used instead of manually placed ROIs for liver fat assessments. Liver attenuation alone in post-contrast phases can be used to assess the presence of moderate-to-severe hepatic steatosis. Correction equations for liver attenuation on post-contrast phase CT scans enable reasonable quantification of liver steatosis, providing potential opportunities for utilizing clinical scans to develop large scale screening or studies in SLD.

The online version contains supplementary material available at 10.1007/s00261-025-05137-x.