Evidence map›Paper›PMID 40715517›Full record

ArticleAbdominal radiology (New York)2026

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

Brian A Derstine, Sven A Holcombe, Vincent L Chen, Manjunath P Pai, June A Sullivan, Stewart C Wang, Grace L Su

Abstract read
In one paragraph

Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Brian A DerstineMichigan Medicine, Ann Arbor, USA. bderstin@med.umich.edu.
Sven A HolcombeMichigan Medicine, Ann Arbor, USA.
Vincent L ChenMichigan Medicine, Ann Arbor, USA.
Manjunath P PaiMichigan Medicine, Ann Arbor, USA.
June A SullivanMichigan Medicine, Ann Arbor, USA.
Stewart C WangMichigan Medicine, Ann Arbor, USA.
Grace L SuMichigan Medicine, Ann Arbor, USA.

Funding

NOVEL STRATEGIES TO IMPROVE LIVER CANCER SURVEILLANCE UPTAKE AND EARLY DETECTIONU01CA230669 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LOK, ANNA S.F., SU, GRACE L. · 2018 to 2022
$3.3M
NCI NIH HHS U01CA230669
6 · The paper itself

Abstract

purposeEarly 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.

methodsThe 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.

resultsThe 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.

conclusionAutomated 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.

Indexed as

Artificial IntelligenceFatty LiverRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedContrast MediaFemaleHumansLiverMachine LearningMaleMiddle AgedRetrospective StudiesContrast Media

Identifiers

PMID40715517
PMCPMC12929335

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.