Evidence map›Paper›PMID 40765093›Full record

ArticleCurrent medical imaging2025

Predicting Chronic Liver Disease Severity by Liver and Splenic Extracellular Volume Fraction Derived from spectral-CT.

Yiming Yang, Zhiyuan Chen, Dongjing Zhou, Mengya Guo, Yan Qi, Mengqi Yu, Yupin Liu

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Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yiming YangDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Zhiyuan ChenDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Dongjing ZhouDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Mengya GuoDepartment of Imaging Research Center, GE HealthCare, Guangzhou, China.
Yan QiDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Mengqi YuDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Yupin LiuDepartment of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo evaluate the effectiveness of spectral-CT in assessing the severity of liver diseases in patients with chronic liver disease (CLD).

methodsA total of 148 CLD patients who underwent spectral-CT were retrospectively recruited, including 40 non-advanced CLD (non-ACLD), 74 compensated ACLD (cACLD), and 34 decompensated ACLD (dACLD). Iodine concentrations in the liver and spleen were assessed on iodine (water) images during the equilibrium phase, which allowed for the calculation of liver and splenic extracellular volume fractions (ECV). We determined the total liver volume, liver segmental volume ratio, and splenic volume from portal phase images. Moreover, established non-invasive tests were also collected. Areas under receiver operating characteristic curve (AUCs) were employed to evaluate the diagnostic performance of CT parameters and non-invasive tests in predicting CLD severity. Additionally, we analyzed the correlations between CT parameters and non-invasive tests.

resultsThe spleen volume demonstrated the highest AUC (0.815, P<0.001) for distinguishing between non-ACLD and cALCD. Child-Pugh score exhibited the highest AUC (0.948, P<0.001) for distinguishing cALCD and dACLD. Splenic ECV exhibited the highest AUC (0.853, P<0.001) for distinguishing non-ALCD and ACLD. In contrast, the liver ECV showed strong correlations with the Fibrosis-4 Index (r=0.653, p<0.001) and the Aminotransferase-to-Platelet Ratio Index (r=0.607, p<0.001), while spleen ECV correlated more strongly with the Child-Pugh score (r=0.719, p<0.001) and the Albumin-Bilirubin Index (r=0.742, p<0.001). DISCUSSION: Liver and splenic ECV can effectively reflect the dynamic progression of CLD and correlate well with non-invasive tests in these patients.

conclusionSpectral-CT liver and splenic ECV could serve as non-invasive imaging biomarkers for severity stratification.

Indexed as

LiverLiver DiseasesSpleenTomography, X-Ray ComputedAdultAgedChronic DiseaseFemaleHumansMaleMiddle AgedOrgan SizeRetrospective StudiesSeverity of Illness IndexChronic liver diseaseExtracellular volume fraction.LiverSeverity stratificationSpectral computed tomographySpleen

Identifiers

PMID40765093
PMCPMC13223410

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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 Socratic graph.