Evidence map›Paper›PMID 41703539›Full record

ArticleBMC medicine2026

Spatial characterization of intrahepatic fat deposition across steatotic liver disease subcategories: a multicenter study.

Yan-Ci Zhao, Min Wang, Shuzhen Wu, Yuanyuan Bao, Zeyan Wu, Shengze Jin, Yang Cao, Yanyan Zhu, Junhan Pan, Huizhen Huang and 5 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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. Article
  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

15 authors.

Yan-Ci Zhao *Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Min Wang *Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China. wangmin707@zju.edu.cn.
Shuzhen Wu *Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yuanyuan Bao *Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, Jiangsu, China.
Zeyan WuKey Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
Shengze JinDepartment of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, Zhejiang, China.
Yang CaoKey Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
Yanyan ZhuDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Junhan PanDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Huizhen HuangDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Shuhan LiuDepartment of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, Jiangsu, China.
Wuyue ChenDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Wenbin JiDepartment of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, Zhejiang, China. jiwb@enzemed.com.
Xiaoli MaiDepartment of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, 210008, Jiangsu, China. maixl@nju.edu.cn.
Feng ChenDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. chenfenghz@zju.edu.cn.

Funding

National Natural Science Foundation of China 12090025
6 · The paper itself

Abstract

backgroundIntrahepatic fat accumulation presents spatial heterogeneity, potentially varying across steatotic liver disease (SLD) subcategories. Current whole-liver spatial profiling remains limited. In this study, we systematically evaluated spatial fat distribution patterns in metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-related liver disease (ALD), and SLD with chronic hepatitis B (SLD&CHB) using a fully automated nnU-Net model for whole-liver segmentation and two-point Dixon MRI-based fat fraction mapping.

methodsThis multicenter retrospective study included the analysis of 682 patients with hepatic steatosis (MASLD: 451; ALD: 89; SLD&CHB: 142) who underwent two-point Dixon MRI. The fully automated nnU-Net-based MRI framework was developed with in-phase (IP), out-phase (OP), water, and fat images, and the model performance was quantified using the Dice similarity coefficient (DSC) score. The fat fraction (FF), standard deviation of FF (SD-FF), lobar asymmetry, and periportal-to-peripheral gradients were compared across SLD subcategories and steatosis severity levels (L1: mild, L2: moderate, L3: severe).

resultsThe segmentation model achieved: whole-liver DSC: 0.94; left lobe DSC: 0.94; right lobe DSC: 0.96. Compared with ALD and SLD&CHB, MASLD exhibited the highest fat burden (FF: 16.29%) and greatest spatial heterogeneity (SD-FF: 8.22). FF was significantly higher in the right lobe than in the left lobe in MASLD and ALD (both P < .05), whereas no significant lobar difference was observed in SLD&CHB. SD-FF was consistently higher in the left lobe across all SLD subcategories (P < .001), indicating greater heterogeneity of fat distribution in the left lobe. Periportal-to-peripheral gradient analysis revealed peripheral fat enrichment in MASLD and ALD, while the opposite trend in SLD&CHB.

conclusionsThis study developed a fully automated quantification model for the spatial distribution of whole-liver fat. Characterization of the spatial heterogeneity of hepatic fat accumulation provides additional, phenotypically relevant information across SLD subcategories. These findings highlight the clinical utility of spatial heterogeneity as a novel, noninvasive imaging biomarker for subtype identification and personalized disease assessment of SLD. Moreover, this automated framework offers a scalable and objective tool for future longitudinal monitoring.

Indexed as

Fatty LiverLiverAdultFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedRetrospective StudiesDixon sequenceFat distributionMagnetic resonance imagingnnU-NetSpatial heterogeneitySteatotic liver disease

Identifiers

PMID41703539
PMCPMC13020284

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