医学
生物标志物
多中心研究
病理
疾病
内科学
脂肪肝
空间异质性
肝病
计算生物学
成像生物标志物
鉴定(生物学)
精密医学
疾病监测
沉积(地质)
生物信息学
生物标志物发现
脂肪堆积
脂肪变性
脂肪分布
遗传异质性
疾病严重程度
临床疾病
慢性肝病
作者
Yan-Ci Zhao,Min Wang,Shuzhen Wu,Yuanyuan Bao,Zeyan Wu,Shengze Jin,Yang Cao,Yanyan Zhu,Junhan Pan,H Huang,Shuhan Liu,Wuyue Chen,Wenbin Ji,Xiaoli Mai,Feng Chen
出处
期刊:BMC Medicine
[BioMed Central]
日期:2026-02-18
卷期号:24 (1)
被引量:1
标识
DOI:10.1186/s12916-026-04706-1
摘要
BACKGROUND: Intrahepatic 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. METHODS: This 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). RESULTS: The 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. CONCLUSIONS: This 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.
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