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Background <p>Hepatic steatosis is a precursor to metabolic dysfunction-associated steatohepatitis (MASH), which is linked to obesity and diabetes and poses a risk for liver cirrhosis and cancer. For detecting MASH and hepatic steatosis, liver biopsy is the reference method, but it is invasive and impractical for repeated monitoring of treatment efficacy. Our goal was to develop a convenient and non-invasive tool for accurately identifying MASH and steatosis grade in people with obesity.</p> Methods <p>This retrospective study included 321 people with obesity who underwent bariatric surgery, during which liver biopsies were also performed. Clinical characteristics, presurgical computed tomography (CT)-derived parameters, magnetic resonance imaging proton density fat fraction (MRI-PDFF), and CT-based skeletal muscle index (SMI) were obtained.</p> Results <p>A convenient model consisting of liver CT attenuation value (CT<sub>Liver</sub>), alanine aminotransferase (ALT) and high-density lipoprotein cholesterol (HDL-C) was developed for identifying MASH, with area under the curve (AUC) of 0.799. To predict hepatic steatosis of grade ≥ 1, ≥ 2 and 3, the AUCs of CT<sub>Liver</sub>-based combination models were 0.954, 0.927, and 0.894, respectively. The diagnostic performance of the CT<sub>Liver</sub>-based combination models was comparable to that of liver MRI-PDFF in identifying different hepatic steatosis grades (Delong’s test, <i>p</i> &gt; 0.05). CT-based SMI was significantly positively correlated with hepatic steatosis grade (<i>r</i> = 0.12, <i>p</i> = 0.031) and non-alcoholic fatty liver disease (NAFLD) activity score (<i>r</i> = 0.18, <i>p</i> = 0.002).</p> Conclusions <p>The liver CT-based composite biomarkers can identify MASH and hepatic steatosis grade with high diagnostic performance in people with obesity, providing a non-invasive, accessible, and practical supplementary tool for clinical stratified management.</p>

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Liver CT-based composite biomarkers can identify MASH and steatosis grade in people with obesity prior to bariatric surgery: a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月14日

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  • Hailong Zhang,
  • Zhuoru Jiang,
  • Huanhuan Zheng,
  • Haoyao Wang,
  • Liankun Xia,
  • Jun Chen,
  • Bing Zhang

摘要

Background

Hepatic steatosis is a precursor to metabolic dysfunction-associated steatohepatitis (MASH), which is linked to obesity and diabetes and poses a risk for liver cirrhosis and cancer. For detecting MASH and hepatic steatosis, liver biopsy is the reference method, but it is invasive and impractical for repeated monitoring of treatment efficacy. Our goal was to develop a convenient and non-invasive tool for accurately identifying MASH and steatosis grade in people with obesity.

Methods

This retrospective study included 321 people with obesity who underwent bariatric surgery, during which liver biopsies were also performed. Clinical characteristics, presurgical computed tomography (CT)-derived parameters, magnetic resonance imaging proton density fat fraction (MRI-PDFF), and CT-based skeletal muscle index (SMI) were obtained.

Results

A convenient model consisting of liver CT attenuation value (CTLiver), alanine aminotransferase (ALT) and high-density lipoprotein cholesterol (HDL-C) was developed for identifying MASH, with area under the curve (AUC) of 0.799. To predict hepatic steatosis of grade ≥ 1, ≥ 2 and 3, the AUCs of CTLiver-based combination models were 0.954, 0.927, and 0.894, respectively. The diagnostic performance of the CTLiver-based combination models was comparable to that of liver MRI-PDFF in identifying different hepatic steatosis grades (Delong’s test, p > 0.05). CT-based SMI was significantly positively correlated with hepatic steatosis grade (r = 0.12, p = 0.031) and non-alcoholic fatty liver disease (NAFLD) activity score (r = 0.18, p = 0.002).

Conclusions

The liver CT-based composite biomarkers can identify MASH and hepatic steatosis grade with high diagnostic performance in people with obesity, providing a non-invasive, accessible, and practical supplementary tool for clinical stratified management.