Scholarlink

Objective <p>Fatty liver index (FLI) is widely used as a non-invasive tool for identifying hepatic steatosis but performs poorly in lean individuals and leaves a substantial proportion of individuals within an indeterminate range. This cross-sectional study evaluated whether combination strategies could improve identification of hepatic steatosis in a selected referred population with suspected steatotic liver disease (SLD).</p> Methods <p>In a derivation cohort of 1648 subjects with suspected SLD who underwent MRI-PDFF assessment between January 2015 and January 2022, we evaluated five non-invasive indices (FLI, hepatic steatosis index [HSI], liver fat score [LFS], visceral adiposity index [VAI], and triglyceride-glucose index [TyG]). Four FLI-based sequential strategies were developed and further validated in an independent biopsy-based cohort of 426 patients from five tertiary centers in South China.</p> Results <p>FLI performed best in identifying hepatic steatosis among the five non-invasive indices, with an AUROC of 0.75, but showed a high missed-identification rate exceeding 90% in individuals with BMI &lt; 23&#xa0;kg/m². In Strategy 4, the FLI-LFS sequential algorithm improved identification performance in lean individuals. In the derivation cohort, sensitivity increased from 9.4% to 78.6%, the missed-identification rate decreased from 90.6% to 21.4%, and accuracy increased from 30.0% to 73.1%. A similar improvement in missed identification was observed in the validation cohort.</p> Conclusions <p>Compared with FLI alone, the sequential FLI–LFS algorithm substantially improved the identification of hepatic steatosis among lean individuals with suspected SLD. The strategy may help identify individuals who require further imaging and etiological evaluation but should not be regarded as a stand-alone tool for determining the subtype or cause of SLD.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A fatty liver index-based sequential strategy for identifying hepatic steatosis in individuals with suspected steatotic liver disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

查看全文
  • Yan Yang,
  • Xiongcai Feng,
  • Hong Deng,
  • Zhiyong Dong,
  • Lishu Xu,
  • Shiting Feng,
  • Bing Liao,
  • Congxiang Shao,
  • Pinning Feng,
  • Bihui Zhong,
  • Junzhao Ye

摘要

Objective

Fatty liver index (FLI) is widely used as a non-invasive tool for identifying hepatic steatosis but performs poorly in lean individuals and leaves a substantial proportion of individuals within an indeterminate range. This cross-sectional study evaluated whether combination strategies could improve identification of hepatic steatosis in a selected referred population with suspected steatotic liver disease (SLD).

Methods

In a derivation cohort of 1648 subjects with suspected SLD who underwent MRI-PDFF assessment between January 2015 and January 2022, we evaluated five non-invasive indices (FLI, hepatic steatosis index [HSI], liver fat score [LFS], visceral adiposity index [VAI], and triglyceride-glucose index [TyG]). Four FLI-based sequential strategies were developed and further validated in an independent biopsy-based cohort of 426 patients from five tertiary centers in South China.

Results

FLI performed best in identifying hepatic steatosis among the five non-invasive indices, with an AUROC of 0.75, but showed a high missed-identification rate exceeding 90% in individuals with BMI < 23 kg/m². In Strategy 4, the FLI-LFS sequential algorithm improved identification performance in lean individuals. In the derivation cohort, sensitivity increased from 9.4% to 78.6%, the missed-identification rate decreased from 90.6% to 21.4%, and accuracy increased from 30.0% to 73.1%. A similar improvement in missed identification was observed in the validation cohort.

Conclusions

Compared with FLI alone, the sequential FLI–LFS algorithm substantially improved the identification of hepatic steatosis among lean individuals with suspected SLD. The strategy may help identify individuals who require further imaging and etiological evaluation but should not be regarded as a stand-alone tool for determining the subtype or cause of SLD.