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Purpose <p>This study aimed to develop and validate a prognostic model for hepatocellular carcinoma (HCC) patients undergoing liver resection, using the functional liver imaging score (FLIS) derived from hepatobiliary-specific contrast-enhanced magnetic resonance imaging (MRI).</p> Material and methods <p>A total of 694 pathologically confirmed HCC patients who underwent hepatobiliary-specific MRI with either gadoxetic acid or gadobenate dimeglumine and subsequent liver resection were included. FLIS was calculated by assigning 0–2 points to three hepatobiliary-phase MRI features: hepatic enhancement, biliary excretion, and portal vein signal intensity. Multivariable Cox regression identified AFP level, tumor size, and extent of resection as independent predictors of overall survival (OS).</p> Results <p>FLIS ≤ 2, alpha-fetoprotein (AFP) &gt; 400&#xa0;ng/mL, tumor size &gt; 5&#xa0;cm, and major resection were identified as independent predictors of worse OS. A predictive model combining these factors demonstrated excellent prognostic performance, with Harrell’s concordance indices of 0.91 in the training cohort and 0.96 internal validation cohort, and 0.94 in external validation cohort. The FLIS-based model significantly outperformed FLIS alone and conventional clinical models (<i>p</i> &lt; 0.05). Kaplan–Meier survival analysis showed that low-risk patients had significantly better OS and recurrence-free survival (RFS) compared to high-risk patients across all cohorts (<i>p</i> &lt; 0.05).</p> Conclusion <p>FLIS is a simple, non-invasive imaging biomarker for evaluating liver function and predicting outcomes in HCC patients. When integrated with key clinical variables, the FLIS-based model demonstrates excellent discrimination and calibration for OS and RFS, providing accurate postoperative prognostic stratification and showing great potential for guiding surveillance and improving long-term survival outcomes in future clinical applications.</p>

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Predictive model development and validation of functional liver imaging score for prognosis of patients with hepatocellular carcinoma after surgical resection: a multicenter study

  • 期刊论文

  • 发表日期：

    2025年11月2日

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  • Feier Ding,
  • Takashi Ota,
  • Shuo Cai,
  • Hui Ma,
  • Masahiro Yanagawa,
  • Atsushi Nakamoto,
  • Noriyuki Tomiyama,
  • Yidi Chen,
  • Bin Song,
  • Xinya Zhao

摘要

Purpose

This study aimed to develop and validate a prognostic model for hepatocellular carcinoma (HCC) patients undergoing liver resection, using the functional liver imaging score (FLIS) derived from hepatobiliary-specific contrast-enhanced magnetic resonance imaging (MRI).

Material and methods

A total of 694 pathologically confirmed HCC patients who underwent hepatobiliary-specific MRI with either gadoxetic acid or gadobenate dimeglumine and subsequent liver resection were included. FLIS was calculated by assigning 0–2 points to three hepatobiliary-phase MRI features: hepatic enhancement, biliary excretion, and portal vein signal intensity. Multivariable Cox regression identified AFP level, tumor size, and extent of resection as independent predictors of overall survival (OS).

Results

FLIS ≤ 2, alpha-fetoprotein (AFP) > 400 ng/mL, tumor size > 5 cm, and major resection were identified as independent predictors of worse OS. A predictive model combining these factors demonstrated excellent prognostic performance, with Harrell’s concordance indices of 0.91 in the training cohort and 0.96 internal validation cohort, and 0.94 in external validation cohort. The FLIS-based model significantly outperformed FLIS alone and conventional clinical models (p < 0.05). Kaplan–Meier survival analysis showed that low-risk patients had significantly better OS and recurrence-free survival (RFS) compared to high-risk patients across all cohorts (p < 0.05).

Conclusion

FLIS is a simple, non-invasive imaging biomarker for evaluating liver function and predicting outcomes in HCC patients. When integrated with key clinical variables, the FLIS-based model demonstrates excellent discrimination and calibration for OS and RFS, providing accurate postoperative prognostic stratification and showing great potential for guiding surveillance and improving long-term survival outcomes in future clinical applications.