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<p>Selecting first-line treatments for advanced hepatocellular carcinoma (HCC) requires carefully balancing the survival benefits against the risk of serious adverse events (SAEs). However, conventional evaluation methods often fail to incorporate multiple clinical endpoints into a single unified framework. To address this limitation, we developed the Efficacy-Safety Integrated Ranking Algorithm (ESIRA), a quantitative framework that generates composite treatment rankings by assigning adjustable weights to efficacy and safety outcomes and calculating the Euclidean distance of each regimen to an ideal efficacy-safety profile. In a training cohort comprising 4,179 patients from eight randomized trials, treatment rankings varied markedly depending on the weighting scheme. Nivolumab plus ipilimumab ranked highest when the safety weight exceeded 0.7, whereas sintilimab plus bevacizumab and camrelizumab plus apatinib rose sharply in ranking when the efficacy weight exceeded 0.8. Sensitivity analysis identified 0.7:0.3 as the optimal efficacy-to-safety weight ratio, under which nivolumab plus ipilimumab achieved the highest composite <i>Q</i>-value (<i>Q</i> is the ranking function with higher values indicating higher ranks) (<i>Q</i> 0.60, 95%CI 0.54–0.68; <i>P</i>=0.192). The robustness of ESIRA was supported by internal consistency analyses within the training cohort. When efficacy weights were below 0.2, the rankings showed strong concordance with the safety endpoint (Spearman’s correlation coefficient, Spearman’s <i>ρ</i>&gt;0.95), while prioritizing efficacy (weight&gt;0.8) resulted in increasing correlations with efficacy endpoints (Spearman’s <i>ρ</i>&gt;0.9). In addition, ESIRA rankings were reproduced in two independent validation cohorts, further demonstrating consistency across heterogeneous datasets. By quantitatively integrating multiple clinical outcomes, ESIRA provides a structured approach to treatment selection that may better capture the trade-offs encountered in real-world clinical decision-making for advanced HCC.</p>

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ESIRA, a novel framework integrating efficacy and safety for evaluating first-line treatments in advanced hepatocellular carcinoma

  • 期刊论文

  • 发表日期：

    2026年1月27日

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  • Hai-Long Li,
  • Hui Zhou,
  • Yi-Yang Zhang,
  • Jin-Ting Lai,
  • Zhen-Zhong Zhou,
  • Jing-Yi Huang,
  • Song-Bin Guo,
  • Yu Wang,
  • Wei-Juan Huang,
  • Xiao-Peng Tian

摘要

Selecting first-line treatments for advanced hepatocellular carcinoma (HCC) requires carefully balancing the survival benefits against the risk of serious adverse events (SAEs). However, conventional evaluation methods often fail to incorporate multiple clinical endpoints into a single unified framework. To address this limitation, we developed the Efficacy-Safety Integrated Ranking Algorithm (ESIRA), a quantitative framework that generates composite treatment rankings by assigning adjustable weights to efficacy and safety outcomes and calculating the Euclidean distance of each regimen to an ideal efficacy-safety profile. In a training cohort comprising 4,179 patients from eight randomized trials, treatment rankings varied markedly depending on the weighting scheme. Nivolumab plus ipilimumab ranked highest when the safety weight exceeded 0.7, whereas sintilimab plus bevacizumab and camrelizumab plus apatinib rose sharply in ranking when the efficacy weight exceeded 0.8. Sensitivity analysis identified 0.7:0.3 as the optimal efficacy-to-safety weight ratio, under which nivolumab plus ipilimumab achieved the highest composite Q-value (Q is the ranking function with higher values indicating higher ranks) (Q 0.60, 95%CI 0.54–0.68; P=0.192). The robustness of ESIRA was supported by internal consistency analyses within the training cohort. When efficacy weights were below 0.2, the rankings showed strong concordance with the safety endpoint (Spearman’s correlation coefficient, Spearman’s ρ>0.95), while prioritizing efficacy (weight>0.8) resulted in increasing correlations with efficacy endpoints (Spearman’s ρ>0.9). In addition, ESIRA rankings were reproduced in two independent validation cohorts, further demonstrating consistency across heterogeneous datasets. By quantitatively integrating multiple clinical outcomes, ESIRA provides a structured approach to treatment selection that may better capture the trade-offs encountered in real-world clinical decision-making for advanced HCC.