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<p>In addressing the challenge of optimizing targeted combined immunotherapy for advanced hepatocellular carcinoma (HCC), this study developed and validated a novel prognostic model, the Target Immunotherapy Predict Model (TIPM), utilizing ultrasound and serological markers. Data from patients at Mengchao Hepatobiliary Hospital and Fujian Provincial Cancer Hospital were analyzed, encompassing demographics, serological markers, and ultrasound findings, including tumor and peritumoral tissue stiffness changes pre- and post-treatment. The multivariate analysis revealed the neutrophil-to-lymphocyte ratio (NLR), ΔT (tumor stiffness change), tumor diameter, and albumin levels as independent predictors of therapy response. The TIPM model, integrating these factors, demonstrated superior predictive accuracy, validated by Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis across both training and external validation cohorts. This predictive model stands to refine clinical decision-making, potentially improving treatment outcomes for advanced HCC patients by identifying those most likely to benefit from combined immunotherapy approaches.</p>

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Construction and validation of a novel model for guiding targeted combined immunotherapy in advanced hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Haibin Tu,
  • Siyi Feng,
  • Lihong Chen,
  • Yujie Huang,
  • Juzhen Zhang,
  • Suyu Peng,
  • Dingluan Lin,
  • Xiaojian Ye

摘要

In addressing the challenge of optimizing targeted combined immunotherapy for advanced hepatocellular carcinoma (HCC), this study developed and validated a novel prognostic model, the Target Immunotherapy Predict Model (TIPM), utilizing ultrasound and serological markers. Data from patients at Mengchao Hepatobiliary Hospital and Fujian Provincial Cancer Hospital were analyzed, encompassing demographics, serological markers, and ultrasound findings, including tumor and peritumoral tissue stiffness changes pre- and post-treatment. The multivariate analysis revealed the neutrophil-to-lymphocyte ratio (NLR), ΔT (tumor stiffness change), tumor diameter, and albumin levels as independent predictors of therapy response. The TIPM model, integrating these factors, demonstrated superior predictive accuracy, validated by Receiver Operating Characteristic (ROC) curves, calibration curves, and decision curve analysis across both training and external validation cohorts. This predictive model stands to refine clinical decision-making, potentially improving treatment outcomes for advanced HCC patients by identifying those most likely to benefit from combined immunotherapy approaches.