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Background <p>PD-1 blockade therapy has emerged as a valuable treatment option for advanced hepatocellular carcinoma (HCC), but its therapeutic response and overall efficacy vary among patients. This study develops an automated framework for predicting response to PD-1 blockade with enhanced accuracy.</p> Methods <p>A comprehensive two-phase investigation was conducted, comprising a retrospective multicenter cohort (<i>n</i> = 793) for model development and a prospective cohort (<i>n</i> = 60) for validation. We established an integrated predictive framework combining ultrasound radiomics with clinical indicators. Model performance was evaluated by ROC analyses, focusing on the area under the curve (AUC). Molecular analyses of liver tissues were performed to explore mechanisms underlying treatment response.</p> Results <p>The ultrasound radiomics model achieved AUCs of 0.714 (training) and 0.617 (validation). The ensemble model, integrating both modalities, demonstrated superior predictive capability, with AUCs of 0.743 (training) and 0.641 (validation). The ensemble learning model, integrating both imaging and clinical modalities, exhibited superior predictive capability, attaining an AUC of 0.743 in the training cohort and 0.641 in the validation cohort. The ensemble model demonstrated exceptional clinical utility in predicting pathological necrosis following PD-1 blockade before hepatectomy, achieving an AUC of 0.692. Notably, it exhibited strong clinical utility in predicting pathological necrosis post-therapy, achieving an AUC of 0.692. Subsequent KEGG/GO analyses implicated key genes in necroptosis and programmed cell death pathways.</p> Conclusion <p>The proposed ultrasound-based ensemble model offers a non-invasive, reproducible method to predict PD-1 blockade response in HCC, effectively integrating imaging and clinical data to enhance predictive accuracy and reveal potential molecular mediators of therapeutic efficacy.</p> Summary <p>We developed an advanced automated predictive model that synergistically integrates ultrasound imaging with clinical indicators through ensemble learning methodology. This innovative model employs state-of-the-art deep learning architectures, specifically optimized convolutional neural networks, to accurately predict therapeutic response to PD-1 blockade in patients with unresectable hepatocellular carcinoma.</p>

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Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月7日

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  • Dong Jiang,
  • Zonghan Liu,
  • Kang Wang,
  • Yi Qian,
  • Jinkai Feng,
  • Lin Gong,
  • Jialun Ren,
  • Yanjun Xiang,
  • Fangfang Zhang,
  • Liheng Liu,
  • Hongkun Zhou,
  • Chao Liang,
  • Wenjing Wei,
  • Boyang Zang,
  • Chui Kong,
  • Yiran Li,
  • Shuqun Cheng

摘要

Background

PD-1 blockade therapy has emerged as a valuable treatment option for advanced hepatocellular carcinoma (HCC), but its therapeutic response and overall efficacy vary among patients. This study develops an automated framework for predicting response to PD-1 blockade with enhanced accuracy.

Methods

A comprehensive two-phase investigation was conducted, comprising a retrospective multicenter cohort (n = 793) for model development and a prospective cohort (n = 60) for validation. We established an integrated predictive framework combining ultrasound radiomics with clinical indicators. Model performance was evaluated by ROC analyses, focusing on the area under the curve (AUC). Molecular analyses of liver tissues were performed to explore mechanisms underlying treatment response.

Results

The ultrasound radiomics model achieved AUCs of 0.714 (training) and 0.617 (validation). The ensemble model, integrating both modalities, demonstrated superior predictive capability, with AUCs of 0.743 (training) and 0.641 (validation). The ensemble learning model, integrating both imaging and clinical modalities, exhibited superior predictive capability, attaining an AUC of 0.743 in the training cohort and 0.641 in the validation cohort. The ensemble model demonstrated exceptional clinical utility in predicting pathological necrosis following PD-1 blockade before hepatectomy, achieving an AUC of 0.692. Notably, it exhibited strong clinical utility in predicting pathological necrosis post-therapy, achieving an AUC of 0.692. Subsequent KEGG/GO analyses implicated key genes in necroptosis and programmed cell death pathways.

Conclusion

The proposed ultrasound-based ensemble model offers a non-invasive, reproducible method to predict PD-1 blockade response in HCC, effectively integrating imaging and clinical data to enhance predictive accuracy and reveal potential molecular mediators of therapeutic efficacy.

Summary

We developed an advanced automated predictive model that synergistically integrates ultrasound imaging with clinical indicators through ensemble learning methodology. This innovative model employs state-of-the-art deep learning architectures, specifically optimized convolutional neural networks, to accurately predict therapeutic response to PD-1 blockade in patients with unresectable hepatocellular carcinoma.