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<p>Hepatocellular carcinoma is among the leading causes of cancer-related mortality, and accurate survival prediction is crucial for personalized treatment. However, conventional approaches such as the Cox proportional hazards model often struggle with nonlinear relationships and high-dimensional data, resulting in suboptimal predictive performance. In this study, we utilized HCC patient data from the SEER and TCGA databases to investigate the potential of machine learning methods in HCC survival prediction. Specifically, we introduced a self-attention mechanism into DeepSurv and DeepHit to better capture feature dependencies and incorporated residual network modules to enhance the training stability of the deep architectures. Furthermore, we developed an ensemble model based on a Cox neural network, combining the predictions from our improved deep learning models, the Cox proportional hazards model, and random survival forest. Both the model improvements and the ensemble approach described here are being applied for the first time in survival analysis. Experimental results demonstrate that the ensemble model achieves superior predictive accuracy (C-index = 0.872) and reliability (Brier score at 9&#xa0;months = 0.149) compared to individual models. These findings indicate that an ensemble-learning-based model offers promising prospects for more precise individualized treatment of HCC.</p> Graphical Abstract <p></p>

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Construction and validation of hepatocellular carcinoma survival prediction models based on machine learning

  • 期刊论文

  • 发表日期：

    2025年11月3日

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  • Qiyang Zhao,
  • Ying Zhang,
  • Qun Xi

摘要

Hepatocellular carcinoma is among the leading causes of cancer-related mortality, and accurate survival prediction is crucial for personalized treatment. However, conventional approaches such as the Cox proportional hazards model often struggle with nonlinear relationships and high-dimensional data, resulting in suboptimal predictive performance. In this study, we utilized HCC patient data from the SEER and TCGA databases to investigate the potential of machine learning methods in HCC survival prediction. Specifically, we introduced a self-attention mechanism into DeepSurv and DeepHit to better capture feature dependencies and incorporated residual network modules to enhance the training stability of the deep architectures. Furthermore, we developed an ensemble model based on a Cox neural network, combining the predictions from our improved deep learning models, the Cox proportional hazards model, and random survival forest. Both the model improvements and the ensemble approach described here are being applied for the first time in survival analysis. Experimental results demonstrate that the ensemble model achieves superior predictive accuracy (C-index = 0.872) and reliability (Brier score at 9 months = 0.149) compared to individual models. These findings indicate that an ensemble-learning-based model offers promising prospects for more precise individualized treatment of HCC.

Graphical Abstract