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Objective <p>This study aims to systematically compare traditional statistical methods and machine learning approaches for survival prediction in adult-type diffuse glioma (ADG), evaluating their performance, interpretability, and clinical applicability.</p> Methods <p>Using two public datasets and one private retrospective cohort, four models (Cox Proportional Hazards, Random Survival Forest, Neural Multi Task Logistic Regression, and DeepSurv) were developed and validated. Missing values were handled in three different ways and subjected to sensitivity analysis.</p> Results <p>In both the internal test cohorts and two external validation cohorts, the neural network-based deep learning model outperformed the random survival forest and Cox proportional hazards model, with the DeepSurv model demonstrating the most robust performance. Key prognostic factors included age, molecular pathology, chemotherapy, extent of resection, extent of disease, and radiotherapy. Sensitivity analysis confirmed model stability. The model has been packaged and made publicly available.</p> Conclusion <p>For retrospective real-world ADG patient data with high heterogeneity and partial missingness, the DeepSurv model demonstrates superior survival prediction performance and stability compared to conventional approaches.</p>

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Deep learning model outperforms traditional models in clinical data-based prognostic prediction for adult-type diffuse glioma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Xiaopeng Li,
  • Chongshun Zhao,
  • Yanpeng Xia,
  • Ya Zhang,
  • Qian Zhang,
  • Mengdan Miao,
  • Zhijiang Xie,
  • Hongjing Yan,
  • Tao Zhou,
  • Shuanli Xin

摘要

Objective

This study aims to systematically compare traditional statistical methods and machine learning approaches for survival prediction in adult-type diffuse glioma (ADG), evaluating their performance, interpretability, and clinical applicability.

Methods

Using two public datasets and one private retrospective cohort, four models (Cox Proportional Hazards, Random Survival Forest, Neural Multi Task Logistic Regression, and DeepSurv) were developed and validated. Missing values were handled in three different ways and subjected to sensitivity analysis.

Results

In both the internal test cohorts and two external validation cohorts, the neural network-based deep learning model outperformed the random survival forest and Cox proportional hazards model, with the DeepSurv model demonstrating the most robust performance. Key prognostic factors included age, molecular pathology, chemotherapy, extent of resection, extent of disease, and radiotherapy. Sensitivity analysis confirmed model stability. The model has been packaged and made publicly available.

Conclusion

For retrospective real-world ADG patient data with high heterogeneity and partial missingness, the DeepSurv model demonstrates superior survival prediction performance and stability compared to conventional approaches.