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<p>Adult diffuse gliomas are clinically and molecularly heterogeneous, complicating risk stratification and personalized management. We introduce GlioSurv, a multimodal transformer model based on an accelerated failure time framework to integrate multiparametric MRI, clinical and molecular variables, and treatment data for personalized survival prediction. In a retrospective analysis of 1944 patients, including one internal cohort (<i>n</i> = 891; mean OS 32.2 months) and three external cohorts (<i>n</i> = 84, 470, 499; mean OS 26.1, 18.8, 19.0 months), GlioSurv demonstrated robust discrimination (IAUC: 0.68–0.86), calibration (IBS: 0.10–0.21) and concordance (C-index: 0.61–0.80). It significantly outperformed a convolutional neural network, a vision transformer, and a non-imaging multimodal transformer (<i>p</i> &lt; 0.01). Sequential integration of imaging, clinical, molecular, then treatment data, progressively improved C-index from 0.69 to 0.80 (<i>p</i> &lt; 0.001). Interpretability analyses confirmed established prognostic factors and indicate the potential of GlioSurv to support personalized survival prediction and risk-stratified decision-making in diffuse glioma.</p>

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GlioSurv: interpretable transformer for multimodal, individualized survival prediction in diffuse glioma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月14日

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  • Junhyeok Lee,
  • Joon Jang,
  • Heeseong Eum,
  • Han Jang,
  • Minchul Kim,
  • Sung Hye Park,
  • Chul Kee Park,
  • Seung Hong Choi,
  • Sung Soo Ahn,
  • Yoseob Han,
  • Kyu Sung Choi

摘要

Adult diffuse gliomas are clinically and molecularly heterogeneous, complicating risk stratification and personalized management. We introduce GlioSurv, a multimodal transformer model based on an accelerated failure time framework to integrate multiparametric MRI, clinical and molecular variables, and treatment data for personalized survival prediction. In a retrospective analysis of 1944 patients, including one internal cohort (n = 891; mean OS 32.2 months) and three external cohorts (n = 84, 470, 499; mean OS 26.1, 18.8, 19.0 months), GlioSurv demonstrated robust discrimination (IAUC: 0.68–0.86), calibration (IBS: 0.10–0.21) and concordance (C-index: 0.61–0.80). It significantly outperformed a convolutional neural network, a vision transformer, and a non-imaging multimodal transformer (p < 0.01). Sequential integration of imaging, clinical, molecular, then treatment data, progressively improved C-index from 0.69 to 0.80 (p < 0.001). Interpretability analyses confirmed established prognostic factors and indicate the potential of GlioSurv to support personalized survival prediction and risk-stratified decision-making in diffuse glioma.