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<p>Hematoma expansion (HE) is a critical therapeutic target in spontaneous intracerebral hemorrhage (sICH), yet its reliable early identification remains challenging. We developed an automated pipeline for HE prediction using non-contrast computed tomography from 2020 patients across five centers. The modular framework comprised automated segmentation, synthetic data augmentation, and Vision Transformer (ViT)-based classification. High-quality hematoma masks were generated by the full-scale U-Mamba model, identified as the optimal architecture through comprehensive benchmarking. Two augmented training sets were constructed using synthetic HE images from the Diffusion-UKAN model: UKAN-Balanced (HE: NHE = 1:1) and UKAN-Semibalanced (HE: NHE = 1:2). The ViT-1:2 classifier, trained on the UKAN-Semibalanced dataset, achieved a training set AUC of 0.815 and demonstrated robust cross-institutional generalization with external validation AUCs of 0.793 and 0.781 on two independent datasets. These findings suggest that the proposed modular approach provides a promising front-line tool for rapid HE risk stratification in acute care settings, with potentially improving clinical decision-making in sICH management.</p>

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An end-to-end deep learning pipeline for hematoma expansion prediction in spontaneous intracerebral hemorrhage based on non-contrast computed tomography

  • 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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  • Qiang Yu,
  • Xin Fan,
  • Jinwei Li,
  • Qianyu Hao,
  • Youquan Ning,
  • Shichao Long,
  • Wenhao Jiang,
  • Fajin Lv,
  • Xianlei Yan,
  • Quan Liu,
  • Xiaoquan Xu,
  • Zongqian Wu,
  • Juan Peng,
  • Min Wu

摘要

Hematoma expansion (HE) is a critical therapeutic target in spontaneous intracerebral hemorrhage (sICH), yet its reliable early identification remains challenging. We developed an automated pipeline for HE prediction using non-contrast computed tomography from 2020 patients across five centers. The modular framework comprised automated segmentation, synthetic data augmentation, and Vision Transformer (ViT)-based classification. High-quality hematoma masks were generated by the full-scale U-Mamba model, identified as the optimal architecture through comprehensive benchmarking. Two augmented training sets were constructed using synthetic HE images from the Diffusion-UKAN model: UKAN-Balanced (HE: NHE = 1:1) and UKAN-Semibalanced (HE: NHE = 1:2). The ViT-1:2 classifier, trained on the UKAN-Semibalanced dataset, achieved a training set AUC of 0.815 and demonstrated robust cross-institutional generalization with external validation AUCs of 0.793 and 0.781 on two independent datasets. These findings suggest that the proposed modular approach provides a promising front-line tool for rapid HE risk stratification in acute care settings, with potentially improving clinical decision-making in sICH management.