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

作者：Qiang Yu, Xin Fan, Jinwei Li, Qi Hao, Youquan Ning, Shichao Long, Wenhao Jiang, Fajin Lv, Xianlei Yan, Quan Liu, Xiao‐Quan Xu, Zongqian Wu, Juan Peng, Min Wu · 发表于：npj Digital Medicine · 年份：2025 · DOI：10.1038/s41746-025-02213-w · 被引用次数：1 · 研究领域：Intracerebral and Subarachnoid Hemorrhage Research、Acute Ischemic Stroke Management、Intracranial Aneurysms: Treatment and Complications

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.