24小时MRI比入院时MRI更能准确预测中风患者3个月恢复情况。
Comparing Baseline and Day-1 Diffusion MRI Using Multimodal Deep Embeddings for Stroke Outcome Prediction
- 用深度嵌入融合MRI与临床数据，提升预测精度。
- 24小时MRI模型AUC达0.923，优于入院时的0.86。
- 加入病灶体积特征后模型更稳定、可解释性更强。
本研究比较了急性缺血性中风（AIS）患者入院时（J0）和24小时后（J1）的弥散加权MRI对三个月功能预后的预测能力。共分析74例有配对表观弥散系数（ADC）扫描和临床数据的AIS患者。采用三维ResNet-50提取影像嵌入，与结构化临床变量融合，经主成分分析压缩至≤12个分量，再用线性支持向量机进行八折分层交叉验证分类。结果显示，基于J1的多模态模型表现最佳（AUC = 0.923 ± 0.085），优于基于J0的配置（AUC ≤ 0.86）。引入病灶体积特征后，模型稳定性与可解释性进一步提升。结果表明，治疗后早期弥散MRI比治疗前影像具有更高预后价值，结合影像、临床及病灶体积特征可构建稳健且可解释的AIS患者三月功能预后预测框架。
原文摘要 · Abstract (English)
This study compares baseline (J0) and 24-hour (J1) diffusion magnetic resonance imaging (MRI) for predicting three-month functional outcomes after acute ischemic stroke (AIS). Seventy-four AIS patients with paired apparent diffusion coefficient (ADC) scans and clinical data were analyzed. Three-dimensional ResNet-50 embeddings were fused with structured clinical variables, reduced via principal component analysis (<=12 components), and classified using linear support vector machines with eight-fold stratified group cross-validation. J1 multimodal models achieved the highest predictive performance (AUC = 0.923 +/- 0.085), outperforming J0-based configurations (AUC <= 0.86). Incorporating lesion-volume features further improved model stability and interpretability. These findings demonstrate that early post-treatment diffusion MRI provides superior prognostic value to pre-treatment imaging and that combining MRI, clinical, and lesion-volume features produces a robust and interpretable framework for predicting three-month functional outcomes in AIS patients.
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