标题： DSA-NRP：基于血管造影灌注动力学的卒中 EVT 无复流预测 显示英文标题

标题： DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT

摘要： 在通过血管内血栓切除术（EVT）成功实现大血管再通后，一些患者会出现一种称为无复流的并发症，其特征是持续的微血管低灌注，这会损害组织恢复并加重临床结局。 尽管及时识别至关重要，但标准临床实践依赖于术后24小时内进行的灌注磁共振成像（MRI），这会延迟干预。 在本研究中，我们引入了首个机器学习（ML）框架，通过利用之前未探索的术中数字减影血管造影（DSA）序列和临床变量，在EVT后立即预测无复流。 我们的回顾性分析包括在UCLA医学中心（2011-2024年）接受治疗的急性缺血性卒中（AIS）患者，这些患者获得了良好的mTICI评分（2b-3）并进行了术前和术后MRI。 无复流被定义为术后影像上持续的低灌注（Tmax > 6秒）。 从DSA序列（正位和侧位视图）中，我们从目标下游区域提取统计和时间灌注特征，以训练ML分类器来预测无复流。 我们的新方法显著优于临床特征基线（AUC：0.7703 $\pm$ 0.12 vs. 0.5728 $\pm$ 0.12；准确率：0.8125 $\pm$ 0.10 vs. 0.6331 $\pm$ 0.09），证明实时DSA灌注动态包含了关于微血管完整性的关键信息。 这种方法为立即、准确的无复流预测奠定了基础，使临床医生能够在不依赖延迟影像的情况下主动管理高风险患者。 显示英文摘要

摘要： Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes. Although prompt identification is crucial, standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention. In this work, we introduce the first-ever machine learning (ML) framework to predict no-reflow immediately after EVT by leveraging previously unexplored intra-procedural digital subtraction angiography (DSA) sequences and clinical variables. Our retrospective analysis included AIS patients treated at UCLA Medical Center (2011-2024) who achieved favorable mTICI scores (2b-3) and underwent pre- and post-procedure MRI. No-reflow was defined as persistent hypoperfusion (Tmax > 6 s) on post-procedural imaging. From DSA sequences (AP and lateral views), we extracted statistical and temporal perfusion features from the target downstream territory to train ML classifiers for predicting no-reflow. Our novel method significantly outperformed a clinical-features baseline(AUC: 0.7703 $\pm$ 0.12 vs. 0.5728 $\pm$ 0.12; accuracy: 0.8125 $\pm$ 0.10 vs. 0.6331 $\pm$ 0.09), demonstrating that real-time DSA perfusion dynamics encode critical insights into microvascular integrity. This approach establishes a foundation for immediate, accurate no-reflow prediction, enabling clinicians to proactively manage high-risk patients without reliance on delayed imaging.