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

• URL: http://arxiv.org/abs/2506.17501v2
• Date: Wed, 25 Jun 2025 23:15:21 GMT
• Title: DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
• Authors: Shreeram Athreya, Carlos Olivares, Ameera Ismail, Kambiz Nael, William Speier, Corey Arnold,
• Abstract summary: No-reflow, defined by persistent microvascular hypoperfusion, undermines tissue recovery and worsens clinical outcomes.<n>Standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention.<n>We introduce the first-ever machine learning framework to predict no-reflow immediately after endovascular thrombectomy.
• Score: 2.5191548037496805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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