X-LMC：基于DSA的跨视图时空侧支循环评分
X-LMC: Cross-View Spatiotemporal Collateral Circulation Scoring from DSA
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中文总结 AI 辅助
该研究提出X-LMC跨视图时空深度学习框架，自动从DSA进行ASITN/SIR侧支评分，在134例M1闭塞患者多中心数据上优于基线，性能接近临床评分者间一致性，为卒中血流表型分析提供可复现基础。
中文摘要 AI 辅助
数字减影血管造影（DSA）是软脑膜侧支（LMC）评估的参考标准，可为指导二级治疗策略、神经康复规划及回顾性卒中研究提供关键预后见解。然而，通过ASITN/SIR量表进行临床LMC分级依赖人工操作，视觉检查的变异性极大。本文提出X-LMC，一种用于从时间分辨双平面DSA中自动进行侧支评分的时空框架。该架构通过DINOv2主干网络编码空间帧表示，通过令牌级跨视图注意力模块融合正交投影，并使用循环网络架构建模对比剂团注动态的表示。我们在包含134例M1段闭塞患者的多中心数据集上评估该框架。在5折交叉验证设置中，X-LMC的点估计值高于静态架构及从相关血管造影任务适配的时空基线，与表现最佳的基线相比，其二次加权卡帕（QWK）为0.398（基线为0.322），二分类宏F1分数为0.711（基线为0.663）。X-LMC的性能也与观察到的临床评分者间一致性（QWK：0.314）相符。作为首个尝试自动进行LMC评分的DSA研究，我们证明多视图时序深度学习可捕获侧支特异性的对比剂动力学。最终，这些基准明确了自动ASITN/SIR分级的临床模糊性与可实现的性能边界，为卒中队列中客观血流表型分析建立了可复现的基础。代码可在指定URL获取。
英文摘要
Digital subtraction angiography (DSA) is the reference standard for leptomeningeal collateral (LMC) assessment, providing critical prognostic insights to guide secondary treatment strategies, neurorehabilitation planning, and retrospective stroke research. However, clinical LMC grading via the ASITN/SIR scale relies on manual, highly variable visual inspection. We introduce X-LMC, a spatiotemporal framework for automated collateral scoring from time-resolved biplane DSA. The proposed architecture encodes spatial frame representations through a DINOv2 backbone, fuses orthogonal projections via a token-level cross-view attention module, and models representations of contrast bolus dynamics using a recurrent network architecture. We evaluate our framework on a multicenter dataset of 134 patients with M1-segment occlusions. In a 5-fold cross-validation setting, X-LMC yields higher point estimates than static architectures and spatiotemporal baselines adapted from related angiographic tasks, achieving a Quadratic Weighted Kappa (QWK) of 0.398 (vs. 0.322) and a dichotomized macro-F1 score of 0.711 (vs. 0.663) against the best-performing baseline. X-LMC performance also aligns with the observed clinical inter-rater agreement (QWK: 0.314). As the first DSA study attempting to automate LMC scoring, we demonstrate that multi-view temporal deep learning can capture collateral-specific contrast kinetics. Ultimately, these benchmarks delineate the clinical ambiguities and achievable performance boundaries of automated ASITN/SIR grading, establishing a reproducible foundation for objective hemodynamic phenotyping in stroke cohorts. Code is available at https://github.com/maedehafezi/X-LMC.
发表机构
- Friedrich-Alexander-Universität, Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
- University Hospital Zurich（苏黎世大学医院）
- University of Zurich（苏黎世大学）
机构由 AI 辅助整理，请以论文原文为准。
