Abstract
Mechanical thrombectomy (MT) is the standard of care for acute ischemic stroke caused by large vessel occlusion (LVO). Yet despite achieving high rates of angiographic reperfusion, nearly half of treated patients do not regain functional independence-a phenomenon termed futile recanalization (FR). This persistent gap between vessel patency and clinical recovery exposes the fundamental limitations of traditional time-based treatment paradigms, which assume a uniform rate of ischemic progression across individuals. The pathophysiology of FR is multifactorial, involving microvascular no-reflow, early arterial reocclusion, collateral circulation failure, and reperfusion-mediated injury. These mechanisms interact in a complex, temporally evolving cascade that cannot be captured by a single imaging or clinical metric. The emerging "tissue clock" framework reframes patient selection from elapsed time to individualized tissue viability, drawing on advanced imaging biomarkers including diffusion-FLAIR mismatch, net water uptake quantification, infarct core-penumbra dynamics, and collateral hemodynamic assessment. The DAWN and DEFUSE 3 trials provided landmark evidence that imaging-guided selection enables safe and effective thrombectomy well beyond conventional time windows, validating the clinical relevance of tissue-based decision-making. In parallel, predictive modeling has evolved from traditional clinical scoring systems toward machine learning-based and multimodal approaches that integrate clinical, imaging, and biological variables for individualized risk stratification. The tissue clock paradigm thus marks a conceptual shift from population-level time thresholds to individualized pathophysiological assessment. By integrating imaging biomarkers, circulating biological indicators, and computational prediction models, clinicians may achieve more accurate outcome prediction and deploy multi-target interventions to mitigate FR. Realizing this vision will require standardized tissue clock quantification protocols, prospective validation of artificial intelligence models across diverse populations, and translational evaluation of combination therapies-ultimately aligning successful recanalization with durable functional recovery.