RESUMEN
BACKGROUND AND PURPOSE: Treatment strategies in acute ischemic stroke aim to curtail ischemic progression. Emerging paradigms propose patient subselection using imaging biomarkers derived from CT, CTA, and CT perfusion. We evaluated the performance of a fully-automated computational tool, hypothesizing enhancements compared with qualitative approaches. The correlation between imaging variables and clinical outcomes in a cohort of patients with acute ischemic stroke is reported. MATERIALS AND METHODS: Sixty-two patients with acute ischemic stroke and MCA or ICA occlusion undergoing multidetector CT, CTA, and CTP were retrospectively evaluated. CTP was processed on a fully operator-independent platform (RApid processing of PerfusIon and Diffusion [RAPID]) computing automated core estimates based on relative cerebral blood flow and relative cerebral blood volume and hypoperfused tissue volumes at varying thresholds of time-to-maximum. Qualitative analysis was assigned by 2 independent reviewers for each variable, including CT-ASPECTS, CBV-ASPECTS, CBF-ASPECTS, CTA collateral score, and CTA clot burden score. Performance as predictors of favorable clinical outcome and final infarct volume was established for each variable. RESULTS: Both RAPID core estimates, CT-ASPECTS, CBV-ASPECTS, and clot burden score correlated with favorable clinical outcome (P < .05); CBF-ASPECTS and collateral score were not significantly associated with favorable outcome, while hypoperfusion estimates were variably associated, depending on the selected time-to-maximum thresholds. Receiver operating characteristic analysis demonstrated disparities among tested variables, with RAPID core and hypoperfusion estimates outperforming all qualitative approaches (area under the curve, relative CBV = 0.86, relative CBF = 0.81; P < .001). CONCLUSIONS: Qualitative approaches to acute ischemic stroke imaging are subject to limitations due to their subjective nature and lack of physiologic information. These findings support the benefits of high-speed automated analysis, outperforming conventional methodologies while limiting delays in clinical management.