Abstract WP11: Semi-automated Quantitative Assessment of Reperfusion in Acute Ischemic Stroke Patients

Abstract

Introduction: Accurate assessment of brain reperfusion during endovascular treatment helps determining whether adjuvant intervention is necessary. The gold standard for reperfusion scales are the modified and extended Treatment in Cerebral Ischemia (mTICI and eTICI) scale. However, these scales are coarse and highly dependent on the observer, which may result in imprecise rating of reperfusion status. In this study, we developed a semi-automated quantitative reperfusion measure (qTICI) that was based on eTICI methodology using deep learning. Methods: Digital subtraction angiography (DSA) images of 67 acute ischemic stroke patients with proximal occlusions were collected from the MRCLEAN registry, including only patients who were eTICI graded by an independent core lab and had follow-up 90 days-modified Rankin Scale (mRS) scores. Both DSA projections, anteroposterior and lateral, were used. We trained a patch-based multi-path convolutional neural network to segment the perfusion area. The ground truths of perfusion segmentation were constructed using heuristic, intensity thresholding after vessel removal, which were inspected for their accuracy by a neuroradiologist. The target downstream territory was manually segmented based on the treated occlusion location by two neurointerventionalists. qTICI was formulated as a ratio between the area of perfusion and target downstream territory. We compared performance between qTICI and eTICI in relation with functional outcome, dichotomized as favorable (mRS 0-2) and unfavorable outcome (mRS 3-6). Results: Our study showed that the multi-path convolutional neural network was able to reproduce the ground truth perfusion segmentation with an average Dice score of 0.91. qTICI correlated significantly with eTICI (p<0.001) and the ordinal 90-days mRS (p<0.05). qTICI had comparable discriminative power to distinguish between favorable and unfavorable functional outcome (AUC = 0.80) with eTICI (AUC = 0.81). Conclusions: qTICI provided a quantitative and semi-automated perfusion assessment with strong association with functional outcome.

Costs and health effects of CT perfusion-based selection for endovascular treatment of patients with a large vessel occlusion presenting within six hours after symptom onset A model-based health economic evaluation

“…10 An OR for EVT effect modification due to ICV (OR:0.98 [95%CI:0.881-1.091] per 10 mL) and MMR (OR:1.010[95%CI:0.994;1.026]) per one point change) were used for baseline simulations. Since these ORs were based on CTPs from trials published between 2015 and 2017 with limited brain coverage, 10,25 the ORs and 95%CIs for effect modification were altered by -0.1 and -0.05 for ICV, and +0.1 and +0.05 for MMR to consider technological improvements in CTP software. Finally, we simulated the most favorable CTP-based EVT patient selection PSA scenario considering patients aged ≥80 years, a 0.3 decrease of the EVT effect OR (OR:1.37[95%CI:0.91-2.0]), and a 0.1 decrease of the ICV or MMR based EVT effect modification (OR:0.88[95%CI:0.781-0.991]).…”

“…The regression analyses for EVT effect modification due to CTP estimated ICV and MMR by Campbell et al were repeated as the initial work only reported p-values (full analyses in Online supplement D). coverage,10,25 the ORs and 95%CIs for effect modification were altered by -0.1 and -0.05 for ICV, and +0.1 and +0.05 for MMR to consider technological improvements in CTP software. Finally, we simulated the…”