Abstract 153: Pre‐procedural 3D visualization of deep‐learning vascular segmentation enhances identification of difficult femoral access in thrombectomy - Pere Canals; Manuel Requena; Álvaro García‐Tornel; Marc Molina; Alejandro Tomasello; Jiahui Li; Simone Balocco; Oliver Díaz; Marc Ribó (Stroke: Vascular and Interventional Neurology)
Introduction Difficult or impossible femoral access remains a significant burden for mechanical thrombectomy (MT) in stroke, affecting between 4.4% (impossible) to ∼10% of cases (time to first angiography [T1A] > 30 min) [1]. Visualization of CT angiography (CTA) or MR angiography are the only available diagnostic tools for neurointerventionalists (NIs) to qualitatively assess catheterization difficulty ahead of intervention. Visualization of 3D segmentation of the head‐and‐neck arteries from CTA may provide enhanced spatial information for catheter access difficulty assessment to the occlusion site, as well as large vessel occlusion (LVO) localization or carotid stenosis detection. Methods Three observers (2 expert NIs and 1 medical image engineer) assessed a sample of patients with anterior circulation stroke rating LVO location, femoral access difficulty according to a Likert scale (0 to 5) and assessment of radial access compared to femoral (easier or not). Raters were asked to answer the same form for each case twice, once after visualizing only the CTA and again after visualizing a deep‐learning, automated 3D segmentation of the head‐and‐neck arteries from CTA [2]. Cases were randomly sampled and blind to raters for both assessments. Likert scale values were normalized and averaged across observers, and correlation to T1A was studied. LVO localization accuracy and time needed for the analysis was also assessed. Results The final sample included N = 117 cases, where 22.98% presented difficult or impossible access. LVO location prevalence was 13/15/45/27% for extracranial ICA (eICA)/TICA/M1/M2, and 49/51% for right/left. Averaged Likert values presented significant linear correlation for both segmentation and CTA visualization, but this was much stronger in the former (R: 0.58 vs 0.30, p<0.001). Three‐dimensional visualization of segmented vessels allowed for an enhanced detection of difficult or impossible femoral access (ROC‐AUC: 0.85 vs 0.66). LVO detection was also superior after segmentation visualization (mean accuracy across observers: 0.81 vs 0.72) and time needed for assessment was on average 2.3 times shorter (44 s vs 100 s). In difficult or impossible patients, radial access was considered easier in 74% (63‐89%) of cases when visualizing the segmentation compared to 42% (35‐48%) when inspecting the CTA. Conclusion Visualization of deep‐learning 3D arterial segmentation resulted in enhanced difficult or impossible femoral access for MT, LVO detection and faster analysis over traditional pre‐procedural CTA inspection. Adding this visualization to the current workflow could enhance difficult access detection ahead of intervention.