Impact of vessel tortuosity and radiological thrombus characteristics on the choice of first-line thrombectomy strategy: Results from the ESCAPE-NA1 trial

INTRODUCTION

Despite improvements in device technology, only one-third of stroke patients undergoing endovascular thrombectomy (EVT) achieve first-pass effect (FPE). We investigated the effect of arterial tortuosity and thrombus characteristics on the relationship between first-line EVT strategy and angiographic outcomes.

PATIENTS AND METHODS

Patients with thin-slice baseline CT-angiography from the ESCAPE-NA1 trial (Efficacy and safety of nerinetide for the treatment of acute ischemic stroke) were included. Tortuosity was estimated using the tortuosity index extracted from catheter pathway, and radiological thrombus characteristics were length, non-contrast density, perviousness and hyperdense artery sign. We assessed the association of first-line EVT strategy (stent-retriever [SR] versus contact aspiration [CA] versus combined SR+CA) with FPE (eTICI score 2c/3 after one pass), final eTICI 2b/3, number of passes and procedure duration using multivariable regression. Interaction of tortuosity and thrombus characteristics with first-line technique were assessed using interaction terms.

RESULTS

Among 520 included patients, SR as a first-line modality was used in 165 (31.7%) patients, CA in 132 (25.4%), and combined SR+CA in 223 (42.9%). FPE was observed in 166 patients (31.9%). First-line strategy was not associated with FPE. Tortuosity had a significant effect on FPE only in the CA group (aOR = 0.90 [95% CI 0.83-0.98]) compared with stent-retrievers and combined first-line approach (p interaction = 0.03). There was an interaction between thrombus length and first-line strategy for number of passes (p interaction = 0.04). Longer thrombi were associated with higher number of passes only in the CA group (acOR 1.03 [95% CI 1.00-1.06]).

CONCLUSION

Our study suggests that vessel tortuosity and longer thrombi may negatively affect the performance of first-line contact aspiration catheters in acute stroke patients undergoing EVT.

Abstract WP100: Automatic Large Vessel Occlusion Detection On Computed Tomography Angiography Using A 3D Convolutional Neural Network

Purpose: To validate the performance of a 3D convolutional neural network (CNN) based algorithm i.e. Stroke SENS LVO, in automatically detecting the presence of large vessel occlusions (LVO) on computed tomography angiography (CTA) images of the head.

Method: A total of 400 studies (217 LVO, 183 non-LVO) were used in the analysis. The LVO group includes internal carotid artery (ICA) and m1 segment of the middle cerebral artery (M1-MCA) occlusions; and the non-LVO group includes more distal or posterior cerebral artery occlusions, no occlusions, and hemorrhagic stroke cases. Expert consensus reads were used as reference standard. Performance was evaluated using sensitivity and specificity and corresponding 95% confidence intervals (CI). Additional analysis was performed on several subgroups of interest.

Results: For detecting LVO, the algorithm achieved a sensitivity of 0.894 [0.853, 0.935] and specificity of 0.874 [0.826, 0.922]. Furthermore, sensitivities of 0.857 [0.779, 0.935] on ICA cases (N=77) and 0.914 [0.868, 0.961] on M1-MCA cases (N=140) were noted; similarly, specificities of 0.891 [0.833, 0.949] on hemorrhagic stroke cases (N=110) and 0.849 [0.767, 0.931] on non-LVO-non-hemorrhage cases (N=73) were noted. Similar performances were observed across stratified datasets based on age, sex, scanner manufacturer and slice thickness when compared to the full cohort.

Conclusion: Stroke SENS LVO demonstrated high accuracy in automatic detection of LVO on a large heterogeneous dataset.

Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with Acute Ischemic Stroke

Identifying the presence and extent of early ischemic changes (EIC) on Non-Contrast Computed Tomography (NCCT) is key to diagnosing and making time-sensitive treatment decisions in patients that present with Acute Ischemic Stroke (AIS). Segmenting EIC on NCCT is however a challenging task. In this study, we investigated a 3D CNN based on nnU-Net, a self-adapting CNN technique that has become the state-of-the-art in medical image segmentation, for segmenting EIC in NCCT of AIS patients. We trained and tested this model on a sizeable and heterogenous dataset of 534 patients, split into 438 for training and validation and 96 for testing. On this test set, we additionally assessed the inter-rater performance by comparing the proposed approach against two reference segmentation annotations by expert neuroradiologist readers, using this as the benchmark against which to compare our model. In terms of spatial agreement, we report median Dice Similarity Coefficients (DSCs) of 39.8% for the model vs. Reader-1, 39.4% for the model vs. Reader-2, and 55.6% for Reader-2 vs. Reader-1. In terms of lesion volume agreement, we report Intraclass Correlation Coefficients (ICCs) of 83.4% for model vs. Reader-1, 80.4% for model vs. Reader-2, and 94.8% for Reader-2 vs. Reader-1. Based on these results, we conclude that our model performs well relative to expert human performance and therefore may be useful as a decision-aid for clinicians.