Aortic and supra-aortic arterial tortuosity and access technique: Impact on time to device deployment in stroke thrombectomy

Deep learning-based model for difficult transfemoral access prediction compared with human assessment in stroke thrombectomy

BACKGROUND

In mechanical thrombectomy (MT), extracranial vascular tortuosity is among the main determinants of procedure duration and success. Currently, no rapid and reliable method exists to identify the anatomical features precluding fast and stable access to the cervical vessels.

METHODS

A retrospective sample of 513 patients were included in this study. Patients underwent first-line transfemoral MT following anterior circulation large vessel occlusion stroke. Difficult transfemoral access (DTFA) was defined as impossible common carotid catheterization or time from groin puncture to first carotid angiogram >30 min. A machine learning model based on 29 anatomical features automatically extracted from head-and-neck computed tomography angiography (CTA) was developed to predict DTFA. Three experienced raters independently assessed the likelihood of DTFA on a reduced cohort of 116 cases using a Likert scale as benchmark for the model, using preprocedural CTA as well as automatic 3D vascular segmentation separately.

RESULTS

Among the study population, 11.5% of procedures (59/513) presented DTFA. Six different features from the aortic, supra-aortic, and cervical regions were included in the model. Cross-validation resulted in an area under the receiver operating characteristic (AUROC) curve of 0.76 (95% CI 0.75 to 0.76) for DTFA prediction, with high sensitivity for impossible access identification (0.90, 95% CI 0.81 to 0.94). The model outperformed human assessment in the reduced cohort [F1-score (95% CI) by experts with CTA: 0.43 (0.37 to 0.50); experts with 3D segmentation: 0.50 (0.46 to 0.54); and model: 0.70 (0.65 to 0.75)].

CONCLUSIONS

A fully automatic model for DTFA prediction was developed and validated. The presented method improved expert assessment of difficult access prediction in stroke MT. Derived information could be used to guide decisions regarding arterial access for MT.

Age and duration of hypertension are associated with carotid artery tortuosity

Objective

Tortuosity of the carotid artery is a common angiographic finding that may impact blood flow and neuronal function. However, information on its prevalence and risk factors remains limited. In this study, we determined to explore the factors affecting carotid artery tortuosity.

Methods

The head and neck computed tomography angiography (CTA) imaging and cerebral angiography data performed at the Second Affiliated Hospital of Wenzhou Medical University between January 2019 and September 2021 were collected, and a total of 356 cases were enrolled in the study after screening. Carotid artery tortuosity refers to the angle between the two adjacent segments of the carotid artery, from the opening of the aortic arch on either side to the external orifice of the carotid canal, being less than 150°. A retrospective analysis was performed to compare the general information, laboratory indicators, personal history, and medical history between the two groups. The χ2 test, t-test, and Mann-Whitney U-test were performed to compare the parameters between the two groups. If there were significant differences between the groups, multivariate logistic regression was performed to analyze the factors affecting carotid artery tortuosity.

Results

A total of 222 of the 356 cases were determined to have carotid artery tortuosity, accounting for 63.6%. There were statistically significant differences in age, body mass index (BMI), duration of diabetes and hypertension, levels of low-density lipoprotein cholesterol (LDL-C), diastolic blood pressure, history of ischemic and hemorrhagic stroke, and the usage of antihypertensive drugs between the two groups. Multivariate logistic regression analysis of the above factors showed that age (OR = 5.063, 95% CI 2.963-10.26, p < 0.001) and duration of hypertension (OR = 2.356, 95% CI 1.353-8.625, p = 0.021) were associated with a higher incidence of carotid artery tortuosity. Compared to patients who did not consume antihypertensive drugs, the incidence of carotid artery tortuosity was significantly less (OR = 0.094, 95% CI 0.002-0.713, p = 0.019) in those consuming antihypertensive drugs.

Conclusion

Carotid artery tortuosity is a relatively common carotid artery disease. The incidence of carotid artery tortuosity may increase with age and the duration of hypertension. The consumption of antihypertensive drugs may reduce the incidence of carotid artery tortuosity.

Deep Learning-based Assessment of Internal Carotid Artery Anatomy to Predict Difficult Intracranial Access in Endovascular Recanalization of Acute Ischemic Stroke

BACKGROUND

Endovascular thrombectomy (EVT) duration is an important predictor for neurological outcome. Recently it was shown that an angle of ≤ 90° of the internal carotid artery (ICA) is predictive for longer EVT duration. As manual angle measurement is not trivial and time-consuming, deep learning (DL) could help identifying difficult EVT cases in advance.

METHODS

We included 379 CT angiographies (CTA) of patients who underwent EVT between January 2016 and December 2020. Manual segmentation of 121 CTAs was performed for the aortic arch, common carotid artery (CCA) and ICA. These were used to train a nnUNet. The remaining 258 CTAs were segmented using the trained nnUNet with manual verification afterwards. Angles of left and right ICAs were measured resulting in two classes: acute angle ≤ 90° and > 90°. The segmentations together with angle measurements were used to train a convolutional neural network (CNN) determining the ICA angle. The performance was evaluated using Dice scores. The classification was evaluated using AUC and accuracy. Associations of ICA angle and procedural times was explored using median and Whitney‑U test.

RESULTS

Median EVT duration for cases with ICA angle > 90° was 48 min and with ≤ 90° was 64 min (p = 0.001). Segmentation evaluation showed Dice scores of 0.94 for the aorta and 0.86 for CCA/ICA, respectively. Evaluation of ICA angle determination resulted in an AUC of 0.92 and accuracy of 0.85.

CONCLUSION

The association between ICA angle and EVT duration could be verified and a DL-based method for semi-automatic assessment with the potential for full automation was developed. More anatomical features of interest could be examined in a similar fashion.

A fully automatic method for vascular tortuosity feature extraction in the supra-aortic region: unraveling possibilities in stroke treatment planning

Vascular tortuosity of supra-aortic vessels is widely considered one of the main reasons for failure and delays in endovascular treatment of large vessel occlusion in patients with acute ischemic stroke. Characterization of tortuosity is a challenging task due to the lack of objective, robust and effective analysis tools. We present a fully automatic method for arterial segmentation, vessel labelling and tortuosity feature extraction applied to the supra-aortic region. A sample of 566 computed tomography angiography scans from acute ischemic stroke patients (aged 74.8 ± 12.9, 51.0% females) were used for training, validation and testing of a segmentation module based on a U-Net architecture (162 cases) and a vessel labelling module powered by a graph U-Net (566 cases). Successively, 30 cases were processed for testing of a tortuosity feature extraction module. Measurements obtained through automatic processing were compared to manual annotations from two observers for a thorough validation of the method. The proposed feature extraction method presented similar performance to the inter-rater variability observed in the measurement of 33 geometrical and morphological features of the arterial anatomy in the supra-aortic region. This system will contribute to the development of more complex models to advance the treatment of stroke by adding immediate automation, objectivity, repeatability and robustness to the vascular tortuosity characterization of patients.

Aortic Arch Variations and Supra-aortic Arterial Tortuosity in Stroke Patients Undergoing Thrombectomy : Retrospective Analysis of 1705 Cases

PURPOSE

Unfavorable vascular anatomy can impede thrombectomy in patients with acute ischemic stroke. The aim of this study was to determine the prevalence of aortic arch types, aortic arch branching patterns and supra-aortic arterial tortuosity in stroke patients with large vessel occlusion.

METHODS

Computed tomography (CT) and magnetic resonance (MR) images of all stroke patients in an institutional thrombectomy registry were retrospectively reviewed. Aortic arch types and branching patterns of all patients were determined. In patients with anterior circulation stroke, the prevalence of tortuosity (elongation, kinking or coiling) of the supra-aortic arteries of the affected side was additionally assessed.

RESULTS

A total of 1705 aortic arches were evaluated. Frequency of aortic arch types I, II and III were 777 (45.6%), 585 (34.3%) and 340 (19.9%), respectively. In 1232 cases (72.3%), there was a normal branching pattern of the aortic arch. The brachiocephalic trunk and the left common carotid artery had a common origin in 258 cases (15.1%). In 209 cases (12.3%), the left common carotid artery arose from the brachiocephalic trunk. Of 1598 analyzed brachiocephalic trunks and/or common carotid arteries, 844 (52.8%) had no vessel tortuosity, 592 (37.0%) had elongation, 155 (9.7%) had kinking, and 7 (0.4%) had coiling. Of 1311 analyzed internal carotid arteries, 471 (35.9%) had no vessel tortuosity, 589 (44.9%) had elongation, 150 (11.4%) had kinking, and 101 (7.7%) had coiling.

CONCLUSION

With 20%, type III aortic arches are found in a relevant proportion of stroke patients eligible for mechanical thrombectomy. Nearly half of the stroke patients present with supra-aortic arterial tortuosity, mostly arterial elongation.