Impact of Multiphase Computed Tomography Angiography for Endovascular Treatment Decision-Making on Outcomes in Patients with Acute Ischemic Stroke

Two-stage convolutional neural network for segmentation and detection of carotid web on CT angiography

BACKGROUND

Carotid web (CaW) is a risk factor for ischemic stroke, mainly in young patients with stroke of undetermined etiology. Its detection is challenging, especially among non-experienced physicians.

METHODS

We included patients with CaW from six international trials and registries of patients with acute ischemic stroke. Identification and manual segmentations of CaW were performed by three trained radiologists. We designed a two-stage segmentation strategy based on a convolutional neural network (CNN). At the first stage, the two carotid arteries were segmented using a U-shaped CNN. At the second stage, the segmentation of the CaW was first confined to the vicinity of the carotid arteries. Then, the carotid bifurcation region was localized by the proposed carotid bifurcation localization algorithm followed by another U-shaped CNN. A volume threshold based on the derived CaW manual segmentation statistics was then used to determine whether or not CaW was present.

RESULTS

We included 58 patients (median (IQR) age 59 (50-75) years, 60% women). The Dice similarity coefficient and 95th percentile Hausdorff distance between manually segmented CaW and the algorithm segmented CaW were 63.20±19.03% and 1.19±0.9 mm, respectively. Using a volume threshold of 5 mm3, binary classification detection metrics for CaW on a single artery were as follows: accuracy: 92.2% (95% CI 87.93% to 96.55%), precision: 94.83% (95% CI 88.68% to 100.00%), sensitivity: 90.16% (95% CI 82.16% to 96.97%), specificity: 94.55% (95% CI 88.0% to 100.0%), F1 measure: 0.9244 (95% CI 0.8679 to 0.9692), area under the curve: 0.9235 (95%CI 0.8726 to 0.9688).

CONCLUSIONS

The proposed two-stage method enables reliable segmentation and detection of CaW from head and neck CT angiography.

CT perfusion stroke lesion threshold calibration between deconvolution algorithms

CTP is an important diagnostic tool in managing patients with acute ischemic stroke, but challenges persist in the agreement of stroke lesion volumes and ischemic core-penumbra mismatch profiles determined with different CTP post-processing software. We investigated a systematic method of calibrating CTP stroke lesion thresholds between deconvolution algorithms using a digital perfusion phantom to improve inter-software agreement of mismatch profiles. Deconvolution-estimated cerebral blood flow (CBF) and Tmax was compared to the phantom ground truth via linear regression for one model-independent and two model-based deconvolution algorithms. Using the clinical standard of model-independent CBF < 30% and Tmax > 6 s as reference thresholds for ischemic core and penumbra, respectively, we determined that model-based CBF < 15% and Tmax > 6 s were the corresponding calibrated thresholds after accounting for quantitative differences revealed at linear regression. Calibrated thresholds were then validated in 63 patients with large vessel stroke by evaluating agreement (concordance and Cohen's kappa, κ) between the two model-based and model-independent deconvolution methods in determining mismatch profiles used for clinical decision-making. Both model-based deconvolution methods achieved 95% concordance with model-independent assessment and Cohen's kappa was excellent (κ = 0.87; 95% confidence interval [CI] 0.72-1.00 and κ = 0.86; 95% CI 0.70-1.00). Our systematic method of calibrating CTP stroke lesion thresholds may help harmonize mismatch profiles determined by different software.

Automated advanced imaging in acute ischemic stroke. Certainties and uncertainties

The purpose of this is study was to review pearls and pitfalls of advanced imaging, such as computed tomography perfusion and diffusion-weighed imaging and perfusion-weighted imaging in the selection of acute ischemic stroke (AIS) patients suitable for endovascular treatment (EVT) in the late time window (6-24 h from symptom onset). Advanced imaging can quantify infarct core and ischemic penumbra using specific threshold values and provides optimal selection parameters, collectively called target mismatch. More precisely, target mismatch criteria consist of core volume and/or penumbra volume and mismatch ratio (the ratio between total hypoperfusion and core volumes) with precise cut-off values. The parameters of target mismatch are automatically calculated with dedicated software packages that allow a quick and standardized interpretation of advanced imaging. However, this approach has several limitations leading to a misclassification of core and penumbra volumes. In fact, automatic software platforms are affected by technical artifacts and are not interchangeable due to a remarkable vendor-dependent variability, resulting in different estimate of target mismatch parameters. In addition, advanced imaging is not completely accurate in detecting infarct core, that can be under- or overestimated. Finally, the selection of candidates for EVT remains currently suboptimal due to the high rates of futile reperfusion and overselection caused by the use of very stringent inclusion criteria. For these reasons, some investigators recently proposed to replace advanced with conventional imaging in the selection for EVT, after the demonstration that non-contrast CT ASPECTS and computed tomography angiography collateral evaluation are not inferior to advanced images in predicting outcome in AIS patients treated with EVT. However, other authors confirmed that CTP and PWI/DWI postprocessed images are superior to conventional imaging in establishing the eligibility of patients for EVT. Therefore, the routine application of automatic assessment of advanced imaging remains a matter of debate. Recent findings suggest that the combination of conventional and advanced imaging might improving our selection criteria.

Recent developments in pre-hospital and in-hospital triage for endovascular stroke treatment

Triage describes the assignment of resources based on where they can be best used, are most needed, or are most likely to achieve success. Triage is of particular importance in time-critical conditions such as acute ischemic stroke. In this setting, one of the goals of triage is to minimize the delay to endovascular thrombectomy (EVT), without delaying intravenous thrombolysis or other time-critical treatments including patients who cannot benefit from EVT. EVT triage is highly context-specific, and depends on availability of financial resources, staff resources, local infrastructure, and geography. Furthermore, the EVT triage landscape is constantly changing, as EVT indications evolve and new neuroimaging methods, EVT technologies, and adjunctive medical treatments are developed and refined. This review provides an overview of recent developments in EVT triage at both the pre-hospital and in-hospital stages. We discuss pre-hospital large vessel occlusion detection tools, transport paradigms, in-hospital workflows, acute stroke neuroimaging protocols, and angiography suite workflows. The most important factor in EVT triage, however, is teamwork. Irrespective of any new technology, EVT triage will only reach optimal performance if all team members, including paramedics, nurses, technologists, emergency physicians, neurologists, radiologists, neurosurgeons, and anesthesiologists, are involved and engaged. Thus, building sustainable relationships through continuous efforts and hands-on training forms an integral part in ensuring rapid and efficient EVT triage.

Angiographic collateral venous phase: a novel landmark for leptomeningeal collaterals evaluation in acute ischemic stroke

BACKGROUND

Although recanalization rates constantly increase (>80%), a favorable clinical outcome is achieved in only 45-55% of patients undergoing mechanical thrombectomy (MT) for anterior circulation stroke. Collateral circulation seems to play a major role in determining this discrepancy. The aim of the study was to investigate a novel angiographic landmark assessing the collateral venous phase (CVP) compared with the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) score, based on the arterial collateral assessment.

METHODS

Two hundred patients with anterior circulation stroke treated by MT between 2016 and 2021 were included. The ASITN/SIR score and the presence of CVP were blindly evaluated by expert neuroradiologists. Three subanalyses were performed comparing patients with good versus poor collaterals, CVP presence versus absence, and a composite analysis including both ASITN/SIR and CVP grading results.

RESULTS

Good collateral circulation (ASITN >2) was observed in 113 patients (56.5%) whereas CVP was present in 90 patients (45%) and mostly in patients with good collaterals. Favorable clinical and neuroradiological outcomes were more likely observed in patients with both good collaterals and the presence of CVP than in those with good collaterals and absence of CVP (modified Rankin Scale score 0-2: 77.3% vs 7.9%, p<0.0001; mortality: 9.3% vs 26.3%, p=0.02; 24-hour Alberta Stroke Program Early CT Score: 8 vs 6, p<0.0001), while ASITN/SIR score alone was not significantly associated with clinical outcomes.

CONCLUSIONS

The presence of CVP improves the angiographic assessment of collateral circulation. CVP could be proposed as a new imaging landmark to better understand the functionality of collaterals.

Pivotal role of multiphase computed tomography angiography for collateral assessment in patients with acute ischemic stroke

The cerebral collateral circulation is the main compensatory mechanism that maintains the ischemic penumbra viable, the tissue at risk for infarction that can be saved if blood flow is restored by reperfusion therapies. In clinical practice, the extent of collateral vessels recruited after vessel occlusion can be easily assessed with computed tomography angiography (CTA) using two different techniques: single-phase CTA (sCTA) and multi-phase CTA (mCTA). Both these methodologies have demonstrated a high prognostic predictive value for prognosis due to the strong association between the presence of good collaterals and favorable radiological and clinical outcomes in patients with acute ischemic stroke (AIS). However, mCTA seems to be superior to sCTA in the evaluation of collaterals and a promising tool for identifying AIS patients who can benefit from reperfusion therapies. In particular, it has recently been proposed the use of mCTA eligibility criteria has been recently proposed for the selection of AIS patients suitable for endovascular treatment instead of the current accepted criteria based on CT perfusion. In this review, we analyzed the characteristics, advantages and disadvantages of sCTA and mCTA to better understand their fields of application and the potential of mCTA in becoming the method of choice to assess collateral extent in AIS patients.

Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model

BACKGROUND AND PURPOSE

Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in patients with stroke.

MATERIALS AND METHODS

A total of 499 patients with large-vessel occlusion from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial were included. All patients had thin-section NCCT and CTA images. Thrombi contoured manually were used as reference standard. A deep learning approach was developed to segment thrombi automatically. Of 499 patients, 263 and 66 patients were randomly selected to train and validate the deep learning model, respectively; the remaining 170 patients were independently used for testing. The deep learning model was quantitatively compared with the reference standard using the Dice coefficient and volumetric error. The proposed deep learning model was externally tested on 83 patients with and without large-vessel occlusion from another independent trial.

RESULTS

The developed deep learning approach obtained a Dice coefficient of 70.7% (interquartile range, 58.0%-77.8%) in the internal cohort. The predicted thrombi length and volume were correlated with those of expert-contoured thrombi (r = 0.88 and 0.87, respectively; P < .001). When the derived deep learning model was applied to the external data set, the model obtained similar results in patients with large-vessel occlusion regarding the Dice coefficient (66.8%; interquartile range, 58.5%-74.6%), thrombus length (r = 0.73), and volume (r = 0.80). The model also obtained a sensitivity of 94.12% (32/34) and a specificity of 97.96% (48/49) in classifying large-vessel occlusion versus non-large-vessel occlusion.

CONCLUSIONS

The proposed deep learning method can reliably detect and measure thrombi on NCCT and CTA in patients with acute ischemic stroke.

Large Vessel Occlusion Sites Affect Agreement Between Outputs of Three Computed Tomography Perfusion Software Packages

OBJECTIVES

Computed tomography perfusion (CTP) data are important for hyperacute stroke decision making. Available comparisons between outputs of different CTP software packages show variable outcomes. Evaluation for factors associated with agreement between the volume estimates is limited. We assessed for differences in core and penumbra volume estimates of three CTP software packages - AutoMIStar, RAPID, and Vitrea - and analyzed factors associated with agreement between the volume estimates.

MATERIALS AND METHODS

Differences between software estimates of penumbra and core volumes were calculated for each patient with suspected acute ischemic stroke who underwent CTP. Exploratory hierarchical clustering and principal component analysis were performed to identify factors of decreased volume estimate agreement. Two-sample t-tests were performed, stratified by large vessel occlusion (LVO) location.

RESULTS

579 CTP studies were performed; 267 were normal, 139 artifacts, with 172 included in the final analysis. 79/172 had LVO of internal carotid artery (ICA, n = 20), M1 (n = 38) and proximal M2 (n = 21). LVO was the only factor associated with decreased software package agreement, and proximal LVO location was associated with general trend of increasing mean differences and standard deviations between software packages (range of mean differences [SD]: non-LVO, -17-6 [4-33] ml; M2, -40-13 [5-39] ml; M1, -43-26 [16-58] ml; ICA, -76-39 [22-97] ml).

CONCLUSIONS

Core and penumbra volume estimates can be affected by LVO location significantly between CTP software packages.