Comparison of Computed Tomography Perfusion and Multiphase Computed Tomography Angiogram in Predicting Clinical Outcomes in Endovascular Thrombectomy

Automated assessment of collateral circulation and infarct core: predictors of functional outcomes in acute ischemic stroke following endovascular thrombectomy

PURPOSE

This study aimed to evaluate the predictive value of automatically assessed collateral circulation (CC) and infarct core for functional outcome in acute ischemic stroke (AIS) patients treated with endovascular thrombectomy (EVT).

METHODS

We conducted a retrospective cohort study of 208 patients with anterior large vessel occlusion treated with EVT. Two AI-powered software were used to automatically assess CC and infarct core. Comparative analyses included patient demographics, clinical and imaging data, and functional outcome. Univariate and multivariable logistic regression analyses were conducted to predict the 90-day functional outcome. A favorable outcome was defined as a modified Rankin scale (mRS) score ≤ 2.

RESULTS

Among the 208 patients, 114 (54.8%) were women and 94 were men, with a mean age of 71.4 ± 13.3 years. Patients with higher collateral score (CS) exhibited lower infarct core volumes (p < 0.001) and better mRS score at 90 days (p = 0.008). Among patients with a favorable outcome, the mean infarct core volume was lower compared to those with poor outcomes (5 mL vs. 8.6 mL, p = 0.003). In univariate logistic regression, both infarct core (OR 0.94, p = 0.005) and CS (OR 1.84, p = 0.014) were predictors of favorable outcome. However, in multivariable models, only infarct core remained a significant independent predictor [AORs of 0.95 (p = 0.021) and 0.96 (p = 0.039)].

CONCLUSION

Automatically assessed infarct core is a robust predictor of functional outcome in AIS patients post-EVT, while CS's predictive value diminishes when adjusted for infarct core. These findings support the integration of AI-powered evaluations in clinical settings to improve prognosis and treatment strategies for AIS.

Simplifying venous outflow: Prolonged venous transit as a novel qualitative marker correlating with acute stroke outcomes

BACKGROUND

Prolonged venous transit (PVT), defined as presence of time-to-maximum ≥ 10 s within the superior sagittal sinus (SSS) and/or torcula, is a novel, qualitatively assessed computed tomography perfusion surrogate parameter of venous outflow with potential utility in pretreatment acute ischemic stroke imaging for neuroprognostication. We aim to characterize the correlation between PVT and neurological functional outcomes in thrombectomy-treated patients.

METHODS

A prospectively-collected database of large vessel occlusion acute ischemic stroke patients treated with thrombectomy was retrospectively analyzed. Spearman's rank correlation coefficient and point-biserial correlations were performed between PVT status (i.e., no region, either SSS or torcula, or both), 90-day modified Rankin score (mRS), mortality (mRS 6), and poor functional outcome (mRS 4-6 vs 0-3).

RESULTS

Of 128 patients, correlation between PVT and 90-day mRS (ρ = 0.35, p < 0.0001), mortality (r = 0.26, p = 0.002), and poor functional outcome (r = 0.27, p = 0.002) were significant.

CONCLUSION

There is a modest, significant correlation between PVT and severity of neurological functional outcome. Consequently, PVT is an easily-ascertained, qualitative metric that may be useful as an adjunct for anticipating a patient's clinical course. Future analyses will determine the significance of incorporating PVT in clinical decision-making.

Importance of computed tomography perfusion on assessing collateral circulation and prognosis of patients with acute anterior circulation large vessel occlusion after endovascular therapy

This study probed the importance of computed tomography perfusion (CTP) on assessing collateral circulation and prognosis in patients with acute anterior circulation large vessel occlusion (AAC-LVO) after endovascular therapy (EVT). Retrospective analysis was performed on the case data of 124 AAC-LVO patients who achieved EVT in the First People's Hospital of Lianyungang. All patients received computed tomography (CT) examination. Based on the multi-phase computed tomography angiography (mCTA) score, patients were separated into poor collateral circulation group and good collateral circulation group. Based on modified Rankin scale (mRS) score, patients were separated into good prognosis group and poor prognosis group. The receiver operating characteristic (ROC) curve was used to measure the efficacy of CTP parameters in predicting good collateral circulation or good prognosis. Correlation between CTP parameters with mCTA collateral and 90-day mRS circulation score was analyzed using the Spearman correlation analysis. The age and admission national Institutes of Health stroke scale (NIHSS) scores of the good collateral circulation group were lower than the poor collateral circulation group, and low perfusion area volume with Tmax > 6 s (VTmax>6 s), infarct core area volume (VCBF<30 %)and hypoperfusion intensity ratio (HIR) were also lower. The mCTA collateral cycle score was negatively related to VTmax>6s, VCBF<30 % and HIR. The area under the curve (AUC) values of VTmax>6s and VCBF<30 % and HIR for predicting good collateral circulation were 0.763, 0.884 and 0.842, respectively, which suggested that perfusion parameters VTmax>6s, VCBF<30 % and HIR could effectively indicate the status of patients' collateral circulation. Relative to the poor prognosis group, patients in the good prognosis group possessed lower admission NIHSS score, younger age, smaller final infarct volume, lower HIR, VCBF<30 %, VTmax>6 s, Alberta Stroke Program Early CT(ASPECT) score, and higher mCTA score. Spearman correlation analysis unveiled that ASPECT score, mCTA score and 90-day mRS were negatively correlated. The final infarct volume, perfusion parameters HIR and VCBF<30 % were positively correlated with 90-day mRS. ROC analysis showed that all variates had good prognostic value for acute anterior circulation great vessel occlusion patients, while VCBF<30 % and HIR had high diagnostic value for prognosis. To sum up, CTP can provide a comprehensive imaging assessment of the collateral circulation of patients with AAC-LVO and has a higher predictive value for the prognosis assessment of patients with EVT in terms of VCBF<30 %, HIR score and mCTA collateral circulation score.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study

BACKGROUND AND PURPOSE

We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models.

METHODS

Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single-center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0-2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented.

RESULTS

A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety-two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome.

CONCLUSIONS

Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals.

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.

Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke

BACKGROUND

Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA.

METHODS

This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA.

RESULTS

A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7.

CONCLUSIONS

This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.