CT vascular territory mapping: a novel method to identify large vessel occlusion collateral.
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ABSTRACT: Introduction
This descriptive study explores typical patterns of vascular territory mapping (VTM) in ischaemic stroke patients with proximal vessel occlusion. VTM is a novel process using CT perfusion that can identify the source and extent of collateral blood flow in patients with vessel occlusion. It functions by determining which vessel provides dominant blood flow to a brain voxel.Methods
A total of 167 consecutive patients were analysed from INSPIRE (International Stroke Perfusion Imaging Registry) with their CT perfusion reprocessed through VTM software. We explored the typical territory maps generated by this software relating to common large vessel occlusion location sites (ACA/MCA/PCA).Results/conclusion
In the presence of occlusion, VTM demonstrated a reciprocal increase in collateral vessel territories.
SUBMITTER: Valente M
PROVIDER: S-EPMC9816260 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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CT vascular territory mapping: a novel method to identify large vessel occlusion collateral.
Valente Michael M Bivard Andrew A Cheung Andrew A Manning Nathan W NW Parsons Mark W MW
Neuroradiology 20220811 1
<h4>Introduction</h4>This descriptive study explores typical patterns of vascular territory mapping (VTM) in ischaemic stroke patients with proximal vessel occlusion. VTM is a novel process using CT perfusion that can identify the source and extent of collateral blood flow in patients with vessel occlusion. It functions by determining which vessel provides dominant blood flow to a brain voxel.<h4>Methods</h4>A total of 167 consecutive patients were analysed from INSPIRE (International Stroke Per ...[more]
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Project description:ObjectiveIdentification of acute ischemic stroke with large vessel occlusion (AIS-LVO) etiology is crucial for effective revascularization therapy. As collaterals are pivotal in maintaining cerebral perfusion in intracranial atherosclerotic stenosis (ICAS), we investigated whether multiphase CT angiography (mCTA) collateral score can be a diagnosis marker of ICAS-related LVO.MethodsWe reviewed clinical and imaging data from 92 patients who presented with AIS-LVO and underwent mCTA (57 ICAS-related LVO and 35 embolic LVO). Logistic regression was used to identify ICAS-related LVO. The diagnostic accuracy of the mCTA collateral score for identifying ICAS-related LVO was determined using receiver operating characteristic (ROC) analysis.ResultsCompared with patients with embolic LVO, those with ICAS-related LVO had a high median mCTA collateral score (4 vs. 3; P<0.0001). The multinomial logistic regression analysis revealed a significant increase in the mCTA collateral score (OR: 3.717, 95% CI: 2.009-6.876, P<0.0001) in patients with ICAS-related LVO. ROC analysis revealed that the optimal cutoff point of the mCTA collateral score to diagnosis the ICAS-related LVO was 3.5, the area under the curve (AUC) was 0.817 (95% CI: 0.736-0.899; P<0.0001), sensitivity was 80.7%, and specificity was 74.3%. Further analysis revealed that patients with a 4 to 5 mCTA collateral score exhibited a significantly higher median modified Rankin Scale (mRS) at discharge compared with those with a 0 to 3 score (P=0.0464).ConclusionsThe mCTA collateral score may be associated with ICAS-related LVO and could be beneficial in identifying the etiology of AIS-LVO.
| S-EPMC11864043 | biostudies-literature
Project description:BackgroundComputed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring.Materials and methodsBrain CTA images of 56 patients with anterior LVO were retrospectively processed. Twelve readers of various clinical training, including junior neuroradiologists, senior neuroradiologists, and neurologists graded collateral flow using visual CTA-CS scale in two sessions separated by a washout period. Reference standard was the consensus of three expert readers. Duration of reading time, inter-rater reliability, and statistical comparison of readers' performance metrics were analyzed between the e-CTA assisted and unassisted sessions.Resultse-CTA assistance resulted in significant increase in mean accuracy (58.6% to 67.5%, p = 0.003), mean F1 score (0.574 to 0.676, p = 0.002), mean precision (58.8% to 68%, p = 0.007), and mean recall (58.7% to 69.9%, p = 0.002), especially with slight filling deficit (CTA-CS 2 and 3). Mean reading time was reduced across all readers (103.4 to 59.7 s, p = 0.001), and inter-rater agreement in CTA-CS assessment was increased (Krippendorff's alpha 0.366 to 0.676). Optimized occlusion laterality detection was also noted with mean accuracy (92.9% to 96.8%, p = 0.009).ConclusionAutomated assistance for CTA-CS using e-CTA software provided helpful decision support for readers in terms of improving scoring accuracy and reading efficiency for physicians with a range of experience and training backgrounds and leading to significant improvements in inter-rater agreement.
| S-EPMC11833891 | biostudies-literature
Project description:BackgroundA major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics.MethodsWe extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy.ResultsWe included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort.ConclusionsAutomated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.
| S-EPMC10930945 | biostudies-literature
Project description:IntroductionThe relevance of the brush-sign remained poorly documented in large vessel occlusion (LVO). We aimed to assess the relationship between the brush-sign and collateral status and its potential impact on baseline diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (DWI-ASPECTS) in acute ischemic stroke (AIS) patients eligible to mechanical thrombectomy (MT).MethodsConsecutive patients admitted in the Lyon Stroke Center with anterior circulation AIS due to intracranial internal carotid artery (ICA) and/or M1 or M2 segment of the middle cerebral artery (MCA) occlusion eligible for MT were included. The brush-sign was assessed on T2-gradient-echo MRI. Collateral status was assessed on digital subtraction angiography according to the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) score.ResultsIn this study, 504 patients were included, among which 171 (33.9%) patients had a brush-sign. Patients with a brush-sign more frequently had a poor collateral status [72 (42.1%) vs. 103 (30.9%); p = 0.017]. In univariable analysis, a DWI-ASPECTS < 7 was associated with a brush sign. Following multivariable analysis, the brush-sign no longer affected DWI-ASPECTS < 7 while the latter remained associated with younger age [odds ratio (OR) 0.97, 95% CI.96-0.99], male sex (OR 1.79, 95% CI 1.08-2.99), a higher National Institutes of Health Stroke Scale (NIHSS) score (OR 1.16, 95% CI 1.1-1.21), a poor collateral status (OR 9.35, 95% CI 5.59-16.02), MCA segment (OR 2.54, 95% CI 1.25-5.38), and intracranial ICA (OR 3.01, 95% CI 1.16-8) occlusion.Conclusions and relevanceThe brush-sign may be a marker of poor collateral status but did not independently predict a lower DWI-ASPECTS.Clinical trial registrationClinicalTrials.gov, identifier: NCT04620642.
| S-EPMC8924293 | biostudies-literature
Project description: Not available
| S-EPMC9937036 | biostudies-literature
Project description:Background and purposeWe aim to examine effects of collateral status and post-thrombectomy reperfusion on final infarct distribution and early functional outcome in patients with anterior circulation large vessel occlusion ischemic stroke.MethodsPatients with large vessel occlusion who underwent endovascular intervention were included in this study. All patients had baseline computed tomography angiography and follow-up magnetic resonance imaging. Collateral status was graded according to the criteria proposed by Miteff et al and reperfusion was assessed using the modified Thrombolysis in Cerebral Infarction (mTICI) system. We applied a multivariate voxel-wise general linear model to correlate the distribution of final infarction with collateral status and degree of reperfusion. Early favorable outcome was defined as a discharge modified Rankin Scale score ≤2.ResultsOf the 283 patients included, 129 (46%) had good, 97 (34%) had moderate, and 57 (20%) had poor collateral status. Successful reperfusion (mTICI 2b/3) was achieved in 206 (73%) patients. Poor collateral status was associated with infarction of middle cerebral artery border zones, whereas worse reperfusion (mTICI scores 0-2a) was associated with infarction of middle cerebral artery territory deep white matter tracts and the posterior limb of the internal capsule. In multivariate regression models, both mTICI (P<0.001) and collateral status (P<0.001) were among independent predictors of final infarct volumes. However, mTICI (P<0.001), but not collateral status (P=0.058), predicted favorable outcome at discharge.ConclusionsIn this cohort of patients with large vessel occlusion stroke, both the collateral status and endovascular reperfusion were strongly associated with middle cerebral artery territory final infarct volumes. Our findings suggesting that baseline collateral status predominantly affected middle cerebral artery border zones infarction, whereas higher mTICI preserved deep white matter and internal capsule from infarction; may explain why reperfusion success-but not collateral status-was among the independent predictors of favorable outcome at discharge. Infarction of the lentiform nuclei was observed regardless of collateral status or reperfusion success.
| S-EPMC7484023 | biostudies-literature
Project description:Background and purposeCollateral blood supply is a key determinant of outcome in large-vessel occlusion acute ischemic stroke. Single- and multiphase CTA collateral scoring systems have been described but are subjective and require training. We aimed to test whether the CTP-derived hypoperfusion intensity ratio is associated with CTA collateral status and whether a threshold hypoperfusion intensity ratio exists that predicts poor CTA collaterals.Materials and methodsImaging and clinical data of consecutive patients with large-vessel occlusion acute ischemic stroke were retrospectively reviewed. Single-phase CTA and multiphase CTA scoring were performed by 2 blinded neuroradiologists using the Tan, Maas, and Calgary/Menon methods. CTP was processed using RApid processing of PerfusIon and Diffusion software (RAPID). Hypoperfusion intensity ratio = ratio of brain volume with time-to-maximum >10 seconds over time-to-maximum >6-second volume. Correlation between the hypoperfusion intensity ratio and CTA collateral scores was calculated using the Pearson correlation. The optimal threshold of the hypoperfusion intensity ratio for predicting poor collaterals was determined using receiver operating characteristic curve analysis.ResultsFifty-two patients with large-vessel occlusion acute ischemic stroke were included. Multiphase CTA collateral scoring showed better interrater agreement (κ = 0.813) than single-phase CTA (Tan, κ = 0.587; Maas, κ = 0.273). The hypoperfusion intensity ratio correlated with CTA collateral scores (multiphase CTA: r = -0.55; 95% CI, -0.67 to -0.40; P ≤ .001). The optimal threshold for predicting poor multiphase CTA collateral status was a hypoperfusion intensity ratio of >0.45 (sensitivity = 78%; specificity = 76%; area under the curve = 0.86). Patients with high hypoperfusion intensity ratio/poor collateral status had lower ASPECTS/larger infarcts, higher NIHSS scores, and larger hypoperfused volumes.ConclusionsThe hypoperfusion intensity ratio is associated with CTA collateral status in patients with large-vessel occlusion acute ischemic stroke. The hypoperfusion intensity ratio is an automated and quantitative alternative to CTA collateral scoring methods for both clinical and future stroke trial settings.
| S-EPMC8367626 | biostudies-literature
Project description:Background and purposeAs "time is brain" in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics.MethodsWe automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: "Radiomics", "Radiomics + Treatment" (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), "Clinical + Treatment" (baseline clinical variables and treatment), and "Combined" (radiomics, treatment, and baseline clinical variables).ResultsFor discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction.ConclusionMachine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.
| S-EPMC9108990 | biostudies-literature
Project description:PurposeComputed tomography angiography (CTA) is routinely used to detect large-vessel occlusion (LVO) in patients with suspected acute ischemic stroke; however, visual analysis is time consuming and prone to error. To evaluate solutions to support imaging triage, we tested performance of automated analysis of CTA source images (CTASI) at identifying patients with LVO.MethodsStroke patients with LVO were selected from a prospectively acquired cohort. A control group was selected from consecutive patients with clinically suspected stroke without signs of ischemia on CT perfusion (CTP) or infarct on follow-up. Software-based automated segmentation and Hounsfield unit (HU) measurements were performed on CTASI for all regions of the Alberta Stroke Program Early CT score (ASPECTS). We derived different parameters from raw measurements and analyzed their performance to identify patients with LVO using receiver operating characteristic curve analysis.ResultsThe retrospective analysis included 145 patients, 79 patients with LVO stroke and 66 patients without stroke. The parameters hemispheric asymmetry ratio (AR), ratio between highest and lowest regional AR and M2-territory AR produced area under the curve (AUC) values from 0.95-0.97 (all p < 0.001) for detecting presence of LVO in the total population. Resulting sensitivity (sens)/specificity (spec) defined by the Youden index were 0.87/0.97-0.99. Maximum sens/spec defined by the specificity threshold ≥0.70 were 0.91-0.96/0.77-0.83. Performance in a small number of patients with isolated M2 occlusion was lower (AUC: 0.72-0.85).ConclusionAutomated attenuation measurements on CTASI identify proximal LVO stroke patients with high sensitivity and specificity. This technique can aid in accurate and timely patient selection for thrombectomy, especially in primary stroke centers without CTP capacity.
| S-EPMC8463515 | biostudies-literature
Project description:PurposeTo build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD).MethodsFifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC).ResultsOf the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively.ConclusionThe TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.
| S-EPMC9403315 | biostudies-literature