BACKGROUND AND PURPOSE: Clot perviousness in acute ischemic stroke is a potential CT imaging biomarker for mechanical thrombectomy efficacy. We investigated the association among perviousness, clot cellular composition, and first-pass effect. MATERIALS AND METHODS: In 40 mechanical thrombectomy–treated cases of acute ischemic stroke, we calculated perviousness as the difference in clot density on CT angiography and noncontrast CT. We assessed the proportion of fibrin/platelet aggregates, red blood cells, and white blood cells on clot histopathology. We tested for linear correlation between histologic components and perviousness, differences in components between “high” and “low” pervious clots defined by median perviousness, and differences in perviousness/composition between cases that did and did not achieve a first-pass effect. RESULTS: Perviousness significantly positively and negatively correlated with the percentage of fibrin/platelet aggregates (P = .001) and the percentage of red blood cells (P = .001), respectively. Higher pervious clots had significantly greater fibrin/platelet aggregate content (P = .042). Cases that achieved a first-pass effect (n = 14) had lower perviousness, though not significantly (P = .055). The percentage of red blood cells was significantly higher (P = .028) and the percentage of fibrin/platelet aggregates was significantly lower (P = .016) in cases with a first-pass effect. There was no association between clot density on NCCT and clot composition or first-pass effect. Receiver operating characteristic analysis indicated that clot composition was the best predictor of first-pass effect (area under receiver operating characteristic curve: percentage of fibrin/platelet aggregates = 0.731, percentage of red blood cells = 0.706, perviousness = 0.668). CONCLUSIONS: Clot perviousness on CT is associated with a higher percentage of fibrin/platelet aggregate content. Histologic data and, to a lesser degree, perviousness may have value in predicting first-pass outcome. Imaging metrics that more strongly reflect clot biology than perviousness may be needed to predict a first-pass effect with high accuracy.

BACKGROUND AND PURPOSE: Locally compacting the mesh of a flow diverter by a dynamic push-pull technique can accelerate intracranial aneurysm healing. We asked how this deployment strategy compares with overlapping 2 flow diverters for aneurysmal flow reduction. MATERIALS AND METHODS: Using a high-fidelity virtual stent placement method, we simulated 3 flow-diverter strategies (single noncompacted, 2 overlapped, and single compacted) in 3 aneurysms (fusiform, large saccular, and medium saccular). Computational fluid dynamics analysis provided posttreatment hemodynamic parameters, including time-averaged inflow rate, aneurysm-averaged velocity, wall shear stress, total absolute circulation, and turnover time. We examined the relationship between the achieved degree of compaction and aneurysm orifice area. RESULTS: Flow-diverter compaction resulted in a compaction coverage of 57%, 47%, and 22% over the orifice of the fusiform, large, and medium saccular aneurysm, respectively. Compaction coverage increased linearly with orifice area. In the fusiform aneurysm, the single compacted flow diverter accomplished more aneurysmal flow reduction than the other 2 strategies, as indicated by all 5 hemodynamic parameters. In the 2 saccular aneurysms, the overlapped flow diverters achieved the most flow reduction, followed by the single compacted and the noncompacted flow diverter. CONCLUSIONS: Compacting a single flow diverter can outperform overlapping 2 flow diverters in aneurysmal flow reduction, provided that the compaction produces a mesh denser than 2 overlapped flow diverters and this denser mesh covers a sufficient portion of the aneurysm orifice area, for which we suggest a minimum of 50%. This strategy is most effective for aneurysms with large orifices, especially fusiform aneurysms.

Background Recurrence of intracranial aneurysms after endovascular coiling is a serious clinical concern. Objective We hypothesized that recurrence is associated with aneurysm morphology and flow, as well as the coil intervention and the induced flow modifications. Methods We collected 52 primary-coiling aneurysm cases that were either occluded (n=34) or recurrent (n=18) at >1 year follow-up. We created aneurysm models from pre-coiling digital subtraction angiographic images, calculated aneurysm morphology, simulated pre-coiling hemodynamics, modeled coil deployment, and obtained post-coiling hemodynamics for each case. We performed univariable analysis on 26 morphologic, treatment-specific, and hemodynamic parameters to distinguish between recurrent and occluded groups, and multivariable analysis to identify independently significant parameters associated with recurrence. Univariable analysis was also performed on ruptured and unruptured aneurysm subcohorts separately to investigate if they shared specific significant parameters. Results Recurrence was associated with pre-coiling aneurysm morphologic and flow parameters including larger size (maximum dimension and volume), larger neck (diameter, area, and neck-to-parent-artery ratio), and higher flow momentum and kinetic energy. Recurrence was also associated with lower coil packing (packing density and uncoiled volume), higher post-treatment flow (velocity, momentum, and kinetic energy), lower post-treatment washout time, and higher post-treatment impingement force at the neck. Multivariable analysis identified two aneurysmal characteristics (neck diameter and pre-coiling flow kinetic energy), one coil packing parameter (uncoiled volume), and one post-treatment hemodynamic parameter (flow momentum) that were independently associated with recurrence. In ruptured aneurysms, recurrence was associated with larger neck (diameter and area), whereas in unruptured aneurysms, recurrence was associated with larger size (maximum dimension and volume). In both subcohorts, recurrence was associated with higher post-coiling flow momentum and kinetic energy. Conclusion Recurrence at >1 year after coil treatment is associated with intrinsic aneurysm characteristics, coiling itself, and flow changes induced by coiling. Larger aneurysm size and neck, less coil packing, and higher intra-aneurysmal flow before and after coiling predict recurrence.

Intracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA. Neutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls). The training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort. Our findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

OBJECTIVE: Computed tomography angiography (CTA) is the most widely used imaging modality for intracranial aneurysm (IA) management, yet it remains inferior to digital subtraction angiography (DSA) for IA detection, particularly of small IAs in the cavernous carotid region. The authors evaluated a deep learning pipeline for segmentation of vessels and IAs from CTA using coregistered, segmented DSA images as ground truth. METHODS: Using 50 paired CTA-DSA images, the authors trained (n = 27), validated (n = 3), and tested (n = 20) a deep learning model (3D DeepMedic) for cerebrovasculature segmentation from CTA. A landmark-based coregistration algorithm was used for registration and upsampling of CTA images to paired DSA images. Segmented vessels from the DSA were used as the ground truth. Accuracy of the model for vessel segmentation was evaluated using conventional metrics (dice similarity coefficient [DSC]) and vessel segmentation–specific metrics, like connectivity-area-length (CAL). On the test cases (20 IAs), 3 expert raters attempted to detect and segment IAs. For each rater, the authors recorded the rate of IA detection, and for detected IAs, raters segmented and calculated important IA morphology parameters to quantify the differences in IA segmentation by raters to segmentations by DeepMedic. The agreement between raters, DeepMedic, and ground truth was assessed using Krippendorf’s alpha. RESULTS: In testing, the DeepMedic model yielded a CAL of 0.971 ± 0.007 and a DSC of 0.868 ± 0.008. The model prediction delineated all IAs and resulted in average error rates of < 10% for all IA morphometrics. Conversely, average IA detection accuracy by the raters was 0.653 (undetected IAs were present to a significantly greater degree on the ICA, likely due to those in the cavernous region, and were significantly smaller). Error rates for IA morphometrics in rater-segmented cases were significantly higher than in DeepMedic-segmented cases, particularly for neck (p = 0.003) and surface area (p = 0.04). For IA morphology, agreement between the raters was acceptable for most metrics, except for the undulation index (α = 0.36) and the nonsphericity index (α = 0.69). Agreement between DeepMedic and ground truth was consistently higher compared with that between expert raters and ground truth. CONCLUSIONS: This CTA segmentation network (DeepMedic trained on DSA-segmented vessels) provides a high-fidelity solution for CTA vessel segmentation, particularly for vessels and IAs in the carotid cavernous region.