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Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods.

Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction.

Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance.

We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

Visualization of structural details of treatment devices during neurointerventional procedures can be challenging. A new true two‐resolution imaging X‐ray detector system features a 194 µm pixel conventional flat‐panel detector (FPD) mode and a 76 µm pixel high‐resolution high‐definition (Hi‐Def) zoom mode in one detector panel. The Hi‐Def zoom mode was developed for use in interventional procedures requiring superior image quality over a small field of view (FOV). We report successful use of this imaging system during intracranial aneurysm treatment in 1 patient with a Pipeline‐embolization device and 1 patient with a low‐profile visualized intramural support (LVIS Blue) device plus adjunctive coiling.

A guide catheter was advanced from the femoral artery insertion site to the proximity of each lesion using standard FPD mode. Under magnified small FOV Hi‐Def imaging mode, an intermediate catheter and microcatheters were guided to the treatment site, and the PED and LVIS Blue plus coils were deployed. Radiation doses were tracked intraprocedurally.

Critical details, including structural changes in the PED and LVIS Blue and position and movement of the microcatheter tip within the coil mass, were more readily apparent in Hi‐Def mode. Skin‐dose mapping indicated that Hi‐Def mode limited radiation exposure to the smaller FOV of the treatment area.

Visualization of device structures was much improved in the high‐resolution Hi‐Def mode, leading to easier, more controlled deployment of stents and coils than conventional FPD mode.