A nomogram to predict rupture risk of middle cerebral artery aneurysm

Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques

The treatment of intracranial aneurysms is dictated by its risk of rupture in the future. Several clinical and radiological risk factors for aneurysm rupture have been described and incorporated into prediction models. Despite the recent technological advancements in aneurysm imaging, linear length and visible irregularity with a bleb are the only radiological measure used in clinical prediction models. The purpose of this article is to summarize both the standard imaging techniques, including their limitations, and the advanced techniques being used experimentally to image aneurysms. It is expected that as our understanding of advanced techniques improves, and their ability to predict clinical events is demonstrated, they become an increasingly routine part of aneurysm assessment. It is important that neurovascular specialists understand the spectrum of imaging techniques available.

Integrate prediction of machine learning for single ACoA rupture risk: a multicenter retrospective analysis

Background

Statistically, Anterior communicating aneurysm (ACoA) accounts for 30 to 35% of intracranial aneurysms. ACoA, once ruptured, will have an acute onset and cause severe neurological dysfunction and even death. Therefore, clinical analysis of risk factors related to ACoA and the establishment of prediction model are the benefits to the primary prevention of ACoA.

Methods

Among 1,436 cases of single ACoA patients, we screened 1,325 valid cases, classified risk factors of 1,124 cases in the ruptured group and 201 cases in the unruptured group, and assessed the risk factors, respectively, and predicted the risk of single ACoA rupture by using the logistic regression and the machine learning.

Results

In the ruptured group (84.8%) of 1,124 cases and the unruptured group (15.2%) of 201 cases, the multivariable logistic regression (MLR) model shows hemorrhagic stroke history (OR 95%CI, p:0.233 (0.120-0.454),<0.001) and the age stratification of 60-69 years (OR 95%CI, p:0.425 (0.271-0.668),<0.001) has a significant statistic difference. In the RandomForest (RF) model, hemorrhagic stroke history and age are the best predictive factors.

Conclusion

We combined the analysis of MLR, RF, and PCA models to conclude that hemorrhagic stroke history and gender affect single ACoA rupture. The RF model with web dynamic nomogram, allows for real-time personalized analysis based on different patients' conditions, which is a tremendous advantage for the primary prevention of single ACoA rupture.

Clinical trial registration

https://www.chictr.org.cn/showproj.html?proj=178501.

A nomogram to predict the risk of bleeding after discharge from stent-assisted ruptured aneurysm embolization in a Chinese population

The occurrence of bleeding events after stent-assisted embolization of a ruptured artery requiring continuous double antiplatelet therapy may seriously affect the prognosis of this group of patients. A nomogram can provide a personalized, more accurate risk estimate based on predictors. We, therefore, developed a nomogram to predict the probability of bleeding events in patients with stent-assisted ruptured aneurysm embolization. We performed a single-center retrospective analysis of data collected from patients undergoing stent-assisted ruptured aneurysm embolization between January 2018 and December 2021. Forward stepwise logistic regression was performed to identify independent predictors of adverse events of bleeding after stent-assisted embolization and to establish nomograms. Discrimination and calibration of this model were performed using the area under the ROC curve (AUC-ROC) and the calibration plot. The model is internally validated by using resampling (1000 replicates). A total of 131 patients were identified, and a total of 118 patients met the study criteria. The predictors included in the nomogram were body mass index (BMI), AAi, and MA-ADP. The model showed good resolving power with a ROC area of 0.893 (95% CI: 0.834 ~ 0.952) for this model with good calibration. The nomogram can be used to individualize, visualize, and accurately predict the risk probability of bleeding events after stent-assisted embolization of ruptured aneurysms.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling

Objective

Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features.

Methods

From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression.

Results

A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms.

Conclusion

Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

Combination of Morphological and Hemodynamic Parameters for Assessing the Rupture Risk of Intracranial Aneurysms: a Retrospective Study On Mirror Middle Cerebral Artery Aneurysms

Discordant findings were frequently reported by studies dedicated to exploring the association of morphological/hemodynamic factors with the rupture of intracranial aneurysms (IAs), probably owing to insufficient control of confounding factors. In this study, we aimed to minimize the influences of confounding factors by focusing IAs of interest on mirror aneurysms and, meanwhile, modeling IAs together with the cerebral arterial network to improve the physiological fidelity of hemodynamic simulation. 52 mirror aneurysms located at the middle cerebral artery (MCA) in 26 patients were retrospectively investigated. Numerical tests performed on two randomly selected patients demonstrated that over truncation of cerebral arteries proximal to the MCA during image-based model reconstruction led to uncertain changes in computed values of intra-aneurysmal hemodynamic parameters, which justified the minimal truncation strategy adopted in our study. Five morphological parameters (i.e., volume (V), height (H), dome area (DA), non-sphericity index (NSI), and size ratio (SR)) and two hemodynamic parameters (i.e., peak WSS (peakWSS), and pressure loss coefficient (PLc)) were found to differ significantly between the ruptured and unruptured aneurysms and proved by receiver operating characteristic (ROC) analysis to have potential value for differentiating the rupture status of aneurysm with the areas under curve (AUCs) ranging from 0.681 to 0.763. Integrating V, SR, peakWSS and PLc or some of them into regression models considerably improved the classification of aneurysms, elevating AUC up to 0.864, which indicates that morphological and hemodynamic parameters have complementary roles in assessing the risk of aneurysm rupture.

The Analysis of Morphoradiological Parameters in Predicting Risk of Basilar Artery Tip Aneurysm Rupture: A Retrospective Cohort Study

Purpose

Basilar artery tip aneurysms can have fatal consequences and poor prognostic outcomes in case of rupture. We investigated the standard parameters used to evaluate aneurysmal morphology to predict aneurysm rupture.

Patients and Methods

We measured the differences between ruptured and unruptured basilar bifurcation aneurysms in terms of morphological features, including aneurysm size, size ratio, aneurysm height, perpendicular height, aneurysm width, bottleneck ratio, aspect ratio, and present daughter domes, using 3D angiography.

Results

Among 34 patients, 21 (61.8%) and 13 (38.2%) had ruptured and unruptured basilar tip aneurysms, respectively. In the ruptured group, the mean aneurysm size, and aspect, size, and bottleneck ratios were 6.8 ± 1.9 mm, 3.2 ± 1.0, 2.3 ± 0.5, and 2.2 ± 0.4, respectively, compared to 4.5 ± 1.5 mm, 4.4 ± 2.0, 1.6 ± 0.4, and 2.7 ± 0.7, respectively, in the non-ruptured group. Univariate analysis showed a larger aneurysmal cross-sectional diameter (odds ratio [OR], 0.50; 95% confidence interval [CI], 0.5-5.392; p < 0.05), a larger size ratio (OR, 21.375; 95% CI, 3.283-139.177; p < 0.05), and presence of a daughter dome (OR, 72.0; 95% CI, 6.7-776.5; p < 0.05) with ruptured basilar artery tip aneurysms.

Conclusion

A larger aneurysm size and size ratio, and the presence of a daughter dome were significantly associated with increased rupture risk in basilar tip aneurysms.