Unruptured untreated intracranial aneurysms: a retrospective analysis of outcomes of 445 aneurysms managed conservatively

Artificial intelligence applied to development of predictive stability model for intracranial aneurysms

BACKGROUND

We aimed to develop multiple machine learning models to predict the risk of early intracranial aneurysms (IAs) rupture, evaluate and compare the performance of predictive models.

METHODS

Information related to patients diagnosed with IA by CT angiography and clinicians in Central hospital of Dalian University of Technology from January 2010 to June 2022 was collected, including clinical characteristics, blood indicators and IA morphological parameters. IA with rupture or maximum growth ≥ 0.5 mm within 1 month of first diagnosis was considered unstable. The relevant factors affecting IA stability were screened and predictive models were developed based on the above three levels, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). Sensitivity, specificity, accuracy and area under curve (AUC) value were used to evaluate the predictive models.

RESULTS

A total of 989 IA patients were included in the study, including 561 stable patients and 428 unstable patients. For RF models, the training set showed that sensitivity, specificity, accuracy and the AUC values were 72.8-83.7%, 76.9-86.9%, 75.1-84.1% and 0.748 (0.719-0.778)-0.839 (0.814-0.864), respectively; after test set validation, the results were 71.9-78.8%, 75.0-84.0%, 73.6-81.1% and 0.734 (0.688-0.781)-0.809 (0.768-0.850), respectively. For SVM models, the training set were 66.0-80.2%, 76.5-85.5%, 71.7-82.3%, 0.712 (0.682-0.743)-0.913 (0.884-0.924), respectively; the test set were 44.2-78.3%, 63.4-84.4%, 57.9-80.9%, 0.699 (0.651-0.747)-0.806 (0.765-0.848), respectively. For ANN models, the training set were 66.8-83.0%, 75.3-82.3%, 71.6-82.1%, 0.783 (0.757-0.808)-0.897 (0.879-0.914); the test set were 63.1-76.3%, 65.5-84.0%, 64.4-80.6%, 0.680 (0.593-0.694)-0.860 (0.821-0.899). The results of variable importance showed that age, white blood cell count (WBC) and uric acid (UA) played an important role in predicting the stability of IA.

CONCLUSIONS

The predictive stability models of IA based on three artificial intelligence methods shows good clinical application. Age, WBC and UA played an important role in predicting the IA stability, and were potentially important predictors.

Current research status and future of endovascular treatment for basilar artery aneurysms

Aneurysms occurring along the basilar artery (BA) account for <1% of all intracranial aneurysms. Endovascular treatment (EVT) in particular is recommended for large unruptured BA aneurysms and ruptured BA aneurysms. Given that EVT techniques vary, a detailed review of EVT for BA aneurysms is necessary. In this review, the following issues were discussed: the anatomy and anomalies of the BA, the classification of BA aneurysms, the natural history of BA aneurysms, the status of open surgery, the use of EVT for various types of BA aneurysms and the deployment of new devices. According to the findings of this review and based on our experience in treating BA aneurysms, traditional coiling EVT is still the optimal therapy for most BA aneurysms. However, in some BA aneurysms, flow diverter (FD) deployment can be used. In addition, there are also some new devices, such as intrasaccular flow disruptors and stent-like devices that can be used to treat BA aneurysms. In general, EVT can yield good clinical and angiographic outcomes for patients with BA aneurysms. In addition, recent new devices and techniques, such as new-generation FDs generated via surface modification and virtual reality simulation techniques, show promise for EVT for BA aneurysms. These devices and techniques may further improve EVT outcomes for BA aneurysms.

Narrowing of the Parent Artery Angle Is Associated With Intracranial Aneurysm Growth

OBJECTIVE

Although risk factors for intracranial aneurysm growth have been reported, studies investigating the influence of the parent artery angle are limited. In this study, we examined the relationship between intracranial aneurysm growth and parent artery angle narrowing by analyzing long-term follow-up magnetic resonance angiography data.

METHODS

We retrospectively reviewed data of patients with untreated aneurysms and those treated by simple coil embolization, who were followed up by magnetic resonance angiography for over 24 months at the Steel Memorial Yawata Hospital between August 2007 and March 2023. We investigated the relationship of aneurysm growth with parent artery angle narrowing, age, sex, follow-up duration, previous subarachnoid hemorrhage, hypertension, smoking, aneurysm location, aneurysm type, maximum size, and neck size.

RESULTS

A total of 180 aneurysms of 162 patients (women, n=113; untreated, n=136) were included. The median age at aneurysm diagnosis was 71 (63.8-76) years and the median follow-up duration was 69 (45-120) months. Among the 180 aneurysms, 41 (untreated, n=30; treated by simple coil embolization, n=11) showed growth during the follow-up period, with a risk of 4.4%/patient-year. In the univariable analysis, the parent artery angles on the initial and last follow-up images and angle change were significantly associated with aneurysm growth. However, in the multivariable analysis, the association remained significant only for angle change (odds ratio, 2.21; 95% confidence interval, 1.42-3.45). The cutoff value of parent artery angle change for intracranial aneurysm growth was -3.4°.

CONCLUSION

Parent artery angle narrowing was significantly associated with intracranial aneurysm growth. This parameter may be useful for the monitoring of patients with unruptured intracranial aneurysms and may contribute to discerning the mechanism of intracranial aneurysm growth.

Risk factors for the rupture of intracranial aneurysms: a systematic review and meta-analysis

Purpose

The study aimed to identify potential risk factors for aneurysm rupture by performing a systematic review and meta-analysis.

Materials and methods

We systematically searched the PubMed, Embase, and Cochrane Library electronic databases for eligible studies from their inception until June 2023.

Results

Eighteen studies involving 17,069 patients with unruptured intracranial aneurysm (UIA) and 2,699 aneurysm ruptures were selected for the meta-analysis. Hyperlipidemia [odds ratio (OR): 0.47; 95% confidence interval (CI): 0.39-0.56; p < 0.001] and a family history of subarachnoid hemorrhage (SAH) (OR: 0.81; 95% CI: 0.71-0.91; p = 0.001) were associated with a reduced risk of aneurysm rupture. In contrast, a large-size aneurysm (OR: 4.49; 95% CI: 2.46-8.17; p < 0.001), ACA (OR: 3.34; 95% CI: 1.94-5.76; p < 0.001), MCA (OR: 2.16; 95% CI: 1.73-2.69; p < 0.001), and VABA (OR: 2.20; 95% CI: 1.24-3.91; p = 0.007) were associated with an increased risk of aneurysm rupture. Furthermore, the risk of aneurysm rupture was not affected by age, sex, current smoking, hypertension, diabetes mellitus, a history of SAH, and multiple aneurysms.

Conclusion

This study identified the predictors of aneurysm rupture in patients with UIAs, including hyperlipidemia, a family history of SAH, a large-size aneurysm, ACA, MCA, and VABA; patients at high risk for aneurysm rupture should be carefully monitored.

Systematic Review Registration

Our study was registered in the INPLASY platform (INPLASY202360062).