Detection and Characterization of Intracranial Aneurysms with Dual-Energy Subtraction CTA: Comparison with DSA

The prognostic value of hyperglycemia in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis

Recent studies have demonstrated that hyperglycemia may result in a poor prognosis following aneurysmal subarachnoid hemorrhage (aSAH). However, the association between hyperglycemia and the clinical outcome of aSAH has not been clearly established thus far. Therefore, we performed a systematic review and meta-analysis to investigate the association between hyperglycemia and the development of aSAH. We completed a literature search in four databases (PubMed, EMBASE, Cochrane Library, and Web of Science) up to November 1, 2021, including all eligible studies investigating the prognostic value of hyperglycemia in patients with aSAH. We performed a quality assessment of included studies using the Newcastle-Ottawa Quality Assessment Scale. The pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to assess the association of hyperglycemia in aneurysmal subarachnoid hemorrhage. A total of 35 studies with 11,519 patients were finally included in the meta-analysis. Nineteen studies reported the association between hyperglycemia and poor outcome, 12 studies reported the association between hyperglycemia and all-cause mortality, 7 studies reported the association between hyperglycemia and cerebral vasospasm, and 9 studies reported the association between hyperglycemia and cerebral infarction. The pooled data of these studies suggested that hyperglycemia was significantly associated with poor functional outcomes (odds ratio [OR], 1.29; 95% confidence interval [CI], 1.17-1.42; P < 0.00001; I² = 83%), all-cause mortality (OR, 1.02; 95% CI, 1.01-1.04; P = 0.0006; I² = 89%), cerebral vasospasm (OR, 1.02; 95% CI, 1.01-1.02; P = 0.0002; I² = 35%), and cerebral infarction (OR, 1.16; 95% CI, 1.09-1.23; P < 0.00001; I² = 10%) in aSAH patients. These findings suggested that assessing for hyperglycemia at admission may help clinicians to identify critically ill patients and complete patient stratification early, which may achieve better management and improve the prognosis of patients with aSAH.

Deep learning for automated cerebral aneurysm detection on computed tomography images

PURPOSE

Cerebrovascular aneurysms are being observed with rapidly increasing incidence. Therefore, tools are needed for accurate and efficient detection of aneurysms. We used deep learning techniques with CT angiography acquired from multiple medical centers and different machines to develop and evaluate an automatic detection model.

METHODS

In this study, we have introduced a deep learning model, the faster RCNN model, in order to develop a tool for automatic detection of aneurysms from medical images. The inputs of the model were 2D nearby projection (NP) images from 3D CTA, which were made by the NP method proposed in this study. This method made aneurysms clearly visible on images and improved the model's performance. The study included 311 patients with 352 aneurysms, selected from three hospitals, and 208 and 103 of these patients, respectively, were randomly selected to train and test the models.

RESULTS

The sensitivity of the trained model was 91.8%. For aneurysm sizes larger than 3 mm, the sensitivity of successful aneurysm detection was 96.7%. We achieved state-of-the-art sensitivity for > 3 mm aneurysms. The sensitivities also indicated that there was no significant difference among aneurysms at different locations in the body. Computing time for the detection process was less than 25 s per case.

CONCLUSIONS

We successfully developed a deep learning model that can automatically detect aneurysms. The model performed well for aneurysms of different sizes or in different locations. This finding indicates that the deep learning model has the potential to vastly improve clinician performance by providing automated aneurysm detection.

Angiographic correlation and synergistic effect of coronary artery stenosis and cerebral artery stenosis: a retrospective study

Background

Comorbidity of coronary artery stenosis (CoAS) and cerebral artery stenosis (CeAS) is relatively common, but little is known about their angiographic correlation and synergistic effect.

Material/Methods

A total of 66 patients with CoAS were divided into 2 groups: 30 patients with mild CoAS in group A and 36 patients with severe CoAS in group B. Patients were subdivided further into 4 groups: 20 patients with multiple CeAS in group B1, 16 patients with non-multiple CeAS in group B2, 22 patients with multiple CeAS in group A1, and 8 patients with non-multiple CeAS in group A2. Then, the morbidity rates for myocardial infarction and ischemic stroke before angiography were analyzed.

Results

Overall, the incidence and extent of CoAS were positively related to those of CeAS (p=0.004 and p=0.008, respectively). After stratification, the incidences of stenotic vessels in the intracranial arteries (EA) and carotid artery system (CAS) in group B were significantly higher than those in group A (p=0.011 and p=0.007, respectively). Additionally, the morbidity rates for ischemic stroke in groups B1 and A1 showed a weak trend toward a significant difference (p=0.060).

Conclusions

This study indicates, for the first time, that severe CoAS might be a predictive marker for stenotic vessels of the EA and CAS and for severe CeAS. Furthermore, this study is the first to report that the synergistic effect of CoAS and CeAS might increase the risk of ischemic stroke, which must be confirmed in a large-scale prospective study.