Growth and Rupture Risk of Small Unruptured Intracranial Aneurysms

2017 ◽  
Vol 167 (1) ◽  
pp. 26 ◽  
Author(s):  
Ajay Malhotra ◽  
Xiao Wu ◽  
Howard P. Forman ◽  
Holly K. Grossetta Nardini ◽  
Charles C. Matouk ◽  
...  
Keyword(s):  
Intracranial Aneurysms ◽  
Rupture Risk ◽  
Unruptured Intracranial Aneurysms

scholarly journals Perioperative rupture risk of unruptured intracranial aneurysms in cardiovascular surgery

Brain ◽  
2019 ◽  
Vol 142 (5) ◽  
pp. 1408-1415 ◽  
Author(s):  
Jae-Sik Nam ◽  
Sang-Beom Jeon ◽  
Jun-Young Jo ◽  
Kyoung-Woon Joung ◽  
Ji-Hyun Chin ◽  
...  
Keyword(s):  
Intracranial Aneurysms ◽  
Cardiovascular Surgery ◽  
Rupture Risk ◽  
Unruptured Intracranial Aneurysms

scholarly journals Unruptured intracranial aneurysms and the assessment of rupture risk based on anatomical and morphological factors: sifting through the sands of data

2009 ◽  
Vol 26 (5) ◽  
pp. E2 ◽  
Author(s):  
Rohan R. Lall ◽  
Christopher S. Eddleman ◽  
Bernard R. Bendok ◽  
H. Hunt Batjer
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Intracranial Aneurysms ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Morphological Characteristics ◽  
Preventive Treatment ◽  
Rupture Risk ◽  
Catastrophic Event ◽  
Aneurysmal Rupture ◽  
Unruptured Aneurysms ◽  
Unruptured Intracranial Aneurysms

Aneurysmal subarachnoid hemorrhage continues to have high rates of morbidity and mortality for patients despite optimal medical and surgical management. Due to the fact that aneurysmal rupture can be such a catastrophic event, preventive treatment is desirable for high-risk lesions. Given the variability of the literature evaluating unruptured aneurysms regarding basic patient population, clinical practice, and aneurysm characteristics studied, such as size, location, aspect ratio, relationship to the surrounding vasculature, and the aneurysm hemodynamics, a metaanalysis is nearly impossible to perform. This review will instead focus on the various anatomical and morphological characteristics of aneurysms reported in the literature with an attempt to draw broad inferences and serve to highlight pressing questions for the future in our continued effort to improve clinical management of unruptured intracranial aneurysms.

scholarly journals Wall Enhancement, Hemodynamics, and Morphology in Unruptured Intracranial Aneurysms with High Rupture Risk

2020 ◽  
Vol 11 (5) ◽  
pp. 882-889 ◽  
Author(s):  
Nan Lv ◽  
Christof Karmonik ◽  
Shiyue Chen ◽  
Xinrui Wang ◽  
Yibin Fang ◽  
...  
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Intracranial Aneurysms ◽  
Rupture Risk ◽  
Hemodynamic Pattern ◽  
Unruptured Intracranial Aneurysms ◽  
Wall Shear ◽  
The Relationship

Abstract The purpose of this study is to investigate the relationship between morphology, hemodynamics, and aneurysm wall enhancement (AWE) on vessel wall MRI and their potential role in rupture of intracranial aneurysms. Fifty-seven patients (22 males and 35 females; mean age of 58.4) harboring 65 unruptured intracranial aneurysms were retrospectively recruited. Vessel wall MRI images were reviewed and differentiated as no (NAWE), partial (PAWE), and circumferential (CAWE) wall enhancement. Computational geometry and computational fluid dynamics were used to calculate morphological and hemodynamic parameters. The PHASES score was calculated for each case to estimate its rupture risk. Univariate and multivariate logistic regression analysis was performed to investigate the relationship between morphological-hemodynamic pattern and AWE as well as their association with rupture risk. AWE was present in 26 (40.0%) lesions, including 14 (21.5%) PAWE and 12 (18.5%) CAWE. Aneurysm size (odds ratio = 7.46, 95% confidence interval = 1.56–35.77, p = 0.012), size ratio (odds ratio = 12.90, 95% confidence interval = 2.28–72.97, p = 0.004), and normalized wall shear stress (odds ratio = 0.11, 95% confidence interval = 0.02–0.69, p = 0.018) were independently associated with the presence of AWE. With increasing PHASES score, size-related parameters and the frequency of irregular shape increased significantly, and a hemodynamic pattern of lower and oscillating wall shear stress was observed. Simultaneously, the proportion of NAWE aneurysms decreased, and PAWE and CAWE aneurysms increased significantly (p < 0.001). Unruptured intracranial aneurysms with a higher rupture risk presented with a significantly larger size, lower wall shear stress, and more intense AWE, which might support the interaction between morphology, hemodynamics, and inflammation and their potential role in aneurysm rupture prediction.

scholarly journals Greater Rupture Risk for Familial as Compared to Sporadic Unruptured Intracranial Aneurysms

Stroke ◽  
2009 ◽  
Vol 40 (6) ◽  
pp. 1952-1957 ◽  
Author(s):  
Joseph P. Broderick ◽  
Robert D. Brown ◽  
Laura Sauerbeck ◽  
Richard Hornung ◽  
John Huston ◽  
...  
Keyword(s):  
Intracranial Aneurysms ◽  
Rupture Risk ◽  
Unruptured Intracranial Aneurysms

scholarly journals Multi-View Convolutional Neural Networks in Rupture Risk Assessment of Small, Unruptured Intracranial Aneurysms

2021 ◽  
Vol 11 (4) ◽  
pp. 239
Author(s):  
Jun Hyong Ahn ◽  
Heung Cheol Kim ◽  
Jong Kook Rhim ◽  
Jeong Jin Park ◽  
Dick Sigmund ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Intracranial Aneurysms ◽  
Three Dimensional ◽  
Cerebral Aneurysms ◽  
Rupture Risk ◽  
Single Image ◽  
Anterior Circulation ◽  
Single View ◽  
Unruptured Intracranial Aneurysms ◽  
Sensitivity Specificity

Auto-detection of cerebral aneurysms via convolutional neural network (CNN) is being increasingly reported. However, few studies to date have accurately predicted the risk, but not the diagnosis itself. We developed a multi-view CNN for the prediction of rupture risk involving small unruptured intracranial aneurysms (UIAs) based on three-dimensional (3D) digital subtraction angiography (DSA). The performance of a multi-view CNN-ResNet50 in accurately predicting the rupture risk (high vs. non-high) of UIAs in the anterior circulation measuring less than 7 mm in size was compared with various CNN architectures (AlexNet and VGG16), with similar type but different layers (ResNet101 and ResNet152), and single image-based CNN (single-view ResNet50). The sensitivity, specificity, and overall accuracy of risk prediction were estimated and compared according to CNN architecture. The study included 364 UIAs in training and 93 in test datasets. A multi-view CNN-ResNet50 exhibited a sensitivity of 81.82 (66.76–91.29)%, a specificity of 81.63 (67.50–90.76)%, and an overall accuracy of 81.72 (66.98–90.92)% for risk prediction. AlexNet, VGG16, ResNet101, ResNet152, and single-view CNN-ResNet50 showed similar specificity. However, the sensitivity and overall accuracy were decreased (AlexNet, 63.64% and 76.34%; VGG16, 68.18% and 74.19%; ResNet101, 68.18% and 73.12%; ResNet152, 54.55% and 72.04%; and single-view CNN-ResNet50, 50.00% and 64.52%) compared with multi-view CNN-ResNet50. Regarding F1 score, it was the highest in multi-view CNN-ResNet50 (80.90 (67.29–91.81)%). Our study suggests that multi-view CNN-ResNet50 may be feasible to assess the rupture risk in small-sized UIAs.

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