Morphological risk model assessing anterior communicating artery aneurysm rupture: Development and validation

Chen, Yongchun; Xing, Haixia; Lin, Boli; Zhou, Jiafeng; Ding, Shilei; Wan, Jieqing; Yang, Yunjun; Pan, Yaohua; Zhao, Bing

doi:10.1016/j.clineuro.2020.106158

Cited by 17 publications

(14 citation statements)

References 28 publications

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“…The assessment of the stability of a CA involves many factors, including inflammatory,2 hemodynamic and morphological factors 3 4. Among them, morphological factors are easily measured, calculated and analyzed and are considered to be directly related to CA rupture; thus, combining morphological factors with clinical features can be used to form a basis by which to judge the stability of a CA 5…”

Section: Introductionmentioning

confidence: 99%

“…Many of the morphological features of aneurysms, including the aspect ratio,6 size ratio, height and bottleneck factor, are considered to be correlated with and have been used to predict aneurysm rupture 7. In previous studies, rupture prediction models for anterior communicating aneurysms and middle cerebral artery (MCA) aneurysms based on identified independent morphological features demonstrated areas under the curve (AUCs) between 0.80 and 0.829 5 8. Furthermore, radiomics features automatically extracted by PyRadiomics (version 3.0.1) were used in recent studies to predict CA rupture 9.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of cerebral aneurysm rupture using a point cloud neural network

Luo

Wang

Xiong

et al. 2022

J NeuroIntervent Surg

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ObjectiveAccurate prediction of cerebral aneurysm (CA) rupture is of great significance. We intended to evaluate the accuracy of the point cloud neural network (PC-NN) in predicting CA rupture using MR angiography (MRA) and CT angiography (CTA) data.Methods418 CAs in 411 consecutive patients confirmed by CTA (n=180) or MRA (n=238) in a single hospital were retrospectively analyzed. A PC-NN aneurysm model with/without parent artery involvement was used for CA rupture prediction and compared with ridge regression, support vector machine (SVM) and neural network (NN) models based on radiomics features. Furthermore, the performance of the trained PC-NN and radiomics-based models was prospectively evaluated in 258 CAs of 254 patients from five external centers.ResultsIn the internal test data, the area under the curve (AUC) of the PC-NN model trained with parent artery (AUC=0.913) was significantly higher than that of the PC-NN model trained without parent artery (AUC=0.851; p=0.041) and of the ridge regression (AUC=0.803; p=0.019), SVM (AUC=0.788; p=0.013) and NN (AUC=0.805; p=0.023) radiomics-based models. Additionally, the PC-NN model trained with MRA source data achieved a higher prediction accuracy (AUC=0.936) than that trained with CTA source data (AUC=0.824; p=0.043). In external data of prospective cohort patients, the AUC of PC-NN was 0.835, significantly higher than ridge regression (0.692; p<0.001), SVM (0.701; p<0.001) and NN (0.681; p<0.001) models.ConclusionPC-NNs can achieve more accurate CA rupture prediction than traditional radiomics-based models. Furthermore, the performance of the PC-NN model trained with MRA data was superior to that trained with CTA data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of cerebral aneurysm rupture using a point cloud neural network

Luo

Wang

Xiong

et al. 2022

J NeuroIntervent Surg

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“…A workstation (Version 4.4; GE Medical Systems) was used to reconstruct 3D images and measure aneurysm morphology. Aneurysm morphological parameters, namely, aneurysm height, perpendicular height, aneurysm size, neck size, vessel size, aspect ratio, size ratio, aneurysm angle, flow angle, and vessel angle were described in detail in previous studies ( 15 , 16 ). The aneurysm size was the largest cross-sectional diameter of the aneurysm dome.…”

Section: Methodsmentioning

confidence: 99%

Irregular Pulsation of Intracranial Aneurysm Detected by Four-Dimensional CT Angiography and Associated With Small Aneurysm Rupture: A Single-Center Prospective Analysis

et al. 2022

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ObjectivesPredicting the risk of rupture of small intracranial aneurysms remains challenging. The irregular pulsation of aneurysms detected by four-dimensional CT angiography (4D-CTA) could be an imaging marker of aneurysm vulnerability. We aimed to investigate the association of irregular pulsation with small aneurysm rupture.Materials and MethodsThis was a prospective study on intracranial aneurysms detected by 4D-CTA from October 2017 to January 2020. A total of 242 consecutive patients with 316 aneurysms were enrolled. Irregular pulsation was defined as a temporary focal protuberance on more than 3 consecutive frames of the 20 phases in the RR interval. Small aneurysms were defined as those <7 mm. Univariate and multivariate analyses were performed to determine the independent predictors of small aneurysm rupture.ResultsA total of 169 patients with 217 small intracranial aneurysms were included. Fourteen (6.5%) of the aneurysms had ruptured, and 77 (35.5%) had irregular pulsation. There were no significant differences in age, sex, hypertension, smoking, diabetes, drinking, or hyperlipidemia between the ruptured and unruptured aneurysm groups. The univariate analysis showed that smaller vessel size (p = 0.008), larger size ratio (p = 0.003), larger aspect ratio (p = 0.006), larger flow angle (p = 0.001), large vessel angle (p = 0.004), middle cerebral artery aneurysms (p = 0.046), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysm (p = 0.006), irregular aneurysm (p = 0.001), and t presence of irregular pulsation (p = 0.001) were associated with small aneurysm rupture. The multivariate analysis showed that the presence of irregular pulsation (p = 0.003), anterior cerebral artery/posterior communicating artery/posterior circulation aneurysms (p = 0.014), and larger flow angle (p = 0.006) was independently associated with aneurysm rupture. Multivariate analysis of predictors of the irregular pulsation of small aneurysms showed that the aneurysm rupture (p = 0.022), irregular aneurysm (p < 0.001), and large size ratio (p = 0.005) were independently associated with the presence of irregular pulsation.ConclusionsThe ruptured small aneurysms more often had irregular pulsation. The irregular pulsation was independently associated with aneurysm rupture and may help evaluate the risk of rupture of small intracranial aneurysms.

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“…In the international study of unruptured intracranial aneurysms, it is reported that the size and location of unruptured aneurysms are the most important determinants of rupture [7] . Other subsequent studies have shown that additional parameters such as smaller aneurysm neck diameter, irregular aneurysm shape, and higher aspect ratio, size ratio, flow angle, and current smoking may also affect the rupture of AComA aneurysm [8–11] …”

Section: Introductionmentioning

confidence: 99%

Risk factors for anterior communicating artery aneurysm rupture

et al. 2021

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Background: Although the research on the risk factors of anterior communicating artery (AComA) aneurysm has made great progress, the independent effect of each risk factor on the rupture of AComA aneurysm is controversial among different studies. We will perform a protocol for systematic review and meta-analysis to investigate risk factors for AComA aneurysm rupture and quantify their independent effects. Methods: A systematic search according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guidelines in PubMed, Embase, and the Cochrane Library databases was conducted from inception to August 31, 2021 for published studies concerning risk factors for AComA aneurysm rupture. In the absence of statistical heterogeneity (ie, P > .10 and I 2 < 50%), we will use a fixed-effects model to pool the results across sufficient studies. Otherwise, we will present the results employing the random-effects model. Quality assessment of the included studies will be evaluated using the Newcastle–Ottawa Scale. Statistical analyses will be performed using Stata16 (Stata Corporation, College Station, TX, USA) software. Results: The findings of this study will be submitted to peer-reviewed journals for publication. Conclusion: This systematic review will provide evidence to determine the risk factors that affect the rupture of the AComA aneurysm and quantify their independent effects. Ethics and dissemination: Since the proposed study uses pre-published data, ethical approval is not required. Review registration number: CRD42021284262. ( https://www.crd.york.ac.uk/PROSPERO/ ).

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Morphological risk model assessing anterior communicating artery aneurysm rupture: Development and validation

Cited by 17 publications

References 28 publications

Prediction of cerebral aneurysm rupture using a point cloud neural network

Prediction of cerebral aneurysm rupture using a point cloud neural network

Irregular Pulsation of Intracranial Aneurysm Detected by Four-Dimensional CT Angiography and Associated With Small Aneurysm Rupture: A Single-Center Prospective Analysis

Risk factors for anterior communicating artery aneurysm rupture

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