Higher Prevalence and Age Susceptibility of Intracranial Aneurysm in Patients With Acoustic Neuroma

You, Hua; Bai, Yinjuan; Yu, Ting; Zeng, Tiefa; Huang, Neng; Mei, Wenzhong; Jiang, Changzhen; Kang, Dezhi; Wu, Xiyue; Chen, Fuxiang

doi:10.3389/fneur.2020.591526

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Age is one of the most essential risk factors in cerebrovascular diseases [15][16] , such as cerebral infarction 17 , transient ischemic attack (TIA) 18 , Intracerebral hemorrhage (ICH) 19 , and intracranial aneurysm 20 . Our study also found age was an independent predict factors for the prognosis of cerebral infarction patients in intensive care units.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Nomogram for Cerebral Infarction Patients in Intensive Care Units: a Retrospective Cohort Study

Yu¹,

Xu²,

Zeng³

et al. 2021

Preprint

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OBJECTIVES: Our study aimed to establish a utility risk prediction model for the prognosis of patients with cerebral infarction.BACKGROUND: Despite large number of studies focus on the prognosis risk factors of patients with cerebral infarction, there were still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with cerebral infarction.METHODS: The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of cerebral infarction patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. RESULTS: Overall, 1,564 patients (1315 survivals and 249 deaths) with cerebral infarction included in our research from MIMIC-IV database. The incident of in-hospital mortality is 15.9%. Lasso regression model verified that age, white blood cell count, anion gap (AG), SOFA score were significantly correlated with hospital mortality. The risk prediction model demonstrated a good discrimination with an AUC of ROC 0.789 (95% CI 0.752–0.826) in training set and 0.829 (95% CI 0.791–0.867) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that this model has good clinical benefits.Conclusion: We developed a nomogram that predicts hospital mortality in patients with cerebral infarction according to the real world’s data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Prognostic Nomogram for Cerebral Infarction Patients in Intensive Care Units: a Retrospective Cohort Study

Yu¹,

Xu²,

Zeng³

et al. 2021

Preprint

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To clip or to coil for unruptured intracranial aneurysm?

Huang¹,

Yang²,

Qin³

et al. 2021

Medicine

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Introduction: Microsurgical clipping and endovascular coiling are the main methods against unruptured intracranial aneurysm (UIA). The craniotomy of surgical clipping may increase the risk of cerebrospinal fluid leakage and infection, damage the brain tissue, produce excessive stimulation to the nerves and blood vessels around the aneurysm, and cause the corresponding neurological deficit. Endovascular coiling could significantly reduce the mortality and disability rate than surgical clipping technique, which made endovascular coiling to become the first choice for the treatment of UIA. However, the long-term results showed attenuated favorable outcomes of coiling over clipping, so it is still in debate whether to clip or to coil. Therefore, we try to conduct a randomized, controlled, prospective trial to assess the long term safety of endovascular coiling therapy against UIA compared with microsurgical clipping technique. Methods: Parallel-group randomization (1:1) is generated through the random number generator in Microsoft Excel 2010. In this trial, blinding to patients, physicians, and outcome assessors is not possible. Endovascular coiling or surgical clipping will be performed once for each patient in treatment group or control group, respectively. The mRS, overall mortality rate, disability rate, morbidity rate, and occurrence of a major aneurysm recurrence measured at 6 month and 1 year will be recorded. Conclusions: The findings will be helpful for the choice of endovascular coiling or surgical clipping by assessing the long term efficacy and safety of both operations against UIA. Trial registration: OSF Registration number: DOI 10.17605/OSF.IO/QYE9F.

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Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis

et al. 2023

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BackgroundIntracranial aneurysm (IA) is a nodular protrusion of the arterial wall caused by the localized abnormal enlargement of the lumen of a brain artery, which is the primary cause of subarachnoid hemorrhage. Accurate rupture risk prediction can effectively aid treatment planning, but conventional rupture risk estimation based on clinical information is subjective and time-consuming.MethodsWe propose a novel classification method based on the CTA images for differentiating aneurysms that are prone to rupture. The main contribution of this study is that the learning-based method proposed in this study leverages deep learning and radiomics features and integrates clinical information for a more accurate prediction of the risk of rupture. Specifically, we first extracted the provided aneurysm regions from the CTA images as 3D patches with the lesions located at their centers. Then, we employed an encoder using a 3D convolutional neural network (CNN) to extract complex latent features automatically. These features were then combined with radiomics features and clinical information. We further applied the LASSO regression method to find optimal features that are highly relevant to the rupture risk information, which is fed into a support vector machine (SVM) for final rupture risk prediction.ResultsThe experimental results demonstrate that our classification method can achieve accuracy and AUC scores of 89.78% and 89.09%, respectively, outperforming all the alternative methods.DiscussionOur study indicates that the incorporation of CNN and radiomics analysis can improve the prediction performance, and the selected optimal feature set can provide essential biomarkers for the determination of rupture risk, which is also of great clinical importance for individualized treatment planning and patient care of IA.

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Higher Prevalence and Age Susceptibility of Intracranial Aneurysm in Patients With Acoustic Neuroma

Cited by 3 publications

References 27 publications

Development and Validation of a Prognostic Nomogram for Cerebral Infarction Patients in Intensive Care Units: a Retrospective Cohort Study

Development and Validation of a Prognostic Nomogram for Cerebral Infarction Patients in Intensive Care Units: a Retrospective Cohort Study

To clip or to coil for unruptured intracranial aneurysm?

Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis

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