Cerebral cavernous malformation in a patient with pontine hemorrhage: A case study

Management of Symptomatic Hemorrhage From a Developmental Venous Anomaly

Developmental venous anomalies (DVAs) are clinically benign, low-flow vascular malformations that classically hemorrhage only when associated with a cerebral cavernous malformation. It is very rare for an isolated DVA to hemorrhage. Resection of the DVA is generally contraindicated because of the high risk of venous infarct. We present the case of a large symptomatic hemorrhage stemming from an isolated DVA. The hematoma was evacuated and the hemorrhagic portion of the DVA was resected. This case demonstrates that in rare circumstances, careful resection can successfully treat hemorrhagic DVAs.

Eagle jugular syndrome accompanied by de novo brainstem cavernous malformation: a case-based systematic review

BACKGROUND

Eagle jugular syndrome (EJS), recently identified as a cause of cerebrovascular disease (CVD) due to venous obstruction by an elongated styloid process (SP), is reported here alongside a case of concurrent de novo cerebral cavernous malformation (CCM). This study aims to explore the potential causal relationship between EJS and de novo CCM through a comprehensive literature review.

METHOD

Systematic literature reviews, spanning from 1995 to 2023, focused on EJS cases with definitive signs and symptoms and de novo CCM cases with detailed clinical characteristics. Data on the pathophysiology and clinical manifestations of EJS, as well as potential risk factors preceding de novo CCM, were collected to assess the relationship between the two conditions.

RESULT

Among 14 patients from 11 articles on EJS, the most common presentation was increased intracranial hypertension (IIH), observed in 10 patients (71.4%), followed by dural sinus thrombosis in four patients (28.6%). In contrast, 30 patients from 28 articles were identified with de novo CCM, involving 37 lesions. In these cases, 13 patients developed CCM subsequent to developmental venous anomalies (43%), seven following dural arteriovenous fistula (dAVF) (23%), and two after sinus thrombosis (6%). In a specific case of de novo brainstem CCM, the development of an enlarged condylar emissary vein, indicative of venous congestion due to IJV compression by the elongated SP, was noted before the emergence of CCM.

CONCLUSION

This study underscores that venous congestion, a primary result of symptomatic EJS, might lead to the development of de novo CCM. Thus, EJS could potentially be an indicator of CCM development. Further epidemiological and pathophysiological investigations focusing on venous circulation are necessary to clarify the causal relationship between EJS and CCM.

Developing a deep learning model to predict epilepsy recurrence in patients with focal cortical dysplasia type III

Background

A sizable number of patients with focal cortical dysplasia (FCD) type III-related refractory epilepsy continue to experience seizures postsurgically. Deep learning models can automatically assess complex medical image characteristics and predict prognosis with higher efficiency. This study sought to determine whether T2-weighted fluid attenuated inversion recovery (T2W FLAIR) images could predict prognosis of FCD type III-related refractory epilepsy using a deep learning approach.

Methods

Magnetic resonance imaging (MRI) images of 266 patients with FCD type III diagnosed between 2015 and 2019 were included in this retrospective analysis. A deep learning algorithm utilizing a convolutional neural network (CNN) was trained to classify T2W FLAIR images according to Engel's classification. The preprocessed original image and the region of interest (ROI) outlined by clinicians were input into our neural network separately and then together. Precision, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curves (AUCs) were computed as part of the statistical analyses of the network performance with varied inputs of the network model assessed.

Results

The overall performance met the following metrics when the original image only was input: AUC of 96.22%, sensitivity of 84.47%, and specificity of 97.21%. The metrics were as follows when the ROI only was input: area under the ROC curve of 94.76%, sensitivity of 84.92%, and specificity of 96.24%. For the combined inputs, the metrics were as follows: AUC of 97.17%, sensitivity of 90.86%, and specificity of 96.63%.

Conclusions

Deep learning used with conventional MRI can effectively predict the recurrence conditions of epilepsy. Artificial intelligence may help the design of clinical management and enable more precise and individualized prediction for postsurgical prognosis of FCD type III-related refractory epilepsy.

Focal Cortical Dysplasia Type Ⅲ Related Medically Refractory Epilepsy: MRI Findings and Potential Predictors of Surgery Outcome

This study aims to explore the relationship between neuropathologic and the post-surgical prognosis of focal cortical dysplasia (FCD) typed-Ⅲ-related medically refractory epilepsy. A total of 266 patients with FCD typed-Ⅲ-related medically refractory epilepsy were retrospectively studied. Presurgical clinical data, type of surgery, and postsurgical seizure outcome were analyzed. The minimum post-surgical follow-up was 1 year. A total of 266 patients of FCD type Ⅲ were included in this study and the median follow-up time was 30 months (range, 12~48 months). Age at onset ranged from 1.0 years to 58.0 years, with a median age of 12.5 years. The number of patients under 12 years old was 133 (50%) in patients with FCD type Ⅲ. A history of febrile seizures was present in 42 (15.8%) cases. In the entire postoperative period, 179 (67.3%) patients were seizure-free. Factors with p < 0.15 in univariate analysis, such as age of onset of epilepsy (p = 0.145), duration of epilepsy (p = 0.004), febrile seizures (p = 0.150), being MRI-negative (p = 0.056), seizure type (p = 0.145) and incomplete resection, were included in multivariate analysis. Multivariate analyses revealed that MRI-negative findings of FCD (OR 0.34, 95% CI 0.45-0.81, p = 0.015) and incomplete resection (OR 0.12, 95% CI 0.05-0.29, p < 0.001) are independent predictors of unfavorable seizure outcomes. MRI-negative finding of FCD lesions and incomplete resection were the most important predictive factors for poor seizure outcome in patients with FCD type Ⅲ.