ABSTRACT
BACKGROUND: In cavernous sinus dural arteriovenous fistulas (CS-DAVF), ophthalmological symptoms are usually the main clinical presentation, caused by abnormal drainage of the superior ophthalmic vein (SOV). Early opacification of the SOV during cerebral angiography inevitably signifies the fistula's shunt point at the confluence of the SOV and CS. We aimed to leverage this anatomical feature to achieve precise embolization, thereby enhancing the embolization success rate and preventing CS-related symptoms and complications resulting from overpacking. METHODS: This single-center, case series study was conducted between May 2017 and September 2023, and included the largest sample of CS-DAVF patients treated via the transfemoral vein-SOV approach. We retrospectively reviewed the data of 32 CS-DAVF patients with inferior petrosal sinus (IPS) occlusion. RESULTS: The study demonstrated an excellent immediate postoperative complete embolization rate (31/32, 97%). Only three patients (3/32, 9%) developed temporary endovascular treatment-related complications. The average operation time was 131.6±61.6 min, with an average of 1.2±1.1 coils and 1.8±1.2 mL Onyx glue used per patient. CS-DAVF-associated ophthalmological symptoms resolved in all patients. We also identified a rare anatomical variation, where 77% of the patients had a facial vein draining into the external jugular vein. CONCLUSIONS: Transfemoral vein-SOV embolization should be considered a crucial alternative approach in CS-DAVF patients with occluded IPS and predominantly SOV drainage. This approach showed an excellent immediate postoperative complete embolization rate and satisfactory long-term outcomes along with clinical safety. We therefore strongly advocate for this 'an eye for an eye' treatment strategy.
ABSTRACT
Aim: This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas. Methods: Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC). Results: Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000). Conclusion: The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial.