BACKGROUND: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.

Purpose: To explore imaging biomarkers predictive of intratumoral haemorrhage for lesions intended for elective stereotactic biopsy. Method: This study included a retrospective cohort of 143 patients with 175 intracranial lesions intended for stereotactic biopsy. All the lesions were randomly split into a training dataset (n=121) and a test dataset (n=54) at a ratio of 7:3. Thirty-four lesions were defined as “hemorrhage-prone tumors” as haemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1 Weighted Imaging and T2 Weighted Imaging images. Features informative of haemorrhage were then selected by the LASSO algorithm, and an Support Vector Machine model was built with selected features. The Support Vector Machine model was further simplified by discarding features with low importance and calculating them using a “permutation importance” method. The model’s performance was evaluated with confusion matrix-derived metrics and area under curve (AUC) values on the independent test dataset. Results: Nine radiomics features were selected as haemorrhage-related features of intracranial tumours by the LASSO algorithm. The simplified model’s sensitivity, specificity, accuracy, and AUC reached 0.909, 0.930, 0.926, and 0.949 (95% CI: 0.865–1.000) on the test dataset in the discrimination of “hemorrhage-prone tumors”. The permutation method rated feature “T2_gradient_firstorder_10Percentile” as the most important, the absence of which decreased the model’s accuracy by 10.9%. Conclusion: Radiomics features extracted on contrast-enhanced T1 Weighted Imaging and T2 Weighted Imaging sequences were predictive of future haemorrhage of intracranial tumours with favourable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.

Ma C, et al. Korean J Radiol. 2024 Jan;25(1):74-85. https://doi.org/10.3348/kjr.2023.0911

Purposes: A stent-assisted coil (SAC) is a safe and effective treatment modality for some complex intracranial aneurysms, especially for wide neck aneurysms. However, some wide neck aneurysms with a tough angle and located in the posterior communicating artery (PCoA) are challenging to treat with a SAC. This study aimed to examine and discuss the swinging-tail technique for treating wide neck aneurysms located in the PCoA using a SAC by Prof. Lv. Materials and Methods: We retrospectively reviewed our institutional clinical database and identified nine patients with neck aneurysms located in the PCoA, and these patients underwent the swinging-tail technique by Prof. Lv, which is a novel technique of releasing a stent, from June 2016 to September 2021. Results: In this study, nine patients underwent SAC treatment using the swinging-tail technique by Prof. Lv. Aneurysmal complete occlusion was observed in every patient without any complications, as shown by immediate postoperative angiography. Additionally, the modified Rankin scale was monitored for clinical outcomes in the follow-up. One patient died postoperatively due to severe SAH with an intraventricular hemorrhage. Four of nine patients underwent imaging follow-up, demonstrating the complete occlusion of aneurysms; eight patients underwent clinical follow-up and achieved a favorable clinical outcome (modified Rankin scale score: 0–2). Conclusion: The SAC treatment for wide neck aneurysms located in the PCoA can be challenging for operators because of the specific location, resulting in inadequate vessel wall apposition by antegrade stenting via the ipsilateral vessel. In this circumstance, the swinging-tail technique may be a feasible and effective choice.