CT Angiography in Occlusion Assessment of Intracranial Aneurysms Treated with the WEB Device

Imaging for Treated Aneurysms (Including Clipping, Coiling, Stents, Flow Diverters)

Intracranial aneurysms are common in the adult population and carry a risk of rupture leading to catastrophic subarachnoid hemorrhage. Treatment of aneurysms has evolved significantly, with the introduction of new techniques and devices for minimally invasive and endovascular approaches. Follow-up imaging after aneurysm treatment is standard of care to monitor for recurrence or other complications, and the preferred imaging modality and schedule for follow-up are areas of active research. The modality and follow-up schedule should be tailored to treatment technique, aneurysm characteristics, and patient factors.

Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network

Intracranial aneurysm (IA) is an enormous threat to human health, which often results in nontraumatic subarachnoid hemorrhage or dismal prognosis. Diagnosing IAs on commonly used computed tomographic angiography (CTA) examinations remains laborious and time consuming, leading to error-prone results in clinical practice, especially for small targets. In this study, we propose a fully automatic deep-learning model for IA segmentation that can be applied to CTA images. Our model, called Global Localization-based IA Network (GLIA-Net), can incorporate the global localization prior and generates the fine-grain three-dimensional segmentation. GLIA-Net is trained and evaluated on a big internal dataset (1,338 scans from six institutions) and two external datasets. Evaluations show that our model exhibits good tolerance to different settings and achieves superior performance to other models. A clinical experiment further demonstrates the clinical utility of our technique, which helps radiologists in the diagnosis of IAs.

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GLIA-Net is a deep learning method for the clinical diagnosis of IAs

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It can be applied directly to CTA images without any laborious preprocessing

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A clinical study demonstrates its effectiveness in assisting diagnosis

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An IA dataset of 1,338 CTA cases from six institutions is publicly released

Intracranial aneurysms (IAs) are enormous threats to human health with a prevalence of approximately 4%. The rupture of IAs usually causes death or severe damage to the patients. To enhance the clinical diagnosis of IAs, we present a deep learning model (GLIA-Net) for IA detection and segmentation without laborious human intervention, which achieves superior diagnostic performance validated by quantitative evaluations as well as a sophisticated clinical study. We anticipate that the publicly released data and the artificial intelligence technique would help to transform the clinical diagnostics and precision treatments of cerebrovascular diseases. They may also revolutionize the landscape of healthcare and biomedical research in the future.

Residual Flow Inside the Woven EndoBridge Device at Follow-Up: Potential Predictors of the Bicêtre Occlusion Scale Score 1 Phenomenon

BACKGROUND AND PURPOSE

The Woven EndoBridge (WEB) device is an established technique for the treatment of intracranial aneurysms. Occasionally, persistent opacification inside the WEB lumen can be observed at follow-up (previously described as Bicêtre Occlusion Scale Score 1). We evaluated potential risk factors of this phenomenon, hypothesizing that initial deviation of the WEB device from the aneurysm axis, size of the aneurysmal neck surface, or inappropriate WEB sizing correlates with Bicêtre Occlusion Scale Score 1 findings.

MATERIALS AND METHODS

We systematically reviewed all patients treated with the WEB device between February 2014 and December 2018 in our neurointerventional center. Patients with midterm follow-up DSA available were considered for aneurysm evaluation applying the Bicêtre Occlusion Scale Score. WEB angle deviation from the aneurysm axis, neck widths, and WEB sizes were collected.

RESULTS

We included 65 patients with 67 intracranial aneurysms. Eleven of 67 (16.4%) intracranial aneurysms showed the Bicêtre Occlusion Scale Score 1 phenomenon at follow-up. Anterior-posterior projections of WEB axis deviation (angles measured in degrees) were significantly different between the Bicêtre Occlusion Scale Score 1 cohort (median ± interquartile range, 17 ± 17) and all other Bicêtre Occlusion Scale Scores (median ± interquartile range, 7 ± 11; P = .023), whereas in lateral projections, no significant difference was observed (median ± interquartile range, 10 ± 10 versus 8 ± 9; P = .169). Neck or aneurysm recurrence, but not the Bicêtre Occlusion Scale Score 1 phenomenon, occurred significantly more often in patients with inappropriate WEB sizing compared with appropriate WEB sizing (median ± interquartile range, 1 ± 1.3 versus 0 ± 0; P < .001/P = .664).

CONCLUSIONS

The Bicêtre Occlusion Scale Score 1 phenomenon is associated with an initial deviation of the WEB device from the aneurysm axis but does not correlate with aneurysmal neck surface measurements or WEB sizing.