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Case series
Predictive score for complete occlusion of intracranial aneurysms treated by flow-diverter stents using machine learning


Background Complete occlusion of an intracranial aneurysm (IA) after the deployment of a flow-diverter stent is currently unpredictable. The aim of this study was to develop a predictive occlusion score based on pretreatment clinical and angiographic criteria.

Methods Consecutive patients with ≥6 months follow-up were included from 2008 to 2019 and retrospectively analyzed. Each IA was evaluated using the Raymond–Roy occlusion classification (RROC) and dichotomized as occluded (A) or residual (B/C); 80% of patients were randomly assigned to the training sample. Feature selection and binary outcome prediction relied on logistic regression and threshold maximizing class separation selected by a CART tree algorithm. The feature selection was addressed by a genetic algorithm selected from the 30 pretreatment available variables.

Results The study included 146 patients with 154 IAs. Feature selection yielded a combination of six variables with a good cross-validated accuracy on the test sample, a combination we labeled DIANES score (IA diameter, indication, parent artery diameter ratio, neck ratio, side-branch artery, and sex). A score of more than −6 maximized the ability to predict RROC=A with sensitivity of 87% (95% CI 79% to 95%) and specificity of 82% (95% CI 64% to 96%) in the training sample. Accuracy was 86% (95% CI 79% to 94%). In the test sample, sensitivity and specificity were 89% (95% CI 77% to 98%) and 60% (95% CI 33% to 86%), respectively. Accuracy was 81% (95% CI 69% to 91%).

Conclusion A score was developed as a grading scale for prediction of the final occlusion status of IAs treated with a flow-diverter stent.

  • Aneurysm
  • Angiography
  • Device
  • Flow Diverter
  • Stent

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