Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population. (22nd October 2021)

• Record Type: Journal Article
• Authors: Salimi Ashkezari, Seyedeh Fatemeh; Mut, Fernando; Slawski, Martin; Cheng, Boyle; Yu, Alexander K; White, Tim G; Woo, Henry H; Koch, Matthew J; Amin-Hanjani, Sepideh; Charbel, Fady T; Rezai Jahromi, Behnam; Niemelä, Mika; Koivisto, Timo; Frosen, Juhana; Tobe, Yasutaka; Maiti, Spandan; Robertson, Anne M; Cebral, Juan R
• Is Part Of: Journal of neurointerventional surgery. Volume 14:Number 10(2022)
• Page Start: 1002
• Page End: 1007
• Publication Date: 2021-10-22
• Subjects: aneurysm -- hemorrhage -- statistics -- blood flow; Nervous system -- Surgery -- Periodicals; Cerebrovascular disease -- Surgery -- Periodicals; 617.48
• DOI: 10.1136/neurintsurg-2021-017976
• Languages: English
• ISSNs: 1759-8478

Abstract

Abstract : Background: Bleb presence in intracranial aneurysms (IAs) is a known indication of instability and vulnerability. Objective: To develop and evaluate predictive models of bleb development in IAs based on hemodynamics, geometry, anatomical location, and patient population. Methods: Cross-sectional data (one time point) of 2395 IAs were used for training bleb formation models using machine learning (random forest, support vector machine, logistic regression, k-nearest neighbor, and bagging). Aneurysm hemodynamics and geometry were characterized using image-based computational fluid dynamics. A separate dataset with 266 aneurysms was used for model evaluation. Model performance was quantified by the area under the receiving operating characteristic curve (AUC), true positive rate (TPR), false positive rate (FPR), precision, and balanced accuracy. Results: The final model retained 18 variables, including hemodynamic, geometrical, location, multiplicity, and morphology parameters, and patient population. Generally, strong and concentrated inflow jets, high speed, complex and unstable flow patterns, and concentrated, oscillatory, and heterogeneous wall shear stress patterns together with larger, more elongated, and more distorted shapes were associated with bleb formation. The best performance on the validation set was achieved by the random forest model (AUC=0.82, TPR=91%, FPR=36%, misclassification error=27%). Conclusions: Based on the premise that aneurysm characteristics prior to bleb formation resemble those derived from vascular reconstructions with their blebs virtually removed, machine learning models can identify aneurysms prone to bleb development with good accuracy. Pending further validation with longitudinal data, these models may prove valuable for assessing the propensity of IAs to progress to vulnerable states and potentially rupturing.