RT Journal Article
SR Electronic
T1 Prediction of bleb formation in intracranial aneurysms using machine learning models based on aneurysm hemodynamics, geometry, location, and patient population
JF Journal of NeuroInterventional Surgery
JO J NeuroIntervent Surg
FD BMJ Publishing Group Ltd.
SP 1002
OP 1007
DO 10.1136/neurintsurg-2021-017976
VO 14
IS 10
A1 Seyedeh Fatemeh Salimi Ashkezari
A1 Fernando Mut
A1 Martin Slawski
A1 Boyle Cheng
A1 Alexander K Yu
A1 Tim G White
A1 Henry H Woo
A1 Matthew J Koch
A1 Sepideh Amin-Hanjani
A1 Fady T Charbel
A1 Behnam Rezai Jahromi
A1 Mika Niemelä
A1 Timo Koivisto
A1 Juhana Frosen
A1 Yasutaka Tobe
A1 Spandan Maiti
A1 Anne M Robertson
A1 Juan R Cebral
YR 2022
UL http://jnis.bmj.com/content/14/10/1002.abstract
AB 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.Data are available upon reasonable request. The data that support the findings of this study are available from the corresponding author, upon reasonable request.