Fluid dynamic analysis in predicting the recanalization of intracranial aneurysms after coil embolization - A study of spatiotemporal characteristics

Jing Liao; Kouichi Misaki; Tekehiro Uno; Iku Nambu; Tomoya Kamide; Zhuoqing Chen; Mitsutoshi Nakada; Jiro Sakamoto

doi:10.1016/j.heliyon.2023.e22801

Fluid dynamic analysis in predicting the recanalization of intracranial aneurysms after coil embolization - A study of spatiotemporal characteristics

Heliyon. 2023 Dec 20;10(1):e22801. doi: 10.1016/j.heliyon.2023.e22801. eCollection 2024 Jan 15.

Authors

Jing Liao¹, Kouichi Misaki², Tekehiro Uno², Iku Nambu², Tomoya Kamide², Zhuoqing Chen³, Mitsutoshi Nakada², Jiro Sakamoto⁴

Affiliations

¹ Division of Transdisciplinary Sciences, Graduate School of Frontier Science Initiative, Kanazawa University, Ishikawa, Japan.
² Department of Neurosurgery, Kanazawa University, Ishikawa, Japan.
³ Department of Nuclear Medicine, Kanazawa University, Ishikawa, Japan.
⁴ Division of Mechanical Science and Engineering, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Ishikawa, Japan.

Abstract

Purpose: Hemodynamics play a key role in the management of cerebral aneurysm recanalization after coil embolization; however, the most reliable hemodynamic parameter remains unknown. Previous studies have explored the use of both spatiotemporally averaged and maximal definitions for hemodynamic parameters, based on computational fluid dynamics (CFD) analysis, to build predictive models for aneurysmal recanalization. In this study, we aimed to assess the influence of different spatiotemporal characteristics of hemodynamic parameters on predictive performance.

Methods: Hemodynamics were simulated using CFD for 66 cerebral aneurysms from 65 patients. We evaluated 14 types of spatiotemporal definitions for two hemodynamic parameters in the pre-coiling model and five in virtual post-coiling model (VM) created by cutting the aneurysm from the pre-coiling model. A total of 91 spatiotemporal hemodynamic features were derived and utilized to develop univariate predictor (UP) and multivariate logistic regression (LR) models. The model's performance was assessed using two metrics: the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).

Results: Different spatiotemporal hemodynamic features exhibited a wide range of AUROC values ranging from 0.224 to 0.747, with 22 feature pairs showing a significant difference in AUROC value (P-value <0.05), despite being derived from the same hemodynamic parameter. PD_ave,q1 was identified as the strongest UP with AUROC/AUPRC values of 0.747/0.385, yielding sensitivity and specificity value of 0.889 and 0.614 at the optimal cut-off value, respectively. The LR model further improved the prediction performance, having AUROC/AUPRC values of 0.890/0.903. At the optimal cut-off value, the LR model achieved a specificity of 0.877, sensitivity of 0.719, outperforming the UP model.

Conclusion: Our research indicated that the characteristics of hemodynamic parameters in terms of space and time had a significant impact on the development of predictive model. Our findings suggest that LR model based on spatiotemporal hemodynamic features could be clinically useful in predicting recanalization after coil embolization in patients, without the need for invasive procedures.

Keywords: CFD; Intracranial aneurysm; Pressure difference; flow pattern.