The biophysical role of hemodynamics in the pathogenesis of cerebral aneurysm formation and rupture.

The pathogenesis of intracranial aneurysms remains complex and multifactorial. While vascular, genetic, and epidemiological factors play a role, nascent aneurysm formation is believed to be induced by hemodynamic forces. Hemodynamic stresses and vascular insults lead to additional aneurysm and vessel remodeling. Advanced imaging techniques allow us to better define the roles of aneurysm and vessel morphology and hemodynamic parameters, such as wall shear stress, oscillatory shear index, and patterns of flow on aneurysm formation, growth, and rupture. While a complete understanding of the interplay between these hemodynamic variables remains elusive, the authors review the efforts that have been made over the past several decades in an attempt to elucidate the physical and biological interactions that govern aneurysm pathophysiology. Furthermore, the current clinical utility of hemodynamics in predicting aneurysm rupture is discussed.

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

Hemodynamic and morphological characteristics of unruptured posterior communicating artery aneurysms with oculomotor nerve palsy.

OBJECT Unruptured posterior communicating artery (PCoA) aneurysms with oculomotor nerve palsy (ONP) have a very high risk of rupture. This study investigated the hemodynamic and morphological characteristics of intracranial aneurysms with high rupture risk by analyzing PCoA aneurysms with ONP. METHODS Fourteen unruptured PCoA aneurysms with ONP, 33 ruptured PCoA aneurysms, and 21 asymptomatic unruptured PCoA aneurysms were included in this study. The clinical, morphological, and hemodynamic characteristics were compared among the different groups. RESULTS The clinical characteristics did not differ among the 3 groups (p > 0.05), whereas the morphological and hemodynamic analyses showed that size, aspect ratio, size ratio, undulation index, nonsphericity index, ellipticity index, normalized wall shear stress (WSS), and percentage of low WSS area differed significantly (p < 0.05) among the 3 groups. Furthermore, multiple comparisons revealed that these parameters differed significantly between the ONP group and the asymptomatic unruptured group and between the ruptured group and the asymptomatic unruptured group, except for size, which differed significantly only between the ONP group and the asymptomatic unruptured group (p = 0.0005). No morphological or hemodynamic parameters differed between the ONP group and the ruptured group. CONCLUSIONS Unruptured PCoA aneurysms with ONP demonstrated a distinctive morphological-hemodynamic pattern that was significantly different compared with asymptomatic unruptured PCoA aneurysms and was similar to ruptured PCoA aneurysms. The larger size, more irregular shape, and lower WSS might be related to the high rupture risk of PCoA aneurysms.