Abstract
A cerebral aneurysm may present irregularities associated with rupture risks. However, conventional morphological parameters are limited in evaluating the aneurysm irregularity. Although the mass moment of inertia has been devised for the irregularity evaluation, its performance still needs to be improved. In this study, three novel morphological indexes (NMIs) were devised based on the mass moment of inertia (ANI, aneurysm-to-neck index; AVI, aneurysm-to-vessel index; AII, aneurysm irregularity index) to effectively describe aneurysm irregularities. 456 patients with cerebral aneurysms (367 unruptured and 89 ruptured) were enrolled and their NMIs and the conventional morphological parameters were calculated for comparison. Artificial neural networks (ANNs) were trained with each parameter and then used to predict rupture risk. All NMIs were significantly higher in ruptured cases than in unruptured cases (p-values for [Formula: see text], [Formula: see text], and [Formula: see text] were < 0.001, <0.001, and < 0.001, respectively). The highest performance for rupture risk prediction (sensitivity, 92.9%; specificity, 92.0%; and area under the receiver operating characteristic curve, 0.951) was obtained when the NMIs were considered in the ANN model. In particular, the [Formula: see text] effectively described the aneurysm irregularities that could not be evaluated using conventional morphological parameters. The NMIs were effective in evaluating aneurysm irregularities, enabling timely prediction of an aneurysm rupture.