Establishment of a decision tree prediction model for the treatment of intracranial aneurysms using temperature-sensitive embolic agents based on geometric features
Abstract
Intracranial aneurysms are abnormal expansions caused by weak arterial walls, which can lead to subarachnoid hemorrhage and high mortality rates in severe cases. Its clinical treatment commonly involves transcatheter arterial embolization. Compared with mainstream coil materials, the use of emerging temperature-sensitive embolic agents has higher occlusion rates, and reduces stress on the aneurysm wall, with lower toxicity and better treatment outcomes. However, due to the irreversibility of the coagulation process, there is a risk of unintended embolization of distal branches, limiting their clinical applicability. In order to obtain the applicable conditions of the temperature-sensitive embolic agent and further improve its applicability, this study employed the Euler two-phase flow model to simulate the embolization process of these agents. Based on the simulation results and geometric features of the cases, a decision tree model was established. Cross-validation revealed an overall success rate of 78.57% for predicting treatment applicability, with a sensitivity of 71.4%, specificity of 81.0%, and an F1 score of 62.5%. This decision tree model can serve as an auxiliary tool in the clinical treatment of intracranial aneurysms, allowing for the selection of cases suitable for temperature-sensitive embolization based on patients’ specific geometric features obtained from imaging, thereby enhancing the success rate of surgical procedures.
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