Intracranial aneurysm instability prediction model based on 4D-Flow MRI and HR-MRI.

Abstract

This study aims to develop a reliable predictive model for assessing intracranial aneurysm (IA) instability by utilizing four-dimensional flow magnetic resonance imaging (4D-Flow MRI) and high-resolution MRI (HR-MRI). Initially, we curated a prospective dataset, dubbed the primary cohort, by aggregating patient data that was consecutively enrolled across two centers from November 2018 to November 2021. Unstable aneurysms were defined as those with symptoms, morphological change or ruptured during follow-up periods. We introduce a specialized ensemble learning framework, termed the Hybrid Model, which synergistically combines two heterogeneous base learning algorithms: 4D-Flow logistic regression (4D-Flow-LR) and Multi-crop Attention Branch Network (MicroAB-Net). The ability of the hybrid model to predict aneurysm instability was compared with baseline models: PHASES (population, hypertension, age, size, earlier rupture, and site) LR, ELAPSS (earlier subarachnoid hemorrhage, location, age, population, size, and shape) LR, aneurysm wall enhancement (AWE) LR, and Radiomics using the area under the curve (AUC) with Delong's test. Finally, the Hybrid Model was further validated in the validation cohort (patients enrolled between December 2021 to May 2022). In the primary cohort, 189 patients (144 women [76.2 ​%]; aged 58.90 years ​± ​10.32) with 213 IAs were included. In the validation cohort, 48 patients (35 women [72.9 ​%]; aged 55.0 years ​± ​10.77) with 53 IAs were included. The Hybrid Model achieved the highest performance both in the primary cohort (AUC ​= ​0.854) and the validation cohort (AUC ​= ​0.876). The Hybrid model provided a promising prediction of aneurysm instability.