Background and Purpose— Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in small aneurysms. This study aims to evaluate the feasibility of applying machine learning for predicting aneurysm stability with radiomics-derived morphological features. Methods— Morphological features of 719 aneurysms were extracted from PyRadiomics, of which 420 aneurysms with Maximum3DDiameter ranging from 4 mm to 8 mm were enrolled for analysis. The stability of these aneurysms and other clinical characteristics were reviewed from the medical records. Based on the morphologies with/without clinical features, machine learning models were constructed and compared to define the morphological determinants and screen the optimal model for predicting aneurysm stability. The effect of clinical characteristics on the morphology of unstable aneurysms was analyzed. Results— Twelve morphological features were automatically extracted from PyRadiomics implemented in Python for each aneurysm. Lasso regression defined Flatness as the most important morphological feature to predict aneurysm stability, followed by SphericalDisproportion, Maximum2DDiameterSlice, and SurfaceArea. SurfaceArea (odds ratio [OR], 0.697; 95% CI, 0.476–0.998), SphericalDisproportion (OR, 1.730; 95% CI, 1.143–2.658), Flatness (OR, 0.584; 95% CI, 0.374–0.894), Hyperlipemia (OR, 2.410; 95% CI, 1.029–5.721), Multiplicity (OR, 0.182; 95% CI, 0.082–0.380), Location at middle cerebral artery (OR, 0.359; 95% CI, 0.134–0.902), and internal carotid artery (OR, 0.087; 95% CI, 0.030–0.211) were enrolled into the final prediction model. In terms of performance, the area under curve of the model reached 0.853 (95% CI, 0.767–0.940). For unstable aneurysms, Compactness1 ( P =0.035), Compactness2 ( P =0.036), Sphericity ( P =0.035), and Flatness ( P =0.010) were low, whereas SphericalDisproportion ( P =0.034) was higher in patients with hypertension. Conclusions— Morphological features extracted from PyRadiomics can be used for aneurysm stratification. Flatness is the most important morphological determinant to predict aneurysm stability. Our model can be used to predict aneurysm stability. Unstable aneurysm is more irregular in patients with hypertension.

ObjectiveTo investigate warning effect of serum miRNA for intracranial aneurysm rupture through microarray hybridization.Methods24 were selected from 560 patients in our department and divided into group A, B, C and D. They are aneurysms with daughter aneurysms group, aneurysm without daughter aneurysms group, ruptured aneurysms group and angiography negative group. Then a microarray study was carried out using serum miRNA. Differentially expressed miRNAs were identified. Cluster analysis was performed in order to make the results looks more intuitive and potential gene targets were retrieved from miRNA target prediction databases.ResultsMicroarray study identified 86 miRNAs with significantly different (p < 0.05) expression levels between three experimental groups and control group. Among them 69 are up-regulated and 17 are down-regulated. All miRNAs in group A are up-regulated, while there are up and down-regulated in group B and C. A total of 8291 predicted target genes are related to these miRNAs. Bioinformatic analysis revealed that several target genes are involved in apoptosis and activation of cells associated with function of vascular wall.ConclusionOur gene level approach reveals several different serum miRNAs between normal people and aneurysm patients, as well as among different phases of aneurysm, suggesting that miRNA may participate in the regulation of the occurrence and development of intracranial aneurysm, and also have warning effect for intracranial aneurysm rupture. All differently expressed miRNA in group A are up-regulated, which may suggesting protective function of miRNA for intracranial vascular wall.

No abstract