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.

No abstract

No abstract

Rupture risk stratification is critical for incidentally detected intracranial aneurysms. Here we developed and validated an institutional nomogram to solve this issue. We reviewed the imaging and clinical databases for aneurysms from January 2015 to September 2018. Aneurysms were reconstructed and morphological features were extracted by the Pyradiomics in python. Multiple logistic regression was performed to develop the nomogram. The consistency of the nomogram predicted rupture risks and PHASES scores was assessed. The performance of the nomogram was evaluated by the discrimination, calibration, and decision curve analysis (DCA). 719 aneurysms were enrolled in this study. For each aneurysm, twelve morphological and nine clinical features were obtained. After logistic regression, seven features were enrolled in the nomogram, which were SurfaceVolumeRatio, Flatness, Age, Hyperlipemia, Smoker, Multiple aneurysms, and Location of the aneurysm. The nomogram had a positive and close correlation with PHASES score in predicting aneurysm rupture risks. AUCs of the nomogram in discriminating aneurysm rupture status was 0.837 in a separate testing set. The calibration curves fitted well and DCA demonstrated positive net benefits of the nomogram in guiding clinical decisions. In conclusion, Pyradiomics derived morphological features based institutional nomogram was useful for aneurysm rupture risk stratification.

No abstract

No abstract

No abstract

Arc erosion behaviors of TiB2/Cu composites with single-scale and dual-scale TiB2 particles fabricated by powder metallurgy were studied. It was revealed that the dual-scale TiB2/Cu composites had fewer structure defects compared with the single-scale TiB2/Cu composites, and TiB2 particles with different size were uniformly distributed in the copper matrix. When the ratio of 2 μm over 50 μm TiB2 particles is 1:2, the density of TiB2/Cu composite is 98.5% and shows best mechanical and thermal properties. The arc duration and energy of TiB2/Cu composites increase with the increase of electric current in contact material testing. Compared with the single-scale TiB2/Cu composites, the arc erosion of dual-scale TiB2/Cu composite with 2 μm+50 μm (1:2) TiB2 was slighter. The anode bulge area and cathode erosion pit of dual-scale TiB2/Cu composite was smaller. The dual-scale TiB2 particles optimize the microstructure and thermal stability of the composite, which is conducive to alleviating arc erosion. The synergistic effect of different sized TiB2 particles in the matrix improved the arc erosion resistance of TiB2/Cu composite during arcing.