BACKGROUND AND PURPOSE: Hemodynamics factors play an important role in the rupture of cerebral aneurysms. The purpose of this study was to evaluate the impact of hemodynamic factors on the rupture of the MANs with 3D reconstruction model CFD simulation. MATERIALS AND METHODS: RDSA was performed in 9 pairs of intracranial MANs. Each pair was divided into ruptured and unruptured groups. The hemodynamic factors of the aneurysms and their parent arteries were compared. RESULTS: There was a significant difference in the WSS at peak systole between the regions of the aneurysms and their parent arteries in the ruptured group (ie, 6.49 ± 3.48 Pa versus 8.78 ± 3.57 Pa, P =.015) but not in the unruptured group (ie, 9.80 ± 4.12 Pa versus 10.17 ± 7.48 Pa, P =.678). The proportion of the low WSS area to the whole area of the aneurysms was 12.20 ± 18.08% in the ruptured group and 3.96 ± 6.91% in the unruptured group; the difference between the 2 groups was statistically significant (P =.015). The OSI was 0.0879 ± 0.0764 in the ruptured group, which was significantly higher than that of the unruptured group (ie, 0.0183 ± 0.0191, P =.008). CONCLUSIONS: MANs may be a useful disease model to investigate possible causes linked to ruptured aneurysms. The ruptured aneurysms manifested lower WSS compared with their parent arteries, a higher proportion of the low WSS area to the whole area of aneurysm, and higher OSI compared with the unruptured aneurysms.

Radiomics for the detection of microvascular invasion in hepatocellular carcinoma

BACKGROUND AND PURPOSE: IDH1 mutational status probably plays an important role in the predictive response for patients with astroglioma. This study explores whether DTI metrics are able to noninvasively detect IDH1 status in astrogliomas. MATERIALS AND METHODS: The DTI data of 112 patients with pathologically proven astroglioma (including 25, 12, and 10 cases with IDH1 mutation and 11, 11, and 43 cases without mutation in grades II, III, and IV, respectively) were retrospectively reviewed. The maximal fractional anisotropy, minimal ADC, ratio of maximal fractional anisotropy, and ratio of minimal ADC in the tumor body were measured. In the same World Health Organization grading, the imaging parameters of patients with and without IDH1 R132H mutation were compared by means of optimal metrics for detecting mutations. Receiver operating characteristic curve analysis was performed. RESULTS: The maximal fractional anisotropy and ratio of maximal fractional anisotropy values had statistical significance between patients with IDH1 R132H mutation and those without mutation in astrogliomas of grades II and III. The areas under the curve for maximal fractional anisotropy and ratio of maximal fractional anisotropy were both 0.92 in grade II and 0.80 and 0.82 in grade III. The minimal ADC value and ratio of minimal ADC value also demonstrated statistical significance between patients with mutation and those without mutation in all astroglioma grades. The areas under the curve for minimal ADC were 0.94 (II), 0.76 (III), and 0.66 (IV), and the areas under the curve for ratio of minimal ADC were 0.93 (II), 0.83 (III), and 0.70 (IV). CONCLUSIONS: Fractional anisotropy and ADC from DTI can noninvasively detect IDH1 R132H mutation in astrogliomas.

Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, grades, and molecular characteristics of gliomas can lead to different behavioral deficits and prognosis, which are closely related to patients' quality of life and associated with neuroplasticity. Some advanced magnetic resonance imaging (MRI) technologies can explore the neuroplasticity of structural, topological, biochemical metabolism, and related mechanisms, which may contribute to the improvement of prognosis and function in glioma patients. In this review, we summarized the studies conducted on structural and topological plasticity of glioma patients through different MRI technologies and discussed future research directions. Previous studies have found that glioma itself and related functional impairments can lead to structural and topological plasticity using multimodal MRI. However, neuroplasticity caused by highly heterogeneous gliomas is not fully understood, and should be further explored through multimodal MRI. In addition, the individualized prediction of functional prognosis of glioma patients from the functional level based on machine learning (ML) is promising. These approaches and the introduction of ML can further shed light on the neuroplasticity and related mechanism of the brain, which will be helpful for management of glioma patients.