Morphometric development of the human fetal cerebellum during the early second trimester.

The protracted nature of development makes the cerebellum vulnerable to a broad spectrum of pathologic conditions, especially during the early fetal period. This study aims to characterize normal cerebellar growth in human fetuses during the early second trimester. We manually segmented the fetal cerebellum using 7.0-T high-resolution MR images obtained in 35 specimens with gestational ages ranging from 15 to 22 weeks. Volume measurements and shape analysis were performed to quantitatively evaluate global and regional cerebellar growth. The absolute volume of the fetal cerebellum showed a quadratic growth with increasing gestational age, while the pattern of relative volume changes revealed that the cerebellum grew at a greater pace than the cerebrum after 17 gestational weeks. Shape analysis was used to examine the distinctive development of subregions of the cerebellum. The extreme lateral portions of both cerebellar hemispheres showed the lowest rate of growth. The anterior lobe grew faster than most of the posterior lobe. These findings expand our understanding of the early growth pattern of the human cerebellum and could be further used to assess the developmental conditions of the fetal brain.

CT texture analysis of vulnerable plaques on optical coherence tomography.

PURPOSE: To explore whether CT texture analysis can identify thin-cap fibroatheroma (TCFA) determined by optical coherence tomography (OCT). METHODS: Thirty-three patients with 43 lesions who underwent both CCTA and OCT within 3 months were retrospectively included. 12 conventional CT-derived plaque features, fat attenuation index (FAI) and 1691 plaque radiomics features were extracted to discriminate TCFA lesions and non-TCFA lesions determined by OCT. Minimum redundancy and maximum relevance (mRMR) method was employed to select radiomics features. The top ranked features were used to construct a forward stepwise logistic radiomics model. The performance of radiomics model was compared with the conventional high-risk plaque (HRP) features model and FAI model for the detection of TCFA. RESULTS: Out of 1691 features, 35 features were significantly different between TCFA and non-TCFA lesions (all p＜0.05) while only low attenuation plaque (LAP) was more frequent in TCFA group (p = 0.004). There was no significant difference in FAI between TCFA and non-TCFA lesions. Five features were ultimately integrated into the radiomics model after mRMR analysis, which demonstrated significantly higher AUC for the detection of TCFA (0.952; 95 % CI: 0.897-1.000) compared with the conventional HRP features model (0.621; 95 % CI: 0.469-0.773, p < 0.001) and FAI model (0.52; 95 % CI: 0.33-0.70, p < 0.001). CONCLUSION: CT texture analysis performs better at identifying TCFA determined by OCT compared with conventional CT-derived plaque parameters and FAI. Texture analysis may serve as a potential non-invasive method of evaluating vulnerable plaque.