BACKGROUND: Current studies that analyze the usefulness of amino acid and FDG-PET in distinguishing brain metastasis recurrence and radionecrosis after radiation therapy are limited by small cohort size. PURPOSE: Our aim was to assess the diagnostic accuracy of amino acid and FDG-PET in differentiating brain metastasis recurrence from radionecrosis after radiation therapy. DATA SOURCES: Studies were retrieved from PubMed, Embase, and the Cochrane Library. STUDY SELECTION: Fifteen studies were included from the literature. Each study used PET to differentiate radiation necrosis from tumor recurrence in contrast-enhancing lesions on follow-up brain MR imaging after treating brain metastasis with radiation therapy. DATA ANALYSIS: Data were analyzed with a bivariate random-effects model. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were pooled, and a summary receiver operating characteristic curve was fit to the data. DATA SYNTHESIS: The overall pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of PET were 0.85, 0.88, 7.0, 0.17, and 40, respectively. The area under the receiver operating characteristic curve was 0.93. On subgroup analysis of different tracers, amino acid and FDG-PET had similar diagnostic accuracy. Meta-regression analysis demonstrated that the method of quantification based on patient, lesion, or PET scan (based on lesion versus not, P = .07) contributed to the heterogeneity. LIMITATIONS: Our study was limited by small sample size, and 60% of the included studies were of retrospective design. CONCLUSIONS: Amino acid and FDG-PET had good diagnostic accuracy in differentiating brain metastasis recurrence from radionecrosis after radiation therapy.

BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging. MATERIALS AND METHODS: This retrospective study included preoperative MR imaging of 288 patients with pediatric posterior fossa tumors, including medulloblastoma (n = 111), ependymoma (n = 70), and pilocytic astrocytoma (n = 107). Radiomics features were extracted from T2-weighted images, contrast-enhanced T1-weighted images, and ADC maps. Models generated by standard manual optimization by a machine learning expert were compared with automatic machine learning via the Tree-Based Pipeline Optimization Tool for performance evaluation. RESULTS: For 3-way classification, the radiomics model by automatic machine learning with the Tree-Based Pipeline Optimization Tool achieved a test micro-averaged area under the curve of 0.91 with an accuracy of 0.83, while the most optimized model based on the feature-selection method χ² score and the Generalized Linear Model classifier achieved a test micro-averaged area under the curve of 0.92 with an accuracy of 0.74. Tree-Based Pipeline Optimization Tool models achieved significantly higher accuracy than average qualitative expert MR imaging review (0.83 versus 0.54, P < .001). For binary classification, Tree-Based Pipeline Optimization Tool models achieved an area under the curve of 0.94 with an accuracy of 0.85 for medulloblastoma versus nonmedulloblastoma, an area under the curve of 0.84 with an accuracy of 0.80 for ependymoma versus nonependymoma, and an area under the curve of 0.94 with an accuracy of 0.88 for pilocytic astrocytoma versus non-pilocytic astrocytoma. CONCLUSIONS: Automatic machine learning based on routine MR imaging classified pediatric posterior fossa tumors with high accuracy compared with manual expert pipeline optimization and qualitative expert MR imaging review.

The angioarchitecture is a fundamental aspect of brain development and physiology. However, available imaging tools are unsuited for non-destructive cerebral mapping of the functionally important three-dimensional (3D) vascular microstructures. To address this issue, we developed an ultra-high resolution 3D digitalized angioarchitectural map for rat brain, based on synchrotron radiation phase contrast imaging (SR-PCI) with pixel size of 5.92 μm. This approach provides a systematic and detailed view of the cerebrovascular anatomy at the micrometer level without any need for contrast agents. From qualitative and quantitative perspectives, the present 3D data provide a considerable insight into the spatial vascular network for whole rodent brain, particularly for functionally important regions of interest, such as the hippocampus, pre-frontal cerebral cortex and the corpus striatum. We extended these results to synchrotron-based virtual micro-endoscopy, thus revealing the trajectory of targeted vessels in 3D. The SR-PCI method for systematic visualization of cerebral microvasculature holds considerable promise for wider application in life sciences, including 3D micro-imaging in experimental models of neurodevelopmental and vascular disorders.

Identification of prognostic biomarkers in glioblastoma using a long non-coding RNA-mediated, competitive endogenous RNA network