The meta-analyses in this review provide some evidence for the effectiveness of psychological therapies in prevention of PTSD and reduction of symptoms in children and adolescents exposed to trauma for up to a month. However, our confidence in these findings is limited by the quality of the included studies and by substantial heterogeneity between studies. Much more evidence is needed to demonstrate the relative effectiveness of different psychological therapies for children exposed to trauma, particularly over the longer term. High-quality studies should be conducted to compare these therapies.

BACKGROUND: Determination of isocitrate dehydrogenase (IDH) status and, if IDH-mutant, assessing 1p19q codeletion are an important component of diagnosis of World Health Organization grades II/III or lower-grade gliomas. This has led to research into noninvasively correlating imaging features ("radiomics") with genetic status. PURPOSE: Our aim was to perform a diagnostic test accuracy systematic review for classifying IDH and 1p19q status using MR imaging radiomics, to provide future directions for integration into clinical radiology. DATA SOURCES: Ovid (MEDLINE), Scopus, and the Web of Science were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy guidelines. STUDY SELECTION: Fourteen journal articles were selected that included 1655 lower-grade gliomas classified by their IDH and/or 1p19q status from MR imaging radiomic features. DATA ANALYSIS: For each article, the classification of IDH and/or 1p19q status using MR imaging radiomics was evaluated using the area under curve or descriptive statistics. Quality assessment was performed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the radiomics quality score. DATA SYNTHESIS: The best classifier of IDH status was with conventional radiomics in combination with convolutional neural network-derived features (area under the curve ¼ 0.95, 94.4% sensitivity, 86.7% specificity). Optimal classification of 1p19q status occurred with texture-based radiomics (area under the curve ¼ 0.96, 90% sensitivity, 89% specificity). LIMITATIONS: A meta-analysis showed high heterogeneity due to the uniqueness of radiomic pipelines. CONCLUSIONS: Radiogenomics is a potential alternative to standard invasive biopsy techniques for determination of IDH and 1p19q status in lower-grade gliomas but requires translational research for clinical uptake. ABBREVIATIONS: AI ¼ artificial intelligence; AUC ¼ area under the curve; CNN ¼ convolutional neural network; IDH ¼ isocitrate dehydrogenase; IDH-mut ¼ IDH-mutant; LGG ¼ lower-grade gliomas; ML ¼ machine learning; PRISMA-DTA ¼ Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy; QUADAS-2 ¼ Quality Assessment of Diagnostic Accuracy Studies 2; RQS ¼ radiomics quality score; SVM ¼ support vector machine; VASARI ¼ Visually Accessible Rembrandt Images; WHO ¼ World Health Organization

The health and social needs of prisoners with ID transitioning into the community are a significant concern for researchers, policy makers and practitioners. Our findings highlight the need for proactive, appropriate and targeted service responses from disability, health and justice sectors.

The introduction of quantitative image analysis has given rise to fields such as radiomics which have been used to predict clinical sequelae. One growing area of interest for analysis is brain tumours, in particular glioblastoma multiforme (GBM). Tumour segmentation is an important step in the pipeline in the analysis of this pathology. Manual segmentation is often inconsistent as it varies between observers. Automated segmentation has been proposed to combat this issue. Methodologies such as convolutional neural networks (CNNs) which are machine learning pipelines modelled on the biological process of neurons (called nodes) and synapses (connections) have been of interest in the literature. We investigate the role of CNNs to segment brain tumours by firstly taking an educational look at CNNs and perform a literature search to determine an example pipeline for segmentation. We then investigate the future use of CNNs by exploring a novel field-radiomics. This examines quantitative features of brain tumours such as shape, texture, and signal intensity to predict clinical outcomes such as survival and response to therapy.