Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.

Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.

Science to practice: Can diffusion-weighted MR imaging findings be used as biomarkers to monitor the progression of renal fibrosis?

In an experimental murine model of unilateral ureteral obstruction, Togao et al demonstrated that diffusion-weighted (DW) magnetic resonance (MR) imaging can depict and enable monitoring of abnormal changes in the progression of renal fibrosis; because these microstructural changes are complex and multifactorial, future studies focused on their specificity should be performed before they are applied in clinical trials.

A strategy combining imaging and laboratory biomarkers in comparison with a simplified clinical score for risk stratification of patients with acute pulmonary embolism

This study aimed to assess the performance of two prognostic models-the European Society of Cardiology (ESC) model and the simplified Pulmonary Embolism Severity Index (sPESI)-in predicting short-term mortality in patients with pulmonary embolism (PE).

Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study

Objectives: Neurofunctional alterations are correlates of vulnerability to psychosis, as well as of the disorder itself. How these abnormalities relate to different probabilities for later transition to psychosis is unclear. We investigated vulnerability- versus disease-related versus resilience biomarkers of psychosis during working memory (WM) processing in individuals with an at-risk mental state (ARMS). Experimental design: Patients with “first-episode psychosis” (FEP, n = 21), short-term ARMS (ARMS-ST, n = 17), long-term ARMS (ARMS-LT, n = 16), and healthy controls (HC, n = 20) were investigated with an n-back WM task. We examined functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data in conjunction using biological parametric mapping (BPM) toolbox. Principal observations: There were no differences in accuracy, but the FEP and the ARMS-ST group had longer reaction times compared with the HC and the ARMS-LT group. With the 2-back > 0-back contrast, we found reduced functional activation in ARMS-ST and FEP compared with the HC group in parietal and middle frontal regions. Relative to ARMS-LT individuals, FEP patients showed decreased activation in the bilateral inferior frontal gyrus and insula, and in the left prefrontal cortex. Compared with the ARMS-LT, the ARMS-ST subjects showed reduced activation in the right inferior frontal gyrus and insula. Reduced insular and prefrontal activation was associated with gray matter volume reduction in the same area in the ARMS-LT group. Conclusions: These findings suggest that vulnerability to psychosis was associated with neurofunctional alterations in fronto-temporo-parietal networks in a WM task. Neurofunctional differences within the ARMS were related to different duration of the prodromal state and resilience factors

Thrombosis during pregnancy: Risks, prevention, and treatment for mother and fetus--harvesting the power of omic technology, biomarkers and in vitro or in vivo models to facilitate the treatment of thrombosis

Maternal thromboembolism and a spectrum of placenta-mediated complications including the pre-eclampsia syndromes, fetal growth restriction, fetal loss, and abruption manifest a shared etiopathogenesis and predisposing risk factors. Furthermore, these maternal and fetal complications are often linked to subsequent maternal health consequences that comprise the metabolic syndrome, namely, thromboembolism, chronic hypertension, and type II diabetes. Traditionally, several lines of evidence have linked vasoconstriction, excessive thrombosis and inflammation, and impaired trophoblast invasion at the uteroplacental interface as hallmark features of the placental complications. "Omic" technologies and biomarker development have been largely based upon advances in vascular biology, improved understanding of the molecular basis and biochemical pathways responsible for the clinically relevant diseases, and increasingly robust large cohort and/or registry based studies. Advances in understanding of innate and adaptive immunity appear to play an important role in several pregnancy complications. Strategies aimed at improving prediction of these pregnancy complications are often incorporating hemodynamic blood flow data using non-invasive imaging technologies of the utero-placental and maternal circulations early in pregnancy. Some evidence suggests that a multiple marker approach will yield the best performing prediction tools, which may then in turn offer the possibility of early intervention to prevent or ameliorate these pregnancy complications. Prediction of maternal cardiovascular and non-cardiovascular consequences following pregnancy represents an important area of future research, which may have significant public health consequences not only for cardiovascular disease, but also for a variety of other disorders, such as autoimmune and neurodegenerative diseases.