Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

A recent joint meeting was held on January 30, 2014, with the US Food and Drug Administration (FDA), National Cancer Institute (NCI), clinical scientists, imaging experts, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocate groups to discuss imaging endpoints for clinical trials in glioblastoma. This workshop developed a set of priorities and action items including the creation of a standardized MRI protocol for multicenter studies. The current document outlines consensus recommendations for a standardized Brain Tumor Imaging Protocol (BTIP), along with the scientific and practical justifications for these recommendations, resulting from a series of discussions between various experts involved in aspects of neuro-oncology neuroimaging for clinical trials. The minimum recommended sequences include: (i) parameter-matched precontrast and postcontrast inversion recovery-prepared, isotropic 3D T1-weighted gradient-recalled echo; (ii) axial 2D T2-weighted turbo spin-echo acquired after contrast injection and before postcontrast 3D T1-weighted images to control timing of images after contrast administration; (iii) precontrast, axial 2D T2-weighted fluid-attenuated inversion recovery; and (iv) precontrast, axial 2D, 3-directional diffusion-weighted images. Recommended ranges of sequence parameters are provided for both 1.5 T and 3 T MR system

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Interrater Reliability of National Institutes of Health Traumatic Brain Injury Imaging Common Data Elements for Brain Magnetic Resonance Imaging in Mild Traumatic Brain Injury

The National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS) Traumatic Brain Injury (TBI) Imaging Common Data Elements (CDEs) are standardized definitions for pathological intracranial lesions based on their appearance on neuroimaging studies. The NIH-NINDS TBI Imaging CDEs were designed to be as consistent as possible with the U.S. Food and Drug Administration (FDA) definition of biomarkers as "an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention." However, the FDA qualification process for biomarkers requires proof of reliable biomarker test measurements. We determined the interrater reliability of TBI Imaging CDEs on subacute brain magnetic resonance imaging (MRI) performed on 517 mild TBI patients presenting to 11 U.S. level 1 trauma centers. Three U.S. board-certified neuroradiologists independently evaluated brain MRI performed 2 weeks post-injury for the following CDEs: traumatic axonal injury (TAI), diffuse axonal injury (DAI), and brain contusion. We found very high interrater agreement for brain contusion, with prevalence- and bias-adjusted kappa (PABAK) values for pairs of readers from 0.92 [95% confidence interval, 0.88-0.95] to 0.94 [0.90-0.96]. We found intermediate agreement for TAI and DAI, with PABAK values of 0.74-0.78 [0.70-0.82]. The near-perfect agreement for subacute brain contusion is likely attributable to the high conspicuity and distinctive appearance of these lesions on T1-weighted images. Interrater agreement for TAI and DAI was lower, because signal void in small vascular structures, and artifactual foci of signal void, can be difficult to distinguish from the punctate round or linear areas of slight hemorrhage that are a common hallmark of TAI/DAI on MRI

Introduction and summary

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Control and optimization of dynamical systems in the presence of stochastic uncertainty is a mature field with a large range of applications. A comprehensive treatment of such problems can be found in excellent books and other resources including [7, 16, 29, 68, 84, 95, 104], and [6]. To date, there exist a nearly complete theory regarding the existence and structure of optimal solutions under various formulations as well as computational methods to obtain such optimal solutions for problems with finite state and control spaces. However, there still exist substantial computational challenges involving problems with large state and action spaces, such as standard Borel spaces. For such state and action spaces, obtaining optimal policies is in general computationally infeasible