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Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest-averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm2/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test-retest coefficient-of-variation (CVWS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CVWS. Determination of T2 is also robust with a ∼3% CVWS. Measurements of T1 and ADC are less robust with CVWS values in the 6%-11% range. Preliminary DCE test-retest time-course determinations, as quantified by area under the curve and Ktrans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%-40% CVWS.
Pubmed ID: 31572793
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
View all literature mentionsOpen source interactive software application for three dimentional medical images, manual delineation of anatomical regions of interest, and performing automatic image segmentation. Used for delineating anatomical structures and regions in MRI, CT and other 3D biomedical imaging data.
View all literature mentionsWelcome to the Bayesian Analysis of Common NMR Problems software home page. This Bayesian analysis software is a series of programs with a Java interface that use Bayesian probability theory to solve common data analysis problems that occur in the sciences and in NMR in particular. Click here for a complete list of the applications addressed. The programs that run the various Bayesian analysis, the server software, were developed at Washington University by Dr. G. Larry Bretthorst and the Java language client interface was developed by Dr. Karen Marutyan. The combination of the server and client software is called the Bayesian Analysis of Common NMR Problems software. However, this name is slightly misleading because this software can analyze data from many different sources, not just NMR data. Additionally, unlike the previous interface to this software, this new interface does not require the user to have access to any specialized NMR software, i.e., this interface is completely independent of Varian''s VnmrJ, although the interface can load and process data from a Varian spectrometer. Sponsors: This resource is supported by the Washington University in St. Louis. Keywords: Analysis, Software, Java, Theory, Science, NMR, Server, Data, Spectrometer,
View all literature mentionsNSG-HLA-A2/HHD mutant mice are immunodeficient and express human HLA class 1 heavy and light chains. This strain may be useful as a human hematopoietic engraftment host that supports the maturation of human T cells with transplantation http://jaxmice.jax.org/strain/014570.html.
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