Study of the reliability of quantification methods of dynamic contrast-enhanced ultrasonography numerical modeling of blood flow in tumor microvascularization

Abstract : Dynamic contrast-enhanced ultrasonography is a recent functional dynamic imaging technique that allows evaluation of the efficacy of anti-angiogenic treatments by quantifying changes in specific parameters of the tumor vasculature. Preclinical and clinical experimental studies now reveal the existence of sources of variability in the quantitative methods. In order to study the reliability of quantification methods (both semi-quantitative and quantitative), we have developed the first numerical model of blood flow and contrast agents in vascular networks with computational fluid dynamics Fluent software version 15.0 (ANSYS, France). We studied four vascular networks (1.84 x 10(-3), 2.28 x 10(-3), 2.4 x 10(-3) and 2.54 x 10(-3 )ml) and four blood velocities (0.01, 0.02, 0.03 and 0.05 ms(-1)). For variations in tumor vascular volume the quantitative method is more sensitive, with variations of parameter perfusion of 25.7%, in contrast to variations of the semi-quantitative parameters between 14.9 and 19.5%. For changes in blood velocity the semi-quantitative method is more sensitive, with variation of the area under the enhancement curve (64%), the maximum of the enhancement curve (60%), and the slope of the enhancement curve (73%). The transit time parameters from the two quantitative methods were weakly sensitive to both blood volume and blood flow variations. This study is hopeful and may be extended to the treatment of more complex vascular networks, to approach clinical conditions, and to the evaluation of quantification methods in contrast imaging.
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Submitted on : Friday, September 21, 2018 - 4:30:14 PM
Last modification on : Friday, June 21, 2019 - 10:35:25 AM

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Laure Boyer, Ingrid Leguerney, Randall S. Thomas, Virginie Grand-Perret, Nathalie Lassau, et al.. Study of the reliability of quantification methods of dynamic contrast-enhanced ultrasonography numerical modeling of blood flow in tumor microvascularization. Physics in Medicine and Biology, IOP Publishing, 2018, 63 (17), pp.17NT01. ⟨10.1088/1361-6560/aad6ae⟩. ⟨hal-01878968⟩

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