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13 March 2014 Validation and reproducibility assessment of modality independent elastography in a pre-clinical model of breast cancer
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Clinical observations have long suggested that cancer progression is accompanied by extracellular matrix remodeling and concomitant increases in mechanical stiffness. Due to the strong association of mechanics and tumor progression, there has been considerable interest in incorporating methodologies to diagnose cancer through the use of mechanical stiffness imaging biomarkers, resulting in commercially available US and MR elastography products. Extension of this approach towards monitoring longitudinal changes in mechanical properties along a course of cancer therapy may provide means for assessing early response to therapy; therefore a systematic study of the elasticity biomarker in characterizing cancer for therapeutic monitoring is needed. The elastography method we employ, modality independent elastography (MIE), can be described as a model-based inverse image-analysis method that reconstructs elasticity images using two acquired image volumes in a pre/post state of compression. In this work, we present preliminary data towards validation and reproducibility assessment of our elasticity biomarker in a pre-clinical model of breast cancer. The goal of this study is to determine the accuracy and reproducibility of MIE and therefore the magnitude of changes required to determine statistical differences during therapy. Our preliminary results suggest that the MIE method can accurately and robustly assess mechanical properties in a pre-clinical system and provide considerable enthusiasm for the extension of this technique towards monitoring therapy-induced changes to breast cancer tissue architecture.
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Jared A. Weis, Dong Kyu Kim, Thomas E. Yankeelov, and Michael I. Miga "Validation and reproducibility assessment of modality independent elastography in a pre-clinical model of breast cancer", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90381I (13 March 2014);

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