Three-dimensional high-frequency ultrasound imaging for longitudinal evaluation of liver metastases in preclinical models

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Abstract

Liver metastasis is a clinically significant contributor to the mortality associated with melanoma, colon, and breast cancer. Preclinical mouse models are essential to the study of liver metastasis, yet their utility has been limited by the inability to study this dynamic process in a noninvasive and longitudinal manner. This study shows that three-dimensional high-frequency ultrasound can be used to noninvasively track the growth of liver metastases and evaluate potential chemotherapeutics in experimental liver metastasis models. Liver metastases produced by mesenteric vein injection of B16F1 (murine melanoma), PAP2 (murine H-ras-transformed fibroblast), HT-29 (human colon carcinoma), and MDA-MB-435/HAL (human breast carcinoma) cells were identified and tracked longitudinally. Tumor size and location were verified by histologic evaluation. Tumor volumes were calculated from the three-dimensional volumetric data, with individual liver metastases showing exponential growth. The importance of volumetric imaging to reduce uncertainty in tumor volume measurement was shown by comparing three-dimensional segmented volumes with volumes estimated from diameter measurements and the assumption of an ellipsoid shape. The utility of high-frequency ultrasound imaging in the evaluation of therapeutic interventions was established with a doxorubicin treatment trial. These results show that three-dimensional high-frequency ultrasound imaging may be particularly well suited for the quantitative assessment of metastatic progression and the evaluation of chemotherapeutics in preclinical liver metastasis models. © 2005 American Association for Cancer Research.

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CITATION STYLE

APA

Graham, K. C., Wirtzfeld, L. A., MacKenzie, L. T., Postenka, C. O., Groom, A. C., MacDonald, I. C., … Chambers, A. F. (2005). Three-dimensional high-frequency ultrasound imaging for longitudinal evaluation of liver metastases in preclinical models. Cancer Research, 65(12), 5231–5237. https://doi.org/10.1158/0008-5472.CAN-05-0440

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