Visualization and Quantification of Intraperitoneal Tumors by In Vivo Computed Tomography Using Negative Contrast Enhancement Strategy in a Mouse Model of Ovarian Cancer
Year: 2009
Type: article
Abstract: Small animal computed tomography (CT) has poor intrinsic soft tissue contrast, limiting evaluation of intra-abdominal structures. Using standard intravascular-extracellular intravenous contrast (IE-IV) alone is theoretically limited by long acquisition times of traditional small animal scanners that may result in equilibration. We assessed whether a negative contrast strategy of enhancing normal tissue surrounding tumor, instead of the tumor itself, can visualize and quantify intraperitoneal (IP) cancer in a mouse model. Two and a half weeks after IP injection of Hey A8 cells, four groups of three animals each were administered serial dilutions of IV Fenestra LC (RES-IV), oral Gastroview, and IP Optiray 320. Another group of three animals was administered IV Optiray 320 (IE-IV), oral Gastroview, and IP Optiray 320 in successive combinations. Both groups were imaged by CT. Tumor and organ Hounsfield units were measured, and visualization was assessed. With increasing contrast amount, the Hounsfield unit of organs generally increased, whereas that of tumor remained essentially stable. The visualization of abdominal organs and tumor also generally increased with increasing contrast amount. Visualization of tumor and its margins adjacent to liver, spleen, and stomach was significantly better on administering RES-IV. However, for tumor adjacent to bladder, both IE-IV and RES-IV were equivalent. In vivo CT-derived tumor weights correlated highly with ex vivo tumor weights (r = 0.96, P < .0001, n = 15). Thus, CT using negative contrast enhancement strategy allows visualization and quantification of IP tumors. Such a strategy will also enable anatomic localization of functional signal for combination/molecular imaging.
Source: Translational Oncology
DOI: 10.1593/tlo.08199
Institution: The University of Texas MD Anderson Cancer Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,390
Funder: National Cancer Institute
OpenAlex ID: W1985376453
