Contrast-enhanced susceptibility weighted imaging with ultrasmall superparamagnetic iron oxide improves the detection of tumor vascularity in a hepatocellular carcinoma nude mouse model

Autor: Shuohui Yang, Hongchen Gu, Caixia Fu, Fang Lu, Zhi-hong Han, Yuan-yuan Dai, Feng-lin Hu, Jiang Lin
Rok vydání: 2016
Předmět:
Zdroj: Journal of Magnetic Resonance Imaging. 44:288-295
ISSN: 1053-1807
DOI: 10.1002/jmri.25167
Popis: Purpose To evaluate the effectiveness of contrast-enhanced susceptibility-weighted imaging with ultrasmall superparamagnetic iron oxide (USPIO-enhanced SWI) in the assessment of intratumoral vascularity in hepatocellular carcinoma (HCC). Materials and Methods Orthotopic xenograft HCC nude mouse models were established first and magnetic resonance imaging (MRI) examinations were performed on a 1.5T MR scanner 28 days later. Three groups of mice, 10 in each, were imaged using unenhanced and USPIO-enhanced SWI at doses of 4, 8, and 12 mg Fe/kg. Intratumoral susceptibility signal intensity (ITSS) was scored. ITSS-to-tumor contrast-to-noise ratio (ITSST-CNR) was measured. These measurements were compared between unenhanced and USPIO-enhanced SWI at each dose and differences in the measurements between different dose groups were estimated. Correlation between ITSS and tumor microvessel density (MVD) was analyzed. Results Compared with unenhanced SWI, significantly higher ITSS was identified on USPIO-enhanced SWI at doses of 8 mg Fe/kg (Z = –2.000, P = 0.046) and 12 mg Fe/kg (Z = –2.333, P = 0.020). Significantly higher ITSST-CNR was found on USPIO-enhanced SWI than that on unenhanced SWI (P < 0.05). Significantly higher ITSST-CNR at a dose of 8 mg Fe/kg was observed than that at 4 mg Fe/kg (Z = –3.326, P = 0.001). Positive correlation between ITSS on USPIO-enhanced SWI at a dose of 8 mg Fe/kg and tumor MVD was demonstrated (r = 0.817, P = 0.004). Conclusion USPIO-enhanced SWI at a dose of 8 mg Fe/kg greatly improves the detection of intratumoral vascularity in a xenograft HCC model. J. Magn. Reson. Imaging 2016.
Databáze: OpenAIRE