부산대학교 도서관

Purpose: In vivo liver cancer research commonly uses rodent models. One of the limitations of such models is the lack of accurate and reproducible endpoints for a dynamic assessment of growing tumor nodules. The aim of this study was to validate a noninvasive, true volume segmentation method using two rat hepatocellular carcinoma (HCC) models, correlating magnetic resonance imaging (MRI) with histological volume measurement, and with blood levels of α-fetoprotein. Materials and methods: We used 3T clinical MRI to quantify tumor volume with follow-up over time. Using two distinct rat HCC models, calculated MRI tumor volumes were correlated with volumes from histological sections, or with blood levels of α-fetoprotein. Eleven rats, comprising six Buffalo rats (n = 9 scans) and five Fischer rats (n = 14 tumors), were injected in the portal vein with 2.5 × 105 and 2.0 × 106 syngeneic HCC cells, respectively. Longitudinal (T1) relaxation time- and transverse (T2) relaxation time-weighted MR images were acquired. Results: The three-dimensional (3D) T1-weighted gradient echo had 0.35-mm isotropic resolution allowing accurate semi-automatic volume segmentation. 2D T2-weighted imaging provided high tumor contrast. Segmentation of combined 3D gradient echo T1-weighted images and 2D turbo spin echo T2-weighted images provided excellent correlation with histology (y = 0.866x + 0.034, R² = 0.997 p < .0001) and with α-fetoprotein (y = 0.736x + 1.077, R² = 0.976, p < .0001). There was robust inter- and intra-observer reproducibility (intra-class correlation coefficient > 0.998, p < .0001). Conclusions: We have developed a novel, noninvasive contrast imaging protocol which enables semi-automatic 3D volume quantification to analyze nonspherical tumor nodules and to follow up the growth of tumor nodules over time.