Dynamic Volume Assessment of Hepatocellular Carcinoma in Rat Livers Using a Clinical 3T MRI and Novel Segmentation

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.

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.

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).

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.