ABSTRACT
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.
ACKNOWLEDGMENT
The authors wish to thank Dr T P Finn, who assisted in the critical review of the manuscript.
FUNDING
This study was supported by the “Ligue Genevoise Contre le Cancer et la Fondation Dr Henri Dubois-Derrière/Dinu Lipatti” and by the Swiss National Science Foundation (grants 323530-151477 and PP00P3139021). This work was supported in part by the Centre for Biomedical Imaging (CIBM) of EPFL, University of Geneva and the University Hospitals of Geneva and Lausanne and the Swiss National Science Foundation for its financial support for the PRISMA MRI (R'Equip grants: SNF No. 326030-150816).
The research leading to these results has received funding from the European Union Seventh Framework Programme NMP-2008-4.0-1, grant agreement No. 228929.
DECLARATION OF INTEREST
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.