Abstract No. 568 Volumetric CT Assessment of In Situ Induced Hepatic Lesions of an Oncopig Model

Quantitatively characterize the growth rate of in situ induced hepatic lesions generated in an oncopig model. Oncopigs are genetically modified immunocompetent pigs with mutations in KRAS and P53 (oncogenic KRASG12D and dominant-negative TP53R167H) that can subsequently be activated via administration of an AdCre virus. Prior to lesion induction, oncopigs (n=9) received a baseline triple phase CT scan. Lesions were then induced through an AdCre in Gelfoam slurry delivered at up to 4 sites in the liver. Weekly triple phase CT scans were obtained to monitor the growth of the lesions. Animals were sacrificed at 14, 21, or 28 days (n=3 each group). Tissues were collected for pathology and to guide image segmentation of CT data. Venous phase CT data was loaded into 3D slicer to manually segment the lesions and volumes were calculated. The resulting data was fit to a lesion growth model and optimized in MATLAB using lsqnonlin. All procedures involving animals were reviewed and approved by the Institutional Animal Care and Use Committee. Lesions presented as hypovascular, low attenuating masses with mild contrast enhancement around the margins, but little to no enhancement within the lesions themselves. The overall success rate of lesion generation was ∼78%. However, in the 28-day group several lesions observed at earlier timepoints had fully regressed before necropsy. The overall trend for lesion growth was increasing volumes through day 21 at a linear rate of ∼223.76 mm3 / day and regression from day 21 to day 28 at a linear rate of ∼573.1 mm3 / day (Table 568.1). The rate of growth of the lesions varied widely but general trends remained relatively consistent within a single oncopig. The lesions demonstrated an overall trend of rapid growth for the first 21 days with spontaneous regression of the lesions being observed from day 21 to 28. Preliminary pathology suggests that these lesions are highly inflammatory and composed of a large leukocyte population, which might be contributing to this phenomenon. The CT volumetric data generated from this study, and resulting mathematical model, can be utilized for future work in computational models to test new treatment methods, or optimize existing therapies.