The Animal Models and Pathology Specialized Resource (Core D) of the Emory Molecular and Translational Imaging Center (EMTIC) will assist the overall project by providing service in two critical areas: animal models and histopathologic services. The Core is necessary for the success of the EMTIC and is highly integrative, being utilized by all four research projects and all five pilot projects. The animal models component will act to generate and characterize novel rodent models for molecular imaging in cancer research and will assist the individual projects by collecting and analyzing tumor data from these animals. The production and analysis of relevant animal models of cancer is central to the EMTIC because of the heavy emphasis on the development and validation of novel tracers in preclinical studies that could be utilized as markers for tumor detection, progression, and dissemination in vivo. The animal component will also serve as a repository for the preservation and distribution of any novel transgenic strains of mice that may be utilized in the Research Projects and Pilot Projects as they become necessary. The Core will perform necropsy and tissue collection for experimental animals, and will collect tumor samples from selected animals for generation of cell lines for use by EMTIC investigators. The pathology component of the Core will provide tissue and tumor procurement expertise, tissue processing, as well as histological and immunohistochemical analysis of tumor samples from human disease and animal models. Histologic and immunohistochemical evaluation of tumors from patients and animal models will be necessary in order to correlate and validate the detection of novel tracers by PET, MRI and optical methods with the targeted biomarkers in tissues. Thus the Animal Models and Pathology Core will contribute significantly to the overall goals of the EMTIC from a scientific perspective by providing these services as well as expertise. In addition, the inclusion of these services into a comprehensive core component will provide additional benefit to EMTIC as a whole, through consolidation of effort, avoidance of unnecessary experimental duplication, and by drawing upon the collective expertise of the Core personnel.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Specialized Center (P50)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Emory University
United States
Zip Code
Akin-Akintayo, Oladunni; Tade, Funmilayo; Mittal, Pardeep et al. (2018) Prospective evaluation of fluciclovine (18F) PET-CT and MRI in detection of recurrent prostate cancer in non-prostatectomy patients. Eur J Radiol 102:1-8
Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng et al. (2017) A supervoxel-based segmentation method for prostate MR images. Med Phys 44:558-569
Dance, David R; Sechopoulos, Ioannis (2016) Dosimetry in x-ray-based breast imaging. Phys Med Biol 61:R271-R304
Liu, Lizhi; Tian, Zhiqiang; Zhang, Zhenfeng et al. (2016) Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications. Acad Radiol 23:1024-46
Orza, Anamaria; Yang, Yi; Feng, Ting et al. (2016) A nanocomposite of Au-AgI core/shell dimer as a dual-modality contrast agent for x-ray computed tomography and photoacoustic imaging. Med Phys 43:589
Pike, Robert; Lu, Guolan; Wang, Dongsheng et al. (2016) A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging. IEEE Trans Biomed Eng 63:653-63
Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng et al. (2016) Superpixel-Based Segmentation for 3D Prostate MR Images. IEEE Trans Med Imaging 35:791-801
Schuster, David M; Nanni, Cristina; Fanti, Stefano (2016) PET Tracers Beyond FDG in Prostate Cancer. Semin Nucl Med 46:507-521
Odewole, Oluwaseun A; Oyenuga, Oyeladun A; Tade, Funmilayo et al. (2015) Reproducibility and reliability of anti-3-[ยน?F]FACBC uptake measurements in background structures and malignant lesions on follow-up PET-CT in prostate carcinoma: an exploratory analysis. Mol Imaging Biol 17:277-83
Pike, Robert; Sechopoulos, Ioannis; Fei, Baowei (2015) A minimum spanning forest based classification method for dedicated breast CT images. Med Phys 42:6190-202

Showing the most recent 10 out of 83 publications