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Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 292921-168-R. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 cm3size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.
Image Modified
The imaging characteristics of this model, which is available from the National Cancer Institute Patient-Derived Models Repository (https://pdmr.cancer.gov/), is highly favorable for preclinical research studies of metastatic disease when used in conjunction with non-contrast T2 weighted MRI.
# animals in Group | # animals that displayed metastasis in MRI and confirmed by Pathology | Pathology confirmation of MRI (primary imaging site) | Other confirmed Location (s) | 10 (non-resected) | 4 (5 mice were EU due to xenograft size prior to observation of metastases) | Lung | Lung | 10 (resected) | 10 | Lung | Lung, Kidney, Nodes, Peritoneal Wall |
Baseline PET [18F]FDG SUVmax: 1.3 ± 0.2 Baseline PET [18F]FLT SUVmax: 2.3 ± 0.6 Conclusion: Excellent metastatic model with at least 50% penetrance un-resected and 100% with planned early resection. Metastases are well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials. |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Frederick National Laboratory for Cancer Research – Special Thanks to Joseph D. Kalen, PhD, Lilia V. Ileva, MS, Lisa A Riffle, Nimit Patel, Keita Saito, PhD, Yvonne Evrard, PhD, Elijah Edmondson, DVM, PhD, Jessica Phillips, Simone Difilippantonio, PhD, Chelsea Sanders, Amy Janes, Lia Thang, Ulrike Wagner, Yanling Liu, PhD, John B. Freymann, and Justin Kirby.
Division of Cancer Therapeutics and Diagnosis/National Cancer Institute - James L. Tatum, MD, Paula M Jacobs, PhD, Melinda G. Hollingshead, DVM, and James H. Doroshow, MD
PixelMed Publishing – Special Thanks to David A. Clunie, MD
University of Arkansas for Medical Sciences – Special Thanks to Kirk E. Smith
This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
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title | Data Access |
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| Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents. Data Type | Download all or Query/Filter |
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Images (DICOM, 1.6 GB) | | PDMR Patient Specimen (external) | |
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title | Detailed Description |
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Modalities | MR | Number of Patients | 20 | Number of Studies | 89 | Number of Series | 160 | Number of Images | 2657 | Images Size (GB) | 1.6 GB |
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title | Citations & Data Usage Policy |
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| Add any special restrictions in here. These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work: Info |
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| DOI goes here. Create using pubhub with information from Collection Approval form Tatum, J. L., (https://orcid.org/0000-0002-3217-2478), Kalen, J. D., (https://orcid.org/0000-0002-7163-4604), Ileva, L. V., (https://orcid.org/0000-0001-8286-8396), Riffle, L. A., Keita, S., Patel, N., Jacobs, P. M., (https://orcid.org/0000-0002-9423-6473) Sanders, C., (https://orcid.org/0000-0001-8042-4783) James, A., Difilippantonio, S., (https://orcid.org/0000-0002-8234-1559) Thang, L., Hollingshead, M. G., (https://orcid.org/0000-0002-1207-1397) Phillips, J., Evrard, Y., Clunie, D. A, (https://orcid.org/0000-0002-2406-1145) Liu, Y., Suloway, C., (https://orcid.org/0000-0002-6710-503X) Smith, K. E, (https://orcid.org/0000-0002-8735-7576) Wagner, U., (https://orcid.org/0000-0002-3230-5058) Freymann, J. B., Kirby, J., https://orcid.org/0000-0003-3487-8922) Doroshow, J. H, (https://orcid.org/0000-0002-4463-1790 |
Info |
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| This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. |
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| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
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| Data Type | Download all or Query/Filter |
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Images (DICOM, 1.6 GB) | | PDMR Patient Specimen (external) | |
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