Summary
This collection contains serial non-contrast non-gated T2w MRI of 18 patient derived xenograft cancer models (518 images) for researchers to develop algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes) for the improvement in patient care through advances in precision medicine.
Characterization of tissue using non-invasive in vivo imaging techniques is used for detection and measurement of disease burden in oncology. Researchers have developed numerous algorithms, such as neural networks, and classification techniques to improve the characterization (morphological changes) of tissue. Unfortunately, to obtain statistical significance, large datasets are a requirement in this research endeavor due to tumor heterogeneity within the same histologic classification. Pre-clinical patient derived xenograft animal models can be a significant resource by providing collections with a more homogenous tumor genome across the collection with companion genomic and pathologic characterization available (https://pdmr.cancer.gov/), allowing determination of the variability of imaging characteristics.
This dataset of a patient derived xenograft models (below tables) can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and cancer model.
Patient ID CTEP SDC Description
Biweekly imaging session Characterization
Characterization | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||
Patient ID | CTEP SDC Description | Implant Date | Passage | Gender | # Mice imaged per biweekly imaging session | |||||||
144126-210-T | Neuroendocrine Cancer, NOS | 2/14/2020 | 4 | M | 8 | 8 | 5 | 5 | 5 | 5 | ||
146476-266-R | Urothelial/Bladder Cancer, NOS | 2/3/2020 | 4 | M | 17 | 16 | 13 | 10 | 11 | 4 | 5 | |
165739-295-R | Adenocarcinoma-Pancreas | 5/4/2018 | 2 | M | 10 | 10 | 1 | |||||
172845-121-T | Adenocarcinoma-Colon | 10/16/2020 | 4 | F | 20 | 20 | ||||||
172845-142-T | Adenocarcinoma-Colon | 8/24/2018 | 3 | F | 15 | 13 | 8 | 3 | ||||
287954-098-R | Ewing sarcoma/Peripheral PNET | 3/18/2021 | 6 | M | 10 | 8 | 1 | 1 | 1 | |||
466636-057-R | Adenocarcinoma-Pancreas | 12/15/2017 | N/A | M | 5 | 5 | 3 | 2 | ||||
521955-158-R4 | Adenocarcinoma-Pancreas | 9/30/2021 | 4 | F | 10 | 10 | 10 | 8 | 5 | 1 | 1 | |
521955-158-R6 | Adenocarcinoma-Pancreas | 3/27/2018 | N/A | F | 7 | 7 | 4 | |||||
625472-104-R | Adenocarcinoma-Colon | 8/27/2019 | 2 | F | 9 | 1 | ||||||
695669-166-R | Melanoma | 4/16/2021 | 3 | M | 7 | 8 | 8 | 6 | 4 | 4 | 2 | |
698357-238-R | Osteosarcoma | 3/5/2021 | 6 | F | 7 | 4 | ||||||
765638-272-R | Squamous Cell Lung Carcinoma | 3/26/2021 | 4 | F | 7 | 8 | 5 | 3 | 1 | |||
779769-127-R | Adenocarcinoma-Rectum | 2/19/2020 | 5 | F | 5 | 5 | 5 | 5 | 3 | 4 | ||
833975-119-R | Adenocarcinoma-pancreas | 10/23/2019 | 2 | F | 12 | 12 | 11 | 7 | ||||
894883-131-R | Squamous Cell Carcinoma-Anus | 2/25/2022 | 5 | F | 6 | 6 | 6 | 1 | 1 | |||
997537-175-T | Adenocarcinoma-Colon | 10/25/2018 | 3 | M | 9 | 2 | ||||||
BL0382-F1232 | Urothelial/bladder cancer, NOS | 5/20/2020 | 4 | F | 9 | 6 | 4 | 1 | 1 |
In this study we performed non-contrast non-gated T2w MRI (SOP50101), initiated 2 weeks post implantation, and continued biweekly imaging sessions until their tumors reached a size requiring humane termination (ACUC guidance > 2 cm in any linear dimension by caliper or MRI measurement) or their clinical status required euthanasia. Fragments (2x2x2 mm3) from the NCI/DCTD PDMR repository are implanted into 5-10 donor mice (NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG)). When tumors reach enrollment criteria (100 – 300 mm3), tumors are excised, cut into 2x2x2 mm3 fragments and implanted with Matrigel (per PDMR SOP50101) into NSG study mice. The multi-mouse non-gated DICOM dataset was split according to Tomography. 2021 Feb 5;7(1):1-9. doi: 10.3390/tomography7010001. eCollection 2021 Mar. PMID: 33681459 and retained their individual mouse DICOM header information.
The genomic and pathologic characteristics of these models, which is available from the National Cancer Institute Patient-Derived Models Repository (https://pdmr.cancer.gov/), can be used in conjunction with this publicly available dataset to guide the development of algorithms for enhanced characterization of tissue for precision medicine.
This collection contains serial non-contrast T2w MRI of 18 patient derived xenograft cancer models (518 images) for researchers to developed various algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes). The genomic and pathologic characteristics of these models, which is available from the National Cancer Institute Patient-Derived Models Repository (https://pdmr.cancer.gov/), can be used in conjunction with this publicly available dataset to guide their development of algorithms for enhanced characterization of tissue for precision medicine.
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 James, 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.
Data Access
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Additional Resources for this Dataset
The National Cancer Institute (NCI) has developed a national repository of Patient-Derived Models (PDMs) comprised of patient-derived xenografts (PDXs), in vitro patient-derived tumor cell cultures (PDCs) and cancer associated fibroblasts (CAFs) as well as patient-derived organoids (PDOrg). These models serve as a resource for public-private partnerships and for academic drug discovery efforts. These PDMs are clinically-annotated with molecular information and made available in the Patient-Derived Model Repository. Data related to the specific subjects in this Collection can be found at:
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
- Imaging Data Commons (IDC) (Imaging Data)
Detailed Description
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Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
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TCIA Citation
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7
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Version X (Current): Updated 2023/mm/dd
Data Type | Download all or Query/Filter | License |
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Images (DICOM, XX.X GB) | (Download requires the NBIA Data Retriever) |