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This collection contains serial non-contrast non-gated T2w MRI of 18 patient derived xenograft cancer models (518 images. 175 mice were imaged at multiple time points (514 total studies) 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 modelsmodel (below tablestable) 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 | ||||||||||||||
Patient ID | CTEP SDC Description | PDMR Web link | ||||||||||||
144126-210-T | Neuroendocrine Cancer, NOS | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:3466,5509 | ||||||||||||
146476-266-R | Urothelial/Bladder Cancer, NOS | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:1644,2836 | ||||||||||||
165739-295-R | Adenocarcinoma-Pancreas | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:445,700 | ||||||||||||
172845-121-T | Adenocarcinoma-Colon | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:186,202 | ||||||||||||
172845-142-T | Adenocarcinoma-Colon | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:186,213 | ||||||||||||
287954-098-R | Ewing sarcoma/Peripheral PNET | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:856,1664 | ||||||||||||
466636-057-R | Adenocarcinoma-Pancreas | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:721,1330 | ||||||||||||
521955-158-R4 | Adenocarcinoma-Pancreas | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:3228,5141 | ||||||||||||
521955-158-R6 | Adenocarcinoma-Pancreas | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:3228,5143 | ||||||||||||
625472-104-R | Adenocarcinoma-Colon | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:875,1715 | ||||||||||||
695669-166-R | Melanoma | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:1106,2151 | ||||||||||||
698357-238-R | Osteosarcoma | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:1516,2696 | ||||||||||||
765638-272-R | Squamous Cell Lung Carcinoma | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:389,590 | ||||||||||||
779769-127-R | Adenocarcinoma-Rectum | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:944,1875 | ||||||||||||
833975-119-R | Adenocarcinoma-pancreas | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:236,302 | ||||||||||||
894883-131-R | Squamous Cell Carcinoma-Anus | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:951,1913 | ||||||||||||
997537-175-T | Adenocarcinoma-Colon | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:3301,5260 | ||||||||||||
BL0382-F1232 | Urothelial/bladder cancer, NOS | https://pdmdb.cancer.gov/web/apex/f?p=101:4:::NO:4:P4_PATIENTSEQNBR,P4_SPECIMENSEQNBR:12,12 |
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Characterization | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
Model ID | CTEP SDC Description | Disease Body Location | Biopsy site | Implant Date | Passage | Gender | # Mice imaged per biweekly imaging session | |||||||
Neuroendocrine |
cancer, NOS | Endocrine and Neuroendocrine | * Liver | 2/14/2020 | 4 | M | 8 | 8 | 5 | 5 | 5 | 5 | ||
Urothelial/ |
bladder cancer, NOS | Genitourinary | Bladder | 2/3/2020 | 4 | M | 17 | 16 | 13 | 10 | 11 | 4 |
4 | ||||||||||||||
Adenocarcinoma-pancreas | Digestive/Gastrointestinal | Pancreas | 5/4/2018 | 2 | M | 10 | 10 | 1 | ||||||
Adenocarcinoma- |
colon | Digestive/Gastrointestinal | * Liver | 10/16/2020 | 4 | F | 20 | 20 | ||||||
Adenocarcinoma- |
colon | Digestive/Gastrointestinal | * Liver | 8/24/2018 | 3 | F | 15 | 13 | 8 | 3 | |||||
Ewing sarcoma/Peripheral PNET | Musculoskeletal | * Pelvis | 3/18/2021 | 6 | M | 10 | 8 | 1 | 1 | 1 | ||||
Adenocarcinoma-pancreas | Digestive/Gastrointestinal | Pancreas | 12/15/2017 | N/A | M | 5 |
4 |
2 |
1 | ||||
Adenocarcinoma- |
pancreas | Digestive/Gastrointestinal | * Tumor in colonic fat | 9/30/2021 | 4 | F | 10 | 10 | 10 | 8 | 5 | 1 | 1 | |
Adenocarcinoma- |
pancreas | Digestive/Gastrointestinal | * Myometrium | 3/27/2018 | N/A | F | 7 | 7 | 4 | |||||
Adenocarcinoma- |
colon | Digestive/Gastrointestinal | * Shoulder | 8/27/2019 | 2 | F | 9 | 1 | |||||||
Melanoma | Skin | Arm | 4/16/2021 | 3 | M | 7 | 8 | 8 | 6 | 4 | 4 | 2 | 2 | |
Osteosarcoma | Musculoskeletal | Scapula | 3/5/2021 | 6 | F | 7 | 4 | |||||||
Squamous |
cell lung carcinoma | Respiratory/Thoracic | * Liver | 3/26/2021 | 4 | F | 7 | 8 | 5 | 3 | 1 | ||||
Adenocarcinoma-rectum | Digestive/Gastrointestinal | Rectum | 2/19/2020 | 5 | F | 5 | 5 | 5 | 5 | 3 | 4 | |||
Adenocarcinoma-pancreas | Digestive/Gastrointestinal | Pancreas | 10/23/2019 | 2 | F | 12 | 12 | 11 | 7 | |||||
Squamous |
cell carcinoma-anus | Digestive/Gastrointestinal | Buttock | 2/25/2022 | 5 | F | 6 | 6 | 6 | 1 | 1 | |||
Adenocarcinoma- |
colon | Digestive/Gastrointestinal | * Liver | 10/25/2018 | 3 | M | 9 | 2 | |||||||
Urothelial/bladder cancer, NOS | Genitourinary | Bladder | 5/20/2020 | 4 | F | 9 | 6 | 4 | 1 | 1 |
Note: Biopsy sites labeled with an (*) were obtained from a metastatic site. All other biopsy sites were at the primary tumor site.
In this study we performed non-contrast non-gated T2w MRI (SOP50101_MRI), 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
mm3mm3) from the NCI/DCTD PDMR repository
arewere implanted into 5-10 donor mice (NOD.Cg-
PrkdcscidIl2rgtm1WjlPrkdcscidIl2rgtm1Wjl/SzJ (NSG)). When tumors
reachreached enrollment criteria (100 – 300
mm3mm3), tumors
arewere excised, cut into 2x2x2
mm3 fragmentsmm3 fragments and implanted with Matrigel (per PDMR SOP50101_Tumor Implantation) 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: 33681459the method published in Tomography and retained their individual mouse DICOM header information.
Structured Reports (SR) were added to the dataset to include fragment implant date, CTEP description, mouse strain (NSG) and model.
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
theirthe 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 L Patel, MS, Keita Saito, PhD, Yvonne Evrard, PhD,
...
- Justin Smith, Simone Difilippantonio, PhD, Chelsea Sanders,
...
- Lai Thang, Ulrike Wagner, Yanling Liu, PhD,
...
- John B. Freymann,
...
- Justin Kirby
...
- and Brenda Fevrier-Sullivan
- 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
...
- Institution, National Institutes of Health, under Contract
...
- Number HHSN261200800001E. 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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