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PDMR-TEXTURE-ANALYSIS

PDMR-Texture-Analysis | Serial Non-contrast Non-gated T2w MRI Datasets of Patient-derived Xenograft Cancer Models for Development of Tissue Characterization Algorithms

DOI: 10.7937/3KQ0-YK19 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Arm, Bladder, Buttock, Colon, Liver, Myometrium, Pancreas, Rectum, Shoulder, and Scapula Mouse 175 MR, SR Adenocarcinoma of colon, Adenocarcinoma of pancreas, Adenocarcinoma of rectum, Ewing sarcoma - Peripheral PNET, Melanoma, Neuroendocrine cancer (NOS), Osteosarcoma, Squamous cell carcinoma of anus, Squamous cell carcinoma of lung, Urothelial - bladder cancer (NOS) 11.78GB Public, Complete 2023/06/14

Summary

This collection contains serial non-contrast non-gated T2w MRI of 18 patient derived xenograft cancer models. 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 model (below table) can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and cancer model.

PDX Model Characterizations and Biweekly imaging sessions 

  

 

 

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

144126-210-T

Neuroendocrine cancer, NOS

Endocrine and Neuroendocrine

* Liver

2/14/2020

4

M

8

8

5

5

5

5

  

146476-266-R

Urothelial/bladder cancer, NOS

Genitourinary

Bladder

2/3/2020

4

M

17

16

13

10

11

4

4

 

165739-295-R

Adenocarcinoma-pancreas

Digestive/Gastrointestinal

Pancreas

5/4/2018

2

M

10

10

1

     

172845-121-T

Adenocarcinoma-colon

Digestive/Gastrointestinal

* Liver

10/16/2020

4

F

20

20

      

172845-142-T

Adenocarcinoma-colon

Digestive/Gastrointestinal

* Liver

8/24/2018

3

F

15

13

8

3

    

287954-098-R

Ewing sarcoma/Peripheral PNET

Musculoskeletal

* Pelvis

3/18/2021

6

M

10

8

1

1

1

   

466636-057-R

Adenocarcinoma-pancreas

Digestive/Gastrointestinal

Pancreas

12/15/2017

N/A

M

5

4

2

1

    

521955-158-R4

Adenocarcinoma-pancreas

Digestive/Gastrointestinal

* Tumor in colonic fat

9/30/2021

4

F

10

10

10

8

5

1

1

 

521955-158-R6

Adenocarcinoma-pancreas

Digestive/Gastrointestinal

* Myometrium

3/27/2018

N/A

F

7

7

4

     

625472-104-R  

Adenocarcinoma-colon

Digestive/Gastrointestinal

* Shoulder

8/27/2019

2

F

9

1

      

695669-166-R

Melanoma

Skin

Arm

4/16/2021

3

M

7

8

8

6

4

4

2

2

698357-238-R

Osteosarcoma

Musculoskeletal

Scapula

3/5/2021

6

F

7

4

      

765638-272-R

Squamous cell lung carcinoma

Respiratory/Thoracic

* Liver

3/26/2021

4

F

7

8

5

3

1

   

779769-127-R

Adenocarcinoma-rectum

Digestive/Gastrointestinal

Rectum

2/19/2020

5

F

5

5

5

5

3

4

  

833975-119-R

Adenocarcinoma-pancreas

Digestive/Gastrointestinal

Pancreas

10/23/2019

2

F

12

12

11

7

    

894883-131-R

Squamous cell carcinoma-anus

Digestive/Gastrointestinal

Buttock

2/25/2022

5

F

6

6

6

1

1

   

997537-175-T

Adenocarcinoma-colon

Digestive/Gastrointestinal

* Liver

10/25/2018

3

M

9

2

      

BL0382-F1232

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 mm3) from the NCI/DCTD PDMR repository were implanted into 5-10 donor mice (NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG)). When tumors reached enrollment criteria (100 – 300 mm3), tumors were excised, cut into 2x2x2 mm3 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 the 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.

Data Access

Version 1: Updated 2023/06/14

Title Data Type Format Access Points Subjects Studies Series Images License
Images MR, SR DICOM
Download requires NBIA Data Retriever
175 689 1,203 19,343 CC BY 4.0
Standard Operating Procedure 50101: MRI T2 Weighted Non-Contrast Protocol Single Mouse Pulmonary Gated and Multi-Mouse Non-Gated PDF CC BY 4.0
Standard Operating Procedure 50101: Tumor_Implantation_PDX PDF CC BY 4.0
PDX Model Characterizations XLSX CC BY 4.0
Data related to specific models in the collection at NCI Patient-Derived Models Repository PDMR DOCX CC BY 4.0

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.

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Kalen, J. D., Ileva, L. V., Riffle, L. A., Keita, S., Tatum, J. L., Jacobs, P. M., Sanders, C., James, A., Difilippantonio, S., Thang, L., Hollingshead, M. G., Evrard, Y., Clunie, D. A., Miao, T., Wagner, U., Freymann, J., Kirby, J., & Doroshow, J. H. (2023). Serial Non-contrast Non-gated T2w MRI Datasets of Patient Derived Xenograft Cancer Models for Development of Tissue Characterization Algorithms (PDMR-Texture Analysis) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/3KQ0-YK19

Detailed Description

In addition to images, this collection includes Raw Data Storage SOP Class instances with MR Modality, generated by a Philips MR scanner; this data is not useful to anyone without the proprietary software to interpret it.

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.

Other Publications Using this Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you’d like to add please contact the TCIA Helpdesk.

  • Kalen, J. D., Clunie, D. A., Liu, Y., Tatum, J. L., Jacobs, P. M., Kirby, J., Freymann, J. B., Wagner, U., Smith, K. E., Suloway, C., & Doroshow, J. H. (2021). Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies. Tomography (Ann Arbor, Mich.), 7(1), 1–9. https://doi.org/10.3390/tomography7010001

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