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

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locationhttps://www.cancerimagingarchive.net/collection/pdmr-texture-analysis/


PDX Model Characterizations and Biweekly imaging sessions 

Biweekly imaging session Characterization 

Excerpt
Image Added

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

models

model (below

tables

table) 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



 

 

Characterization

1

2

3

4

5

6

7

8

Patient ID

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

cancer, NOS

Endocrine and Neuroendocrine

* Liver

2/14/2020

4

M

8

8

5

5

5

5



146476-266-R

Urothelial/

Bladder Cancer

bladder cancer, NOS

Genitourinary

Bladder

2/3/2020

4

M

17

16

13

10

11

4

5

4


165739-295-R

Adenocarcinoma-pancreas

Digestive/Gastrointestinal

Pancreas

5/4/2018

2

M

10

10

1






172845-121-T

Adenocarcinoma-

Colon

colon

Digestive/Gastrointestinal

* Liver

10/16/2020

4

F

20

20







172845-142-T

Adenocarcinoma-

Colon

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

5

4

3

2

2

1





521955-158-R4

Adenocarcinoma-

Pancreas

pancreas

Digestive/Gastrointestinal

* Tumor in colonic fat

9/30/2021

4

F

10

10

10

8

5

1

1


521955-158-R6

Adenocarcinoma-

Pancreas

pancreas

Digestive/Gastrointestinal

* Myometrium

3/27/2018

N/A

F

7

7

4






625472-104-R  

Adenocarcinoma-

Colon

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

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

cell carcinoma-anus

Digestive/Gastrointestinal

Buttock

2/25/2022

5

F

6

6

6

1

1




997537-175-T

Adenocarcinoma-

Colon

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

mm3) from the NCI/DCTD PDMR repository

are

were implanted into 5-10 donor mice (NOD.Cg-

PrkdcscidIl2rgtm1Wjl

PrkdcscidIl2rgtm1Wjl/SzJ (NSG)). When tumors

reach

reached enrollment criteria (100 – 300

mm3

mm3), tumors

are

were excised, cut into 2x2x2

mm3 fragments

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

Tomography. 2021 Feb 5;7(1):1-9. doi: 10.3390/tomography7010001. eCollection 2021 Mar. PMID: 33681459

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.

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

the 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.


Localtab Group


Localtab
activetrue
titleData Access

Data Access

Data TypeDownload all or Query/FilterLicense

Images (DICOM,

XX

11.

X

8 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/PDMR-Texture-Analysis.tcia?api=v2



tcia-button-generator
labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-Texture-Analysis



(Download requires NBIA Data Retriever)

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Standard Operating Procedure 50101: MRI T2 Weighted Non-Contrast Protocol Single Mouse Pulmonary Gated and Multi-Mouse Non-Gated (PDF, 141 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/SOP50101_MRI%20T2%20Weighted%20Non-Contrast%20Protocol_Single%20Mouse%20Pulmonary%20Gated%20and%20Multi-Mouse%20Non-Gated.pdf?api=v2



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Standard Operating Procedure 50101: Tumor_Implantation_PDX (PDF, 328 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/SOP50101_Tumor_Implantation_PDX.pdf?api=v2



Tcia cc by 4

PDX Model Characterizations (XLSX, 27 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/PDX%20stats_V2.xlsx?api=v2



Tcia cc by 4

Data related to specific models in the collection at NCI Patient-Derived Models Repository PDMR (DOCX, 16 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/PDMR-LINKS%2020230607.docx?api=v2



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Click the Versions tab for more info about data releases.

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.

  

(IDC) (Imaging Data)



Localtab
titleDetailed Description

Detailed Description

Image Statistics

Radiology Image StatisticsPathology Image Statistics

Modalities

MR, SRModalities

Number of Patients

175

Number of Studies

689

Number of Series

1203

Number of Images

19,343

Images Size (GB)11.8

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.


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia limited license policy

Info
titleData Citation

DOI goes here. Create using Datacite with information from Collection Approval formKalen, 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


Info
titleTCIA 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

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


Localtab
titleVersions

Version

X

1 (Current): Updated 2023/

mm

06/

dd

14

Data TypeDownload all or Query/FilterLicense

Images (DICOM,

XX

11.

X

8 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/PDMR-Texture-Analysis.tcia?api=v2



Tcia button generator
labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-Texture-Analysis



(Download

requires 

requires NBIA Data Retriever)

Tcia cc by 4

Standard Operating Procedure 50101: MRI T2 Weighted Non-Contrast Protocol Single Mouse Pulmonary Gated and Multi-Mouse Non-Gated (PDF, 141 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/SOP50101_MRI%20T2%20Weighted%20Non-Contrast%20Protocol_Single%20Mouse%20Pulmonary%20Gated%20and%20Multi-Mouse%20Non-Gated.pdf?api=v2



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Standard Operating Procedure 50101: Tumor_Implantation_PDX (PDF, 328 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/SOP50101_Tumor_Implantation_PDX.pdf?api=v2



Tcia cc by 4

PDX Model Characterizations (XLSX, 27 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/PDX%20stats_V2.xlsx?api=v2



Tcia cc by 4

Data related to specific models in the collection at NCI Patient-Derived Models Repository PDMR (DOCX, 16 KB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/145752799/PDMR-LINKS%2020230607.docx?api=v2
the NBIA Data Retriever)



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