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  • The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA)

Summary

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.

Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name "CPTAC-cancertype".  Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.  For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow.  

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable.  There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term "discovery proteomics" is in reference to "untargeted" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term “targeted proteomics” refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo.  Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page. 

CPTAC Imaging Special Interest Group

You can join the CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!  Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars page.


Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • Beaumont Health System, Royal Oak, MI - Special thanks to George D. Wilson, PhD from the Department of Radiation Oncology Research, Barbara Pruetz of the Biobank, Debra Kapczynski, MHSA, CIIP, RT(R)(CT) and Rachel Deyer from the Department of Diagnostic Radiology.
  • Boston Medical Center, Boston, MA - Special thanks to Chris D. Andry M.Phil, PhD from the Department of Pathology and Laboratory Medicine,  Margaret Lavoye, Artem Kaliaev, Wilson Chavez, Stephan Anderson, Jorge Soto, and Mitchell Horn from the Department of Radiology, Elizabeth Duffy, MA and Cheryl Spencer, MA of the Biobank.
  • International Institute for Molecular Oncology, Poznań, Poland - Special thanks to Maciej Wiznerowicz MD, PhD and Jan Lubiński MD PhDMarek Durlik MD, PhD, and Magdalena Derejska MD, PhD. from Central Clinical Hospital of the Ministry of Interior in Warsaw; and Katarzyna Kuśnierz MD, PhD, Sergiusz Nawrocki MD, PhD and Pawel Lampe MD, PhD from University Clinical Center in Katowice, Poland
  • St. Joseph's Hospital and Medical Center, Phoenix, AZ - Special thanks to Jennifer Eschbacher, MD from the Department of Neuropathology, Catherine Seiler, PhDRosy Singh and Beth Hermes from the Biobank Core Facility, and Victor Sisneros, RT(R)(CT), CPSA.
  • University of Calgary, Alberta, Canada - Special thanks to Oliver Bathe, MD, FRCS(C) from the Departments of Surgery/Oncology, Marina Salluzzi, PhD and Nicole Blenkin from the Department of Radiology, Calgary Image Processing and Analysis Centre (CIPAC), and Jennifer Koziak from the Department of Surgery.
  • Cureline, Inc. team and clinical network, Brisbane, CA - Special thanks to Olga Potapova, Ph.D., Vladislav Golubkov, Ph.D.Victoria Fulidou, M.D.Alexander Sviridov,  Dmitry Belyaev, M.D., Oxana Paklina, M.D., Dr.Sc., Galiya Setdikova, M.D., Ph.D.,  Denis Golbin, M.D., Ph.D.


Data Access

Data TypeDownload all or Query/FilterLicense
Images (DICOM, 50.8 GB)


   

(Download requires the NBIA Data Retriever)

Tissue Slide Images (SVS, 88 GB)

Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

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.

Third Party Analyses of this Dataset

TCIA encourages the community to  publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

Detailed Description


Radiology Image Statistics

Pathology Image Statistics

Modalities

CR, CT, MR, PT, RF, US, XA

Pathology

Number of Participants

98

168

Number of Studies

130

N/A

Number of Series

966

N/A

Number of Images

105,546

557
Images Size (GB)

50.79

88

Accessing CPTAC publication cohorts

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicableIn the case of CPTAC-PDA there was a "Discovery Cohort" release.  Images associated with these cases can be downloaded using the following links:

Accessing the Proteomic & Genomic Clinical Data

To access/download the clinical data on the Proteomic Data Commons (PDC) and Genomic Data Commons (GDC)once you have identified the data of your interest, move to the 'Clinical' tab on the browse page. Select the checkbox to select a specific row, all rows on the page or all pages and click the export clinical manifest button in CSV or TSV format on the GDC, or TSV or JSON format on the PDC.

A Note about TCIA and CPTAC Subject Identifiers and Dates

Subject Identifiers: 

A subject with radiology and pathology images stored in TCIA is identified with a de-identified project Patient ID that is identical to the Patient ID of the same subject with clinical, proteomic, and/or genomic data stored in other CPTAC databases and web sites.

Dates: 

The radiology imaging data is in DICOM format. To provide temporal context information aligned with events in the clinical data set for each patient, TCIA has inserted information in DICOM tag (0012,0050) Clinical Trial Time Point ID. This DICOM tag contains the number of days from the date the patient was initially diagnosed pathologically with the disease to the date of the scan. E.g. a scan acquired 3 days before the diagnosis would contain the value -3. A follow up scan acquired 90 days after diagnosis would contain the value 90.

The DICOM date tags (i.e. birth dates, imaging study dates, etc.) are modified per TCIA's standard process which offsets them by a random number of days. The offset is a number of days between 3 and 10 years prior to the real date that is consistent for each TCIA image-submitting site and collection, but that varies among sites and among collections from the same site. Thus, the number of days between a subject’s longitudinal imaging studies are accurately preserved when more than one study has been archived while still meeting HIPAA requirements.

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

National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA) (Version 11) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.SC20FO18

Acknowledgement

The CPTAC program requests that publications using data from this program include the following statement: “Data used in this publication were generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC).”

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

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.

Version 11: 2021/02/04

Data TypeDownload all or Query/Filter
Images (DICOM, 50.8 GB)

 

Tissue Slide Images (SVS, 88 GB)

download pathology slide images  

Clinical Data API (JSON -  more info
Proteomics (web)

Version 10 : 2020/09/03

Data TypeDownload all or Query/Filter
Images (DICOM, 37 GB)

  

Tissue Slide Images (SVS, 88 GB)

download pathology slide images  

Clinical Data API (JSON -  more info
Proteomics (web)

Changed to new Aspera download link for histopathology slides.

Version 9: Updated 2020/03/31

Data TypeDownload all or Query/Filter
Images (DICOM, 37 GB)

 

Tissue Slide Images (SVS, 88 GB)

download pathology slide images  

Clinical Data API (JSON -  more info
Proteomics (web)

Added 6 radiology subjects

Version 8: Updated 2019/12/16

Data TypeDownload all or Query/Filter
Images (DICOM, 33.5 GB)

 

(Requires NBIA Data Retriever .)

Tissue Slide Images (SVS, 88 GB)

download pathology slide images  

Clinical Data (web)

Coming soon

Proteomics (web)

Added 1 pathology and 6 new radiology subjects

Version 7 : Updated 2019/09/30

Data TypeDownload all or Query/Filter
Images (DICOM, 31.8 GB)

 

(Requires NBIA Data Retriever .)

Tissue Slide Images (SVS, 88 GB)

download pathology slide images  

Clinical Data (web)

Coming soon

Proteomics (web)

Added new subjects.

Version 6: Updated 2019/06/30

Data TypeDownload all or Query/Filter
Images (DICOM, 25.6 GB)

 

(Requires NBIA Data Retriever .)

Tissue Slide Images (SVS, 86.4 GB)

download pathology slide images  

Clinical Data (web)

Coming soon

Proteomics (web)

Version 5 : Updated 2019/03/31

Data TypeDownload all or Query/Filter
Images (DICOM, 23.8 GB)

 

(Requires NBIA Data Retriever .)

Tissue Slide Images (web)
Clinical Data (web)

Coming soon

Proteomics (web)

Added new subjects.

Version 4 : Updated 2018/10/24

Data TypeDownload all or Query/Filter
Images (DICOM, 21.8 GB)

 

(Requires NBIA Data Retriever .)

Tissue Slide Images (web)
Proteomics (web)

Added new subjects.

Version 3: Updated 2018/06/30

Data TypeDownload all or Query/Filter
Images (DICOM, 21.8 GB)

(Requires NBIA Data Retriever .)

Tissue Slide Images (web)
Proteomics (web)

Added new subjects.

Version 2: Updated 2018/04/26

Data TypeDownload all or Query/Filter
Images (DICOM, 21.4 GB)

(Requires NBIA Data Retriever .)

Tissue Slide Images (web)
Proteomics (web)

Added new subjects.

Version 1: Updated 2018/01/10

Data TypeDownload all or Query/Filter
Images (DICOM, 4.38 GB)

(Requires NBIA Data Retriever .)

Tissue Slide Images (web)
Proteomics (web)


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