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CPTAC-UCEC - The Cancer Imaging Archive (TCIA)
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CPTAC-UCEC


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The Cancer Imaging Archive

CPTAC-UCEC | The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection

DOI: 10.7937/K9/TCIA.2018.3R3JUISW | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Uterus Human 250 US, CT, MR, PT, RTSTRUCT, Histopathology Corpus Endometrial Carcinoma 234.28GB Clinical, Genomics, Proteomics Public, Ongoing 2024/04/05

Summary

This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma (CPTAC-UCEC) 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.

On January 14, 2020 Emily Kawaler presented the consortium's proteogenomic analyses of the CPTAC-UCEC.  This deep dive into the UCEC genomic and proteomic datasets will help researchers better understand how they can be correlated with features derived from the imaging data. (Download the slides)

Data Access

Version 12: Updated 2024/04/05

correction to days between imaging and diagnosis for C3N-01510 no change to pixels. Added 5 caseID to radiology.

Title Data Type Format Access Points Subjects Studies Series Images License
Images US, CT, MR, PT, RTSTRUCT DICOM
Download requires NBIA Data Retriever
90 146 2,576 187,643 CC BY 3.0
Tissue Slide Histopathology Images Histopathology SVS
Download requires IBM-Aspera-Connect plugin
250 888 CC BY 3.0
Analysis Results Using This Collection
Related Datasets
Legend: Analysis Results| Collections

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.

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

National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2019). The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC) (Version 12) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.3R3JUISW

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).”

Detailed Description

Accessing CPTAC publication cohorts

All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. In the case of CPTAC-UCEC 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.

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 PhDRafal Matkowski, MD, PhD, Marcin Jędryka MD, PhD, and Andrzej Czekański MD PhD, from Lower Silesia Cancer Center in Wrocław, 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.
  • BioPartners, CA - Special thanks to Alexander Gasparian, PhD. from the Department of Drug Discovery and Biomedical Sciences, University of South Carolina College of Pharmacy, Kakhaber Zaalishvili, MD Medical Advisor and Staff Pathologist at BioPartners, LLC, Milla Gorodnia, President of BioPartners, Inc., Victoria Christensen, Global Business Development/Project Coordination Manager, Oksana Havryliuk, MD. Chief of Research department of radiodiagnostics of NCI (Ukraine), Marianna Gredil’, Director of BioPartners, LLC, and Anna Legenka Chief of the Data Department at BioPartners, LLC
  • University of Pittsburgh/UPMC, Pittsburgh, PA - Special thanks to Scott Beasley (MD, FACR) and Rose Jarosz in the Department of Radiology; Rajiv Dhir (MBBS, MBA) and Tony Green (HT (ASCP), AS) in the Department of Pathology (PBC).
  • University of Kansas Medical Center – Special thanks to Andrew K. Godwin, PhD and Rashna Madan, MBBS from the Department of Pathology and Laboratory Medicine and Monica MaysLauren DiMartino, and Alex Webster from the University of Kansas Cancer Center.

Related Publications

Publications by the Dataset Authors

The authors recommended this paper as the best source of additional information about this dataset:

  • Y. Dou, L. Katsnelson, M.A. Gritsenko, Y. Hu, B. Reva, R. Hong, Y-T. Wang, I. Kolodziejczak, R.J-H. Lu, C-F Tsai, W. Bu, W. Liu, X. Guo, E. An, R.C. Arend, J. Bavarva, L. Chen, R.K. Chu, A. Czekański, T. Davoli, E.G. Demicco, D. DeLair, K. Devereaux, S.M. Dhanasekaran, P. Dottino, B. Dover, T.L. Fillmore, McK. Foxall, C.E. Hermann, T. Hiltke, G. Hostetter, M. Jędryka, S.D. Jewell, I. Johnson, A.G. Kahn, A.T. Ku, C. Kumar-Sinha, P. Kurzawa, A.J. Lazar, R. Lazcano, J.T. Lei, Y. Li, Y. Liao, T-S.M. Lih, T-T. Lin, J.A. Martignetti, R.P. Masand, R. Matkowski, W. McKerrow, M. Mesri, Matthew E. Monroe, J. Moon, R.J. Moore, M.D. Nestor, C. Newton, T. Omelchenko, G.S. Omenn, S.H. Payne, V.A. Petyuk, A.I. Robles, H. Rodriguez, K.V. Ruggles, D. Rykunov, S.R. Savage, A.A. Schepmoes, T. Shi, Z. Shi, J. Tan, M. Taylor, M. Thiagarajan, J.M. Wang, K.K. Weitz, B. Wen, C.M. Williams, Y. Wu, M.A. Wyczalkowski, X. Yi, X.Zhang, R. Zhao, D. Mutch, A.M. Chinnaiyan, R.D. Smith, A.I. Nesvizhskii, P. Wang, M. Wiznerowicz, Li Ding, D.R. Mani, H. Zhang, M.L. Anderson, K.D. Rodland, B. Zhang, T. Liu, D. Fenyö , and the Clinical Proteomic Tumor Analysis Consortium. (2023)  Proteogenomic insights suggest druggable pathways in endometrial carcinoma. Cancer Cell. https://doi.org/10.1016/j.ccell.2023.07.007

No publications by dataset authors were found.

Publication Citation

Y. Dou, L. Katsnelson, M.A. Gritsenko, Y. Hu, B. Reva, R. Hong, Y-T. Wang, I. Kolodziejczak, R.J-H. Lu, C-F Tsai, W. Bu, W. Liu, X. Guo, E. An, R.C. Arend, J. Bavarva, L. Chen, R.K. Chu, A. Czekański, T. Davoli, E.G. Demicco, D. DeLair, K. Devereaux, S.M. Dhanasekaran, P. Dottino, B. Dover, T.L. Fillmore, McK. Foxall, C.E. Hermann, T. Hiltke, G. Hostetter, M. Jędryka, S.D. Jewell, I. Johnson, A.G. Kahn, A.T. Ku, C. Kumar-Sinha, P. Kurzawa, A.J. Lazar, R. Lazcano, J.T. Lei, Y. Li, Y. Liao, T-S.M. Lih, T-T. Lin, J.A. Martignetti, R.P. Masand, R. Matkowski, W. McKerrow, M. Mesri, Matthew E. Monroe, J. Moon, R.J. Moore, M.D. Nestor, C. Newton, T. Omelchenko, G.S. Omenn, S.H. Payne, V.A. Petyuk, A.I. Robles, H. Rodriguez, K.V. Ruggles, D. Rykunov, S.R. Savage, A.A. Schepmoes, T. Shi, Z. Shi, J. Tan, M. Taylor, M. Thiagarajan, J.M. Wang, K.K. Weitz, B. Wen, C.M. Williams, Y. Wu, M.A. Wyczalkowski, X. Yi, X.Zhang, R. Zhao, D. Mutch, A.M. Chinnaiyan, R.D. Smith, A.I. Nesvizhskii, P. Wang, M. Wiznerowicz, Li Ding, D.R. Mani, H. Zhang, M.L. Anderson, K.D. Rodland, B. Zhang, T. Liu, D. Fenyö , and the Clinical Proteomic Tumor Analysis Consortium. (2023)  Proteogenomic insights suggest druggable pathways in endometrial carcinoma. Cancer Cell. https://doi.org/10.1016/j.ccell.2023.07.007

Research Community Publications

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

Other Publications Using this Data

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

Previous Versions

Version 10: Updated 2023/02/24

Radiology modality data cleanup to remove extraneous scans.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever
Tissue Slide Images SVS
Download requires IBM-Aspera-Connect plugin

Version 9: Updated 2020/09/03

Changed to new Aspera download link for histopathology slides.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Tissue Slide Images SVS
Discovery Study Proteomics/Clinical Data EXTERNAL
Genomics/Clinical Data EXTERNAL

Version 8: Updated 2020/03/31

Added 14 radiology subjects

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Tissue Slide Images SVS

Version 7: Updated 2019/09/30

Added new subjects

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Tissue Slide Images SVS

Version 6: Updated 2019/06/30

Added Subjects

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Tissue Slide Images SVS

Version 5: Updated 2019/01/23

Added links to clinical and proteomic data from CPTAC Discovery Study (first 100 patients)

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Tissue Slide Images WEB

Version 4: Updated 2018/10/29

Added new subjects.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM

Version 3: Updated 2018/06/30

Added new subjects.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM

Version 2: Updated 2018/04/26

Added new subjects.

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM

Version 1: Updated 2018/01/10

Title Data Type Format Access Points Subjects Studies Series Images License
Images DICOM
Tissue Slide Images WEB

Version 11: Updated 2023/08/18

Added cases new DICOM imaging.

Title Data Type Format Access Points Subjects Studies Series Images License
Images US, CT, MR, PT DICOM
Download requires NBIA Data Retriever
85 140 1,871 179,471 CC BY 3.0
Tissue Slide Histopathology Images Histopathology SVS
Download requires IBM-Aspera-Connect plugin
250 888 CC BY 3.0