- Created by Tracy Nolan, last modified by Brenda Fevrier-Sullivan on May 23, 2023
This collection contains subjects from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium Acute Myeloid Leukemia (CPTAC-AML) 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.
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Boston Medical Center, Boston, MA - Special thanks to Chris D. Andry M.Phil, PhD from the Department of Pathology and Laboratory Medicine, Margaret Lavoye from the Department of Radiology, Elizabeth Duffy, MA and Cheryl Spencer, MA of the Biobank.
- University of Calgary, Alberta, Canada - Special thanks to 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.
- International Institute for Molecular Oncology, Poznań, Poland - Special thanks to Maciej Wiznerowicz MD, PhD and Jan Lubiński MD PhD.
- 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).
- St. Joseph's Hospital and Medical Center, Phoenix, AZ - Special thanks to Jennifer Eschbacher, MD from the Department of Neuropathology, Catherine Seiler, PhD, Rosy Singh and Beth Hermes from the Biobank Core Facility, and Victor Sisneros, RT(R)(CT), CPSA.
- 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.
- 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.
- 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.
- H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL - Special thanks to John Koomen, Ph.D. from the Department of Molecular Oncology, and Edward R. Seijo, MS and Olya Stringfield, PhD.
|Data Type||Download all or Query/Filter||License|
|Tissue Slide Images (SVS, 378 GB)|
(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)
|Additional data elements (CSV)|
Click the Versions tab for more info about data releases.
Additional Resources for this DatasetThe 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.
- Imaging Data Commons (IDC) (Imaging Data)
Pathology Image Statistics
Number of Participants
Number of Images
|Images Size (GB)||378|
A Note about TCIA and CPTAC Subject Identifiers and Dates
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.
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:
National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2019). The Clinical Proteomic Tumor Analysis Consortium Acute Myeloid Leukemia Collection (CPTAC-AML) (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.B6FOE619
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).”
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 that leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
Version 4 (Current): 2020/09/03
Changed to new Aspera download link for histopathology slides.
Version 3: 2020/04/28
Added 33 pathology subjects & 50 pathology images. Future pathology data releases will include ONLY fully qualified cases.
Version 2: 2019/12/03
Added Pathology Subjects
Version 1: 2019/01/02
Added this descriptor for pathology slides. At this time there are no DICOM radiological imaging files.
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