The TCGA-KIRC data collection is part of a larger effort to enhance the The Cancer Genome Atlas (TCGA) data set with characterized radiological images. The Cancer Imaging Program (CIP) with the cooperation of several of the TCGA tissue-contributing institutions has archived a large portion of the radiological images of the genetically-analyzed Kidney renal clear cell carcinoma (KIRC) cases.Clinical, genetic, and pathology data resides in the TCGA data portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). The data utilizes the same TCGA patient identifiers in both repositories, allowing researchers to explore the correlations between tissue genotype and radiological phenotype. This Collection within TCIA is related to the KIRC disease type within TCGA.
CIP TCGA Radiology Initiative
The Cancer Imaging Program is supporting multiple projects within the academic community to encourage cross disciplinary research which utilizes the data provided in these resources. Much more can be learned about this effort on the TCGA Renal Phenotype Research Group page.
How to retrieve the images from TCIA
Number of Patients
Number of Studies
Number of Series
Number of Images
If you are unsure how to download this Collection please view our quick guide on Searching by Collection or refer to our The Cancer Imaging Archive User's Guide for more detailed instructions on using the site.
Verbose explanations of the clinical data can be found on the Biospecimen Core Resource Clinical Data Forms linked below:
A note about TCIA and TCGA Subject Identifiers and Dates
Subject Identifiers: a subject with radiology images stored in The Cancer Imaging Archive (TCIA) is identified with a Patient ID that is identical to the Patient ID of the same subject with demographic, clinical, pathological, and/or genomic data stored in the Cancer Genome Atlas (TCGA). For each TCGA case, the baseline TCGA imaging studies found on TCIA are pre-surgical.
Dates: TCIA and TCGA handle dates differently, and there are no immediate plans to reconcile:
- TCIA Dates: dates (be they birthdates, imaging study dates, etc.) in the DICOM headers of TCIA radiology images have been offset 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.
- TCGA Dates: the patient demographic and clinical event dates are all the number of days from the index date, which is the actual date of pathologic Dx. So all the dates in the data are relative negative or positive integers, except for the “days_to_pathologic_diagnosis” value, which is 0 – the index date. The years of birth and Dx are maintained in the distributed clinical data file. The NCI retains a copy of the data with complete dates, but those data are not made available.With regard to other TCGA dates, if a date comes from a HIPAA “covered entity’s” medical record, it is turned into the relative day count from the index date. Dates like the date TCGA received the specimen or when the TCGA case report form was filled out are not such covered dates, and they will appear as real dates (month, day, year).
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Memorial Sloan-Kettering Cancer Center, New York, NY - Special thanks to Oguz Akin, MD and Pierre Elnajjar.
- University of Pittsburgh/UPMC, Pittsburgh, PA - Special thanks to Matthew Heller, MD and Rose Jarosz.
- Mayo Clinic, Rochester, MN - Special thanks to Bradley J. Erickson, MD, PhD from the Department of Radiology, Mayo Medical School.
- University of North Carolina, Chapel Hill, NC - Special thanks to J. Keith Smith, M.D.,Ph.D and Shanah Kirk.
- National Cancer Institute, Bethesda, MD - Special thanks to Marston Linehan, M.D. and Rabindra Gautam from the Urologic Oncology Branch.