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TCGA-BLCA | The Cancer Genome Atlas Urothelial Bladder Carcinoma Collection

DOI: 10.7937/K9/TCIA.2016.8LNG8XDR | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Bladder Human 120 CT, CR, MR, PT, DX, Pathology Bladder Endothelial Carcinoma 58.03GB Clinical, Genomics Public, Complete 2020/05/29


The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA). 

Matched TCGA patient identifiers allow researchers to explore the TCGA/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial. 

CIP TCGA Radiology Initiative

Imaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type per the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the TCGA Bladder Phenotype Research Group.

Data Access

Version 8: Updated 2020/05/29

Updated clinical data link with latest spreadsheets from GDC. Added new biomedical spreadsheets from GDC.

Title Data Type Format Access Points Subjects Studies Series Images License
Download requires NBIA Data Retriever
120 192 1,051 111,781 CC BY 3.0

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

Kirk, S., Lee, Y., Lucchesi, F. R., Aredes, N. D., Gruszauskas, N., Catto, J., Garcia, K., Jarosz, R., Duddalwar, V., Varghese, B., Rieger-Christ, K., & Lemmerman, J. (2016). The Cancer Genome Atlas Urothelial Bladder Carcinoma Collection (TCGA-BLCA) (Version 8) [Data set]. The Cancer Imaging Archive.

Detailed Description

GDC Data Portal – Clinical and Genomic Data

The GDC Data Portal has extensive clinical and genomic data, which can be matched to the patient identifiers on the images here in TCIA.  Below is a snapshot of clinical data extracted on 1/27/2016.

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 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 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 birth dates, imaging study dates, etc.) in the Digital Imaging and Communications in Medicine (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 diagnosis. 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 diagnosis 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, and year).


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

  • University of North Carolina- Special thanks to J. Keith Smith, M.D., Ph.D. and Shanah Kirk from the Department of Radiology. 
  • Barretos Cancer Hospital, Barretos, São Paulo, Brazil – Special Thanks to Fabiano Rubião Lucchesi, MD and Natália Del Angelo Aredes
  • University of Chicago- Special thanks to Nicholas Gruszauskas, Ph.D.
  • University of Sheffield - Special thanks to James Catto, MB, ChB, PhD, FRCS from the Department of Oncology.
  • Memorial Sloan-Kettering Cancer Center, New York, NY - Special thanks to Hebert A. Vargas Alvarez, MD and Pierre Elnajjar.
  • Lahey Hospital & Medical Center, Burlington, MA - Special thanks to John Lemmerman, RT and Kimberly Reiger-Christ, PhD, Cancer Research, Sophia Gordon Cancer Center.
  • University of Southern California- Special thanks to Siamak Daneshmand, MD, from the Department of Urology and Vinay Duddalwar, MD, FRCR from the Department of Radiology.

Other Publications Using this Data

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


  • Cai, Y., Li, Y., Qiu, C., Ma, J., & Gao, X. (2019). Medical Image Retrieval Based on Convolutional Neural Network and Supervised Hashing. IEEE Access, 7, 51877-51885. doi: 
  • Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada. PMLR 121:174-192, 2020.
  • Lin, P., Wen, D.-Y., Chen, L., Li, X., Li, S.-H., Yan, H.-B., . . . Yang, H. (2019). A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma. Eur Radiol. doi:10.1007/s00330-019-06371-w
  • Moitra, D., & Mandal, R. K. (2022). Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model. Multimed Tools Appl, 1-19. doi: 
  • Moshfeghifar, F., Gholamalizadeh, T., Ferguson, Z., Schneider, T., Nielsen, M. B., Panozzo, D., . . . Erleben, K. (2022). LibHip: An open-access hip joint model repository suitable for finite element method simulation. Computer Methods and Programs in Biomedicine, 226, 107140. doi:
  • Pinto, J. R., & Tavares, J. M. R. (2017). A versatile method for bladder segmentation in computed tomography two-dimensional images under adverse conditions. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 1-8. doi:
  • Wong, J., Fong, A., McVicar, N., Smith, S., Giambattista, J., Wells, D., . . . Alexander, A. (2019). Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol, 144, 152-158. doi: 

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.


“The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network:”

Previous Versions

Version 7: Updated 2019/08/30

Added 14 new subjects of imaging data.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Tissue Slide Images WEB
Clinical Data TXT
Genomics WEB

Version 6: Updated 2017/10/30

Added 9 new subjects of imaging data.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical Data TXT
Genomics WEB

Version 5: Updated 2017/01/31

Added 6 new subjects of imaging data.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical Data TXT
Genomics WEB

Version 4: Updated 2016/08/31

Added 20 subjects’ imaging data.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical Data TXT
Genomics WEB

Version 3: Updated 2016/05/31

Added 31 new subjects of imaging data.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical Data TXT
Genomics WEB

Version 2: Updated 2016/01/27

Extracted latest release of clinical data (TXT) from the GDC Data Portal.

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical Data TXT
Genomics WEB

Version 1: Updated 2014/12/09

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Clinical Data TXT
Genomics WEB
Analysis Results Using This Collection