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Description

At the time of our study, 108 cases with breast MRI data were available in the TCGA-BRCA collection. In order to minimize variations in image quality across the multi-institutional cases we included only breast MRI studies acquired on GE 1.5 Tesla magnet strength scanners (GE Medical Systems, Milwaukee,Wisconsin, USA) scanners, yielding a total of 93 cases. We then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the TCGA Data Portal (8 patients). After these criteria, a dataset of 84 breast cancer patients resulted, with MRIs from four institutions: Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center, and the Roswell Park Cancer Institute. The resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. The dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. Of these, 73 (87%) were ER+, 67 (80%) were PR+, and 19 (23%) were HER2+.  Various types of analyses were conducted using the combined imaging, genomic, and clinical data.  Those analyses are described within several manuscripts created by the group (cited below).


Data Access

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever

Data TypeDownload all or Query/Filter
Images (DICOM)

Annotations (XLS)

Segmentations (ZIP, XLS)

3D SegmentationsQuantitative Radiomics

Multi-gene Assays (XLS)

Clinical Data (XLS)

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description

  • 3D Lesion Segmentations & Quantitative Radiomic Features
    • 3D Segmentations 

      • Readme instructions

      • Note: With regards to the naming structure, *S2-1.les: S2 means DCE-MRI sequence 2, lesion #1. Sometimes, there are multiple DCE-MRI sequences on TCIA data, and so the team used the sequence that corresponded to the one on which the radiologists annotated the truth.
    • Quantitative Radiomics

      • Note: please reference these data extracted using version V2010 of the UChicago MRI Quantitative Radiomics workstation
  • Multi-gene assays include MammaPrint, Oncotype DX, and PAM50
  • TCGA Clinical Data comes from TCGA Data Portal, archived in case of subsequent updates made by TCGA

Citations & Data Usage Policy 

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

Morris, Elizabeth, Burnside, Elizabeth, Whitman, Gary, Zuley, Margarita, Bonaccio, Ermelinda, Ganott, Marie, … Giger, Maryellen L. (2014). Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014.8SIPIY6G

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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Publication Citation

  • Guo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, Morris E, Burnside E, Whitman G, Giger ML*, Ji Y*:  Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.  J Medical Imaging 2(4), 041007 (Oct-Dec 2015). doi: 10.1117/1.JMI.2.4.041007

Publication Citation

  • Burnside E, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton E, Brandt K, Whitman G, Conzen S, Lan L, Ji Y, Zhu Y, Jaffe C, Huang E, Freymann J, Kirby J, Morris EA*, Giger ML*:  Using computer-extracted image phenotypes from tumors on breast MRI to predict breast cancer pathologic stage. Cancer doi: 10.1002/cncr.29791, 2015.

Publication Citation

  • Zhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML*, Ji Y*:  Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma.  Nature – Scientific Reports 5:17787. doi: 10.1038/srep17787, 2015.

Publication Citation

  • Li H, Zhu Y, Burnside ES, …. Perou CM, Ji Y*, Giger ML*:  MRI radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of gene assays of MammaPrint, Oncotype DX, and PAM50.  Radiology. doi: 10.1148/radiol.2016152110, 2016.

Publication Citation

  • Li H, Zhu Y, Burnside ES, …. Perou CM, Ji Y, Giger ML:  Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA Dataset. npj Breast Cancer (2016) 2, 16012; doi:10.1038/npjbcancer.2016.12; published online 11 May 2016.

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 1 (Current): 2019/02/20

Data TypeDownload all or Query/Filter
Images (DICOM)

Annotations (XLS)

Segmentations (ZIP, XLS)

3D SegmentationsQuantitative Radiomics

Multi-gene Assays (XLS)

Clinical Data (XLS)

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