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  • Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations (Duke-Breast-Cancer-MRI)
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Summary

Breast MRI is a common image modality to assess extent of disease in breast cancer patients. Recent studies show that MRI has a potential in prognosis of patient sort and long term outcomes as well as predicting pathological and genomic features of the tumors. However, large, well annotated datasets are needed to make further progress in the field. In this dataset, we share MRI imaging and other data for 922 patients with invasive breast cancer. Their prone position axial breast MRI images were acquired by 1.5T or 3T scanners. Following MRI sequences are shared: a non-fat saturated T1-weighted sequence, a fat-saturated gradient echo T1-weighted pre-contrast sequence, and mostly three to four post-contrast sequences.

The images are associated with a variety of metadata. Specifically, we are sharing:

  1. Demographic, clinical, pathology, treatment, outcomes, and genomic data. This data has been collected from a variety of sources including clinical notes and has served as a source for multiple published papers on radiogenomics, outcomes prediction, and other.
  2. 529 imaging features which represent a variety of imaging characteristics including size, shape, texture, and enhancement of both the tumor and the surrounding tissue, which is combined of features commonly published in the literature, as well as the features developed in our lab. These features were used in our previous paper (https://doi.org/10.1016/j.eswa.2017.06.029)
  3. Annotation boxes provided by radiologists that indicate locations of the lesions in the images.

For more information see this site https://sites.duke.edu/mazurowski/resources/breast-cancer-mri-dataset/, the related publication, or contact TCIA Helpdesk at help@cancerimagingarchive.net



Data Access

Click the  Download button to save to your local storage. Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents.

Data TypeDownload all or Query/Filter
Images (DICOM, 368.4 GB)

   

(Download requires the NBIA Data Retriever)

File Path mapping tables (XLSX, 49.6 MB)
Clinical and Other Features (XLSX, 582 kB)
Annotation Boxes (XLSX, 49 kB)  


Imaging features (XLSX, 6.44 MB)

 


Click the Versions tab for more info about data releases.

Detailed Description

Image Statistics


Modalities

MR

Number of Participants

922

Number of Studies

922

Number of Series

5,034

Number of Images

773,126

Images Size (GB)368.4

Users please note this caveat about DICOM tag (0020,0052) Frame Of Reference UID:  This DICOM tag was lost during deidentification and curation, and has been replaced with a Dummy value per study that may not be reliable for image alignment.

Citations & Data Usage Policy


Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to the following citations: 

Data Citation

Saha, A., Harowicz, M. R., Grimm, L. J., Weng, J., Cain, E. H., Kim, C. E., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2021). Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.e3sv-re93

Publication Citation

Saha, A., Harowicz, M. R., Grimm, L. J., Kim, C. E., Ghate, S. V., Walsh, R., & Mazurowski, M. A. (2018). A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. British journal of cancer, 119(4), 508-516. DOI: https://doi.org/10.1038/s41416-018-0185-8

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. Journal of Digital Imaging, 26(6), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

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

Version 1 (Current): Updated 2021/04/06

Data TypeDownload all or Query/Filter
Images (DICOM, 368.4 GB)

   

(Download requires the NBIA Data Retriever)

File Path mapping tables (XLSX, 49.6 MB)
Clinical and Other Features (XLSX, 582 kB)
Annotation Boxes (XLSX, 49 kB)  


Imaging features (XLSX, 6.44 MB)

 


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