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:
- 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.
- 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)
- 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
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Data Type | Download all or Query/Filter |
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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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Detailed Description
Image Statistics | |
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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
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
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
Source Publication
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 Type | Download 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) |
Added new subjects.