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A large dataset of dynamic contrast-enhanced magnetic resonance images of the breast for breast cancer patients. Tumors annotated by radiologists. Extensive meta-data including patient demographics, treatment, outcomes, pathology, genomics.
This dataset is the dataset used in the following paper:
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title | Related article |
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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. https://doi.org/10.1038/s41416-018-0185-8 |
The dataset contains four components: (1) DICOM Images, (2) Annotation_Boxes, (3) Clinical_and_Other_Features, and (4) Imaging_Features. Breast MRI images of 922 patients, including the following sequences: fat saturated pre-contrast sequence, fat saturated post-contrast sequences, and non-fat saturated T1 weighted sequence. Tumor annotations in the form of boxes drawn by radiologists, six values in the spreadsheet represent the location of the start row, end row, start column, end column, start slice, and end slice of the box. The annotation boxes can be applied directly to pre and post sequences.
Accompanying the DICOM are two spreadsheets: a spreadsheet containing demographic, clinical, pathology, outcomes, genomic, and other information for the patients. The legend and column explanation are included in the spreadsheet (different tabs). Also, a spreadsheet that contains 529 imaging features extracted from the images. These are the features that were used in the paper listed above (Saha et al., BJR 2018).
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
- Continue with any names from additional submitting sites if collection consists of more that one.
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title | Data Access |
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| Data AccessClick 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 Type | Download all or Query/Filter |
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Images (DICOM, XX.X GB) | Tcia button generator |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?CollectionCriteria=Duke-Breast-Cancer-MRI |
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(Requires NBIA Data Retriever .) | Clinical and Other Features (XLSX, 582 kB) | | Annotation Boxes (XLSX, 49 kB) | | Imaging features (XLSX, 6.6 44 MB) | |
Click the Versions tab for more info about data releases. |
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title | Detailed Description |
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| Detailed Description
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Modalities | MR | Number of Participants | 922 | Number of Studies | 922 | Number of Series | 5,304 | Number of Images | 773,126 | Images Size (GB) | 351.3 | 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. |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage PolicyAdd any special restrictions in here. Tcia license 4 noncommercial |
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| Mazurowski M., Saha A., Harowicz M., Grimm L., Weng J. (2020) Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations [ Dataset]. The Cancer Imaging Archive. https://doi.org/ < Dataset DOI coming soon>
(no other authors seem to have an ORCiD) Saha https://orcid.org/0000-0002-7650-1720 |
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| 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
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| 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. DOI: https://doi.org/10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version 1 (Current): Updated 2020/mm12/dd03
Data Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | Image Modified Tcia button generator |
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label | Search |
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url | https://nbia.cancerimagingarchive.net/nbia-search/?CollectionCriteria=Duke-Breast-Cancer-MRI |
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(Requires NBIA Data Retriever .) | Clinical and Other Features (XLSX, 582 kB) | | Annotation Boxes (XLSX, 49 kB) | | Imaging features (XLSX, 6.6 44 MB) | |
Added new subjects. |
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