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| title | Data Access |
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| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever | Data Type | Download all or Query/Filter |
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| Images (DICOM) | | | Annotations (XLS) | | | Segmentations (ZIP, XLS) | | | Multi-gene Assays (XLS) | | | Clinical Data (XLS) | |
Please contact help@cancerimagingarchive.net with any questions regarding usage. |
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| title | Detailed Description |
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| 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
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| title | Citations & Data Usage Policy |
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| 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: | Info |
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| 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 |
| Info |
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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. (paper) |
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research: | Info |
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| title | Publication Citation |
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| - 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
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| Info |
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| title | Publication Citation |
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| - 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.
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| title | Publication Citation |
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| - 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.
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| title | Publication Citation |
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| - 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.
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| title | Publication Citation |
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| - 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.
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