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title | Data Access |
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| Data Access
Data Type | Download all or Query/Filter | License |
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Radiologist Annotations (XLS) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19039112/tcga%20breast%20radiologist%20reads.xls?api=v2 |
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| | Segmentations (ZIP, XLS) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19039112/TCGA_Segmented_Lesions_UofC.zip?api=v2 |
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| | Quantitative Radiomic Features |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19039112/TCGA%20Run%202014_91cases_features_UChicago%20V2010%20MRI%20Workstation.xls?api=v2 |
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| | MammaPrint, Oncotype DX, and PAM50 Multi-gene Assays (XLS) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19039112/Perou%20TCGA%20BRCA%20MRIs%2BPAM50%2BGHI21%2BNKI70%20MAILED.xlsx?api=v2 |
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| | Clinical Data (XLS) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19039112/brca-clinicalforwiki.xls?api=v2 |
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Click the Versions tab for more info about data releases.
Collections Used in this Third Party AnalysisBelow is a list of the Collections used in these analyses: Source Data Type | Download all or Query/Filter | License |
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Corresponding Original Images from TCGA-BRCA (DICOM, 52GB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/19039112/doiJNLP-I50pw3Gc.tcia?api=v2 |
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(Open with the NBIA Data Retriever ) | |
Please contact help@cancerimagingarchive.net with any questions regarding usage. |
Localtab |
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title | Detailed Description |
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| Detailed DescriptionHow to use the Segmentations 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. Each of our tumor segmentation files is a binary file, consisting of the following format: 1. six uint16 values for the inclusive coordinates of the lesion’s cuboid , relative to the image: y_start y_end x_start x_end z_start z_end 2. the N int8 on/off voxels (0 or 1) for the above specified cube, where N = (y_end y_start +1) * (x_end - x_start + 1) * (z_end - z_start + 1). A voxel value of 1 denotes that it is part of the lesion, while a value of zero denotes it is not. Please reference these data extracted using version V2010 of the UChicago MRI Quantitative Radiomics workstation. |
Localtab |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia limited license policy |
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| Morris, E., Burnside, E., Whitman, G., Zuley, M., Bonaccio, E., Ganott, M., Sutton, E., Net, J., Brandt, K., Li, H., Drukker, K., Perou, C., & Giger, M. L. (2014). Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2014.8SIPIY6G |
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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. (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 |
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:
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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* Y*: , Y., & TCGA Breast Phenotype Research Group. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. |
| J Journal of Medical Imaging, 2(4), 041007 |
| (Oct-Dec 2015).
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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 |
| **:. (2016) Using computer-extracted image phenotypes from tumors on breast MRI to predict breast cancer pathologic stage. Cancer |
| doi, 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, |
| …. Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y |
| **: MRI . (2016) MR Imaging 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: http://dx.doi.org/, 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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