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Localtab Group


Localtab
activetrue
titleData Access

Data Access


Data TypeDownload all or Query/FilterLicense
Radiologist Annotations (XLS)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/19039112/tcga%20breast%20radiologist%20reads.xls?api=v2



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Segmentations (ZIP, XLS)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/19039112/TCGA_Segmented_Lesions_UofC.zip?api=v2



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Quantitative Radiomic Features


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urlhttps://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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urlhttps://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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urlhttps://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 Analysis
Below is a list of the Collections used in these analyses:

Source Data TypeDownload all or Query/FilterLicense

Corresponding Original  Images from TCGA-BRCA (DICOM, 52G)


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urlhttps://wiki.cancerimagingarchive.net/download/attachments/19039112/doiJNLP-I50pw3Gc.tcia?api=v2



(Open with the NBIA Data Retriever )

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Please contact help@cancerimagingarchive.net  with any questions regarding usage.


Localtab
titleDetailed Description

Detailed Description

How 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
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

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Info
titleData Citation

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


Info
titleTCIA 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


In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:


Info
titlePublication Citation

Guo, W., Li, H., Zhu, Y., Lan, L., Yang, S., Drukker, K., Morris, E., Burnside, E., Whitman, G., Giger, M. L., Ji, Y., & TCGA Breast Phenotype Research Group. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. Journal of Medical Imaging, 2(4), 041007. https://doi.org/10.1117/1.jmi.2.4.041007


Info
titlePublication Citation

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. (2016)  Using computer-extracted image phenotypes from tumors on breast MRI to predict breast cancer pathologic stage. Cancer 122(5): 748-757 . DOI: 10.1002/cncr.29791


Info
titlePublication Citation

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.


Info
titlePublication Citation

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, Giger ML. (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 281(2):382-391. doi: 10.1148/radiol.2016152110


Info
titlePublication Citation

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.


Info
titleAcknowledgment

Please also include the following acknowledgement:

“The authors would like to thank the TCGA Breast Phenotype Research Group for providing the computer-extracted tumor segmentation data used in this study. The tumor segmentation data comes from the University of Chicago lab of Maryellen Giger,
whose lab members participated in the TCGA Breast Phenotype Research Group. In any presentation, poster, paper, etc, the segmentations should be identified as “Chicago Dynamic MRI Explorer 2005 Version”. We would also like to acknowledge The Cancer Imaging Archive and The Cancer Genome Atlas initiatives for making the imaging and the clinical data used in this study publicly available.”

Other Publications Using This Data

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


Localtab
titleVersions

Version 1 (Current): 2018/09/04


Data TypeDownload all or Query/Filter
Images (DICOM)

Annotations (XLS)

Segmentations (ZIP, XLS)

3D Segmentations Quantitative Radiomics

Multi-gene Assays (XLS)

Clinical Data (XLS)





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