TCGA-Breast-Radiogenomics | TCGA Breast Phenotype Research Group Data sets
DOI: 10.7937/K9/TCIA.2014.8SIPIY6G | Data Citation Required | 6 Citations | Analysis Result
| Location | Subjects | Size | Updated | |||
|---|---|---|---|---|---|---|
| Breast | Breast | 84 | Radiologist assessments of image features, Tumor segmentations, radiomic features, multi-gene assays | 2018/09/04 |
Summary
At the time of our study, 108 cases with breast MRI data were available in the The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA) collection. In order to minimize variations in image quality across the multi-institutional cases we included only breast MRI studies acquired on GE 1.5 Tesla magnet strength scanners (GE Medical Systems, Milwaukee, Wisconsin, USA) scanners, yielding a total of 93 cases. We then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the TCGA Data Portal (8 patients). After these criteria, a dataset of 84 breast cancer patients resulted, with MRIs from four institutions: Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center, and the Roswell Park Cancer Institute. The resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. The dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. Of these, 73 (87%) were ER+, 67 (80%) were PR+, and 19 (23%) were HER2+. Various types of analyses were conducted using the combined imaging, genomic, and clinical data. Those analyses are described within several manuscripts created by the group (cited below). Additional information about the methodology for how the Radiologist Annotations file can be found on the TCGA Breast Image Feature Scoring Project page.
Data Access
Version 1: Updated 2018/09/04
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Radiologist Annotations | Radiomic Feature | XLS | CC BY 3.0 | |||||
| Segmented Lesions (*.les) | Segmentation | ZIP | 91 | CC BY 3.0 | ||||
| Quantitative Radiomic Features | Radiomic Feature | XLS | CC BY 3.0 | |||||
| MammaPrint, Oncotype DX, and PAM50 Multi-gene Assays | Classification | XLS | 100 | CC BY 3.0 | ||||
| Clinical Data | Demographic, Diagnosis, Follow-Up, Molecular Test, Treatment | XLS | 334 | CC BY 3.0 |
Collections Used In This Analysis Result
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Corresponding Original Images from TCGA-BRCA | MG, MR | DICOM | Requires NBIA Data Retriever |
91 | 104 | 1,129 | 114,323 | CC BY 3.0 |
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
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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 |
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.
The LES file is binary in format and contains the coordinates and shape mask volume of the lesion. It consists of six 2-byte short integer values which represent a 3×2 array of y,x,z start and end points. The remainder of the file contains the 1-byte mask values for the lesion voxels.
The following MATLAB statement reads a LES file:
[binles, lesRange] = loadlesion(targetfile);
where targetfile is a string of the file name path.
and where loadlesion.m is:
function [binles,rg]=loadlesion(targetfile)
fid=fopen(targetfile,’r’);
if fid ~=-1
rg=fread(fid,[3 2],’uint16′)
sz=rg(:,2)-rg(:,1)+1;
binles=fread(fid,[prod(sz) 1],’int8′);
binles=reshape(binles,sz’);
fclose(fid);
range=[rg(5)-rg(2)+1,rg(4)-rg(1)+1,rg(6)-rg(3)+1]
else
binles=[];
rg=[];
end
end
For example, if
rg =
122 150
327 379
71 84
then
yEnd = 150 and yStart = 122
xEnd = 379 and xStart = 327
zEnd = 84 and zStart = 71
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
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, 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 |
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. (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 |
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. |
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, 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 |
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. |
Research Community Publications
TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.