Child pages
  • TCGA Breast Phenotype Research Group Data sets

Versions Compared

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

Localtab Group


Localtab
activetrue
titleData Access

Data Access

Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever

Data TypeDownload all or Query/Filter
Images (DICOM)

Radiologist Annotations (XLS)

Segmentations (ZIP, XLS)

Note:  README

3D Segmentations

Quantitative Radiomic Features

Quantitative Radiomics

MammaPrint, Oncotype DX, and PAM50 Multi-gene Assays (XLS)

Clinical Data (XLS)

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


Localtab
titleDetailed Description

Detailed Description

Readme instructions are available for the 3d 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.

Please reference these data extracted using version V2010 of the UChicago MRI Quantitative Radiomics workstation.


Localtab
titleCitations & Data Usage Policy

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
titleData Citation

MorrisBurnside E, ElizabethDrukker K, BurnsideLi H, Elizabeth, Whitman, Gary, Zuley, Margarita, Bonaccio, Ermelinda, Ganott, Marie, … Giger, Maryellen LBonaccio 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. (2014). Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Stage. The Cancer Imaging Archive. httphttps://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. 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)DOI: 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 ML

*

, Ji Y

*:

. (2015)   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


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

doi:

122(5): 748-757 . DOI: 10.1002/cncr.29791

, 2015.


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

*:  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 281(2):382-391. doi: 10.1148/radiol.2016152110

, 2016.


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.

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 SegmentationsQuantitative Radiomics

Multi-gene Assays (XLS)

Clinical Data (XLS)



...