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  • ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection (TCGA-LGG-Mask)

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Comment: updated citation formats, added shortname, all underlying data is untouched.

Special Instruction:

The dataset should not be listed until a related manuscript is published.

The dataset will need a jnlp and DOI. They have a Box link  per Jan 31 and Martin already uploaded the Matlab data there.

  • Shared List Name: TCGA-LGG -- MATLAB ROI
  • Title: TCGA-LGG – MATLAB ROI
  • Authors: Chang Su, Martin Vallières, Harrison Bai

 

Info
titleData Citation

Chang Su, Martin Vallières, Harrison Bai. (2017) ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.BD7SGWCA

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Summary

This collection contains 406 ROI masks in MATLAB format defining the low grade glioma (LGG) tumour region on T1-weighted (T1W), T2-weighted (T2W), T1-weighted post-contrast (T1CE) and T2-flair (T2F) MR images of 108 different patients from the TCGA-LGG collection. From this subset of 108 patients, 81 patients have ROI masks drawn for the four MRI sequences (T1W, T2W, T1CE and T2F), and 27 patients have ROI masks drawn for three or less of the four MRI sequences.The ROI masks were used to extract texture features in order to develop radiomic-based multivariable models for the prediction of isocitrate dehydrogenase 1 (IDH1) mutation, 1p/19q codeletion status, histological grade and tumour progression. 

Clinical data (188 patients in total from the TCGA-LGG collection, some incomplete depending on the clinical attribute), VASARI scores (188 patients in total from the TCGA-LGG collection, 178 complete) with feature keys, and source code used in this study are also available with this collection. Please contact Martin Vallières (mart.vallieres@gmail.com) of the Medical Physics Unit of McGill University for any scientific inquiries about this dataset.

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, 9.03 GB)
Clinical data (CSV)
VASARI information (CSV)
VASARI MR feature key (PDF)

The analysis results are presented in the following study:

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KeysImage Added

Matlab Segmentations 

Please contact help@cancerimagingarchive.net with a completed Data Use Agreement to request access.  More information is on the Detailed Description tab of this page.


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

Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:




Localtab
titleDetailed Description

Detailed Description

Access to this collection's MATLAB ROI masks is currently restricted by Harrison X. Bai from the Department of Radiology, Hospital of University of Pennsylvania. Access could be granted if this dataset is properly acknowledged in your research. If you believe this data will be useful for a current or planned research project, you may request access to this dataset by completing the attached Data Use Agreement and forwarding it via e-mail to the TCIA help desk help@cancerimagingarchive.net. Please make sure that the form is filled out by a formal Principal Investigator (PI) and please also make sure to include your institutional address with contact information. The Data Use Agreement will then be promptly reviewed by Harrison X. Bai and you will be informed of his decision. In most cases, access will be granted and members of your research team will be granted access to the dataset. Note: you must have TCIA login credentials in order to access any restricted collection.




Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Public collection license

Info
titleData Citation

Su, C., Vallières, M., & Bai, H. (2017). ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.BD7SGWCA


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
Hao

Zhou, H.,

Martin

Vallières,

Harrison

M., Bai, H. X.

Bai

,

Chang

Su, C.,

Haiyun

Tang, H.,

Derek

Oldridge, D.,

Zishu

Zhang, Z.,

Bo

Xiao, B.,

Weihua

Liao,

Yongguang

W., Tao,

Jianhua Zhou, Paul Zhang, Li Yang;

Y., Zhou, J., Zhang, P., & Yang, L. (2017). MRI features predict survival and molecular markers in diffuse lower-grade gliomas.

 Neuro Oncol 2017 now256. doi:

Neuro-Oncology, 19(6), 862–870. https://doi.org/10.1093/neuonc/now256


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): 2017/03/17


Data TypeDownload all or Query/Filter

...

Images (DICOM, 9.03 GB)

...

Clinical data (CSV)
VASARI information (CSV)
VASARI MR feature key (PDF)

KeysImage Added

Matlab Segmentations 

Please contact help@cancerimagingarchive.net with a completed Data Use Agreement to request access.  More information is on the Detailed Description tab of this page.