Summary
This collection contains 406 ROI masks in MATLAB format defining the low grade glioma (LGG)
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.
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Martin Vallières et al. (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. The Cancer Imaging Archive. https://doi.org/link |
Description
To perform this study, we have contoured the LGG tumour region on T1-weighted (T1W), T2-weighted (T2W), T1-weighted post-contrast (T1CE) and T2-flair images for a subset of (T2F) MR images of 108 different patients from the TCGA-LGG collection. From this subset of 108 patients (total of 108). The end results is a set of ROI masks for each imaging series in MATLAB format. We would like now to share these ROI masks on the TCIA website.
In order to share this data on the TCIA website, we have created a new set of DICOM images containing only 0's and 1's to define the ROI in the corresponding DICOM images of the 108 TCGA-LGG patients. We understand that this is definitely not the most efficient way of sharing ROIs (I see that DICOM SEG format is becoming the accepted standard?). However, this is the best that we have at the moment.
We would like to know if it is possible to share those ROI masks in DICOM format (~15GB)? Another option would be to directly share the ROI masks in MATLAB format (~130MB). Otherwise, do you have another option to suggest?
Finally, my collaborators would like to add a restriction on the access of the ROI masks (i.e. asking permission by email), but only for a certain period of time.
, 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.
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| title | Publication Citation |
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| active | true |
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| title | Data Access |
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| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever
| Data Type | Download all or Query/Filter |
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| Images (DICOM, 9.03 GB) | | | Clinical data (CSV) | | | VASARI information (CSV) | | | VASARI MR feature key (PDF) |  Image 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: |
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| title | Detailed Description |
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| Detailed DescriptionAccess 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. |
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| title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy | Public collection license |
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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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| Zhou, H., Vallières, M., Bai, H. X., Su, C., Tang, H., Oldridge, D., Zhang, Z., Xiao, B., Liao, W., Tao, Y., Zhou, J., Zhang, P., & Yang, L. (2017). |
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MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro-Oncology |
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. [Epub ahead of print] PMID: |
Download
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| Localtab |
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| Version 1 (Current): 2017/03/17
| Data Type | Download all or Query/Filter |
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| Images (DICOM, 9.03 GB) | | | Clinical data (CSV) | | | VASARI information (CSV) | | | VASARI MR feature key (PDF) | Image 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. | |
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