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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

Description

To perform this study, we have contoured the LGG This collection contains 406 ROI masks in MATLAB format defining the low grade glioma (LGG) tumour region on T1-weighted, T2-weighted, T1-weighted post-contrast and T2-flair MR images for a subset of of 108 different patients from the TCGA-LGG patients (total of 108) dataset. 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.

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, VASARI scores and source code used in this study are also available with this collection. Please see the DOI below for more details and link to access the whole dataset. Please contact Martin Vallières (mart.vallieres@gmail.com^{Image Added}) of the Medical Physics Unit of McGill University for any scientific inquiries about this dataset.

The analysis results are presented in the following study: 

Download

• DICOM Image Data (xx GB)  – needs JNLP from shared list
• Matlab Segmentations (xx MB) – from box link
• TCGA Clinical data
• VASARI scores
• VASARI Feature Key
• Source Code