Page History

Detailed Description

Supporting Documentation and Metadata

For the 1p/19q status "n/n" means neither 1p nor 19q were deleted. "d/d" means 1p and 19q are co-deleted. 

Citations & Data Usage Policy 

This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.  See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.

Please be sure to include the following citations in your work if you use this data set:

Other Publications Using This Data

Other Publications Using This Data

TCIA maintains a list of publications which

TCIA maintains a list of publications which leverage our data. If you have a publication you'd like to add, please contact the contact TCIA's Helpdesk.

1. Banerjee, S., Mitra, S., Masulli, F., & Rovetta, S. (2020). Glioma Classification Using Deep Radiomics. SN Computer Science, 1(4), 209. doi:10.1007/s42979-020-00214-y
   
2. Bhattacharya, D., Sinha, N., & Saini, J. (2020). Radial Cumulative Frequency Distribution: A New Imaging Signature to Detect Chromosomal Arms 1p/19q Co-deletion Status in Glioma. Paper presented at the International Conference on Computer Vision and Image Processing.
   
3. Casale, R., Lavrova, E., Sanduleanu, S., Woodruff, H. C., & Lambin, P. (2021). Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients. Eur J Radiol, 139, 109678. doi:10.1016/j.ejrad.2021.109678
   
4. Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada. Available from https://proceedings.mlr.press/v121/du20a.html.
   
5. Gore, S., & Jagtap, J. (2021). Radiogenomic analysis: 1p/19q codeletion based subtyping of low-grade glioma by analysing advanced biomedical texture descriptors. Journal of King Saud University - Computer and Information Sciences. doi:10.1016/j.jksuci.2021.08.024
   
6. Kobayashi, T. (2022). RadiomicsJ: a library to compute radiomic features. Radiol Phys Technol, 15(3), 255-263. doi:10.1007/s12194-022-00664-4
   
7. Kocak, B., Durmaz, E. S., Ates, E., Sel, I., Turgut Gunes, S., Kaya, O. K., . . . Kilickesmez, O. (2019). Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status. Eur Radiol. doi:10.1007/s00330-019-06492-2
   
8. Ning, Z., Luo, J., Xiao, Q., Cai, L., Chen, Y., Yu, X., . . . Zhang, Y. (2021). Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features. Ann Transl Med, 9(4), 298. doi:10.21037/atm-20-4076
   
9. Öksüz, C., Urhan, O., & Güllü, M. K. (2022). Brain tumor classification using the fused features extracted from expanded tumor region. Biomedical Signal Processing and Control, 72, 103356. doi:10.1016/j.bspc.2021.103356
   
10. Parekh, V. S., Pillai, J. J., Macura, K. J., LaViolette, P. S., & Jacobs, M. A. (2022). Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning. Cancers (Basel), 14(6). doi:https://doi.org/10.3390/cancers14061481
    
11. Rathore, S., Chaddad, A., Bukhari, N. H., & Niazi, T. (2020). Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma. In Radiomics and Radiogenomics in Neuro-oncology (Vol. 11991, pp. 61-69): Springer International Publishing.
12. van der Voort, S. R., Incekara, F., Wijnenga, M. M., Kapsas, G., Gardeniers, M., Schouten, J. W., . . . French, P. J. (2019). Predicting the 1p/19q co-deletion status of presumed low grade glioma with an externally validated machine learning algorithm. Clinical Cancer Research, clincanres. 1127.2019. doi:10.1158/1078-0432.CCR-19-1127
13. Yogananda, C. G. B. (2021). Non-invasive Profiling of Molecular Markers in Brain Gliomas using Deep Learning and Magnetic Resonance Images. (Ph.D. Doctor of Philosophy in Biomedical Engineering Dissertation). The University of Texas at Arlington, Proquest. Retrieved from http://hdl.handle.net/10106/29765
14. Yogananda, C. G. B., Shah, B. R., Nalawade, S. S., Murugesan, G. K., Yu, F. F., Pinho, M. C., . . . Maldjian, J. A. (2021). MRI-Based Deep-Learning Method for Determining Glioma <em>MGMT</em> Promoter Methylation Status. American Journal of Neuroradiology, 1-8. doi:10.3174/ajnr.A7029

Version 2 (Current): Updated 2020/06/26

Previously the segmentations of the tumors were provided in NIfTI format and only included three axial slices (the one with the largest tumor diameter and ones below and above).   In version 2 segmentations of the entire T2 signal abnormality are provided in DICOM-SEG format.

Version 1: Updated 2017/09/30

Data Type	Download all or Query/Filter
Images (2.7GB)	Image Removed Image Removed		Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion-doiJNLP-Zr9PZSDF.tcia?version=1&modificationDate=1534787036556&api=v2     (Download requires NBIA Data Retriever )
Segmentations (NIfTISegmentations (NiFTi, 2.9GB)	Tcia button generator url https://app.box.com/s/d0ew9t885nktg163ia4r8qntav9boevj (Redirects to large-file storage "Box")Image Removed
1p19q Status and Histologic Type	Tcia button generator url https://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2 Image Removed