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title | Data Access |
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| Data AccessChoosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option. Data Type | Download all or Query/Filter | License |
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Images and Segmentations (DICOM, 2. | 7GBImage Removed Image RemovedSegmentations (NiFTI, 2.9GB) | Image Removed | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia?version=1&modificationDate=1593205545466&api=v2 |
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Tcia button generator |
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label | Search |
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url | https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=LGG-1p19qDeletion |
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(Download requires NBIA Data Retriever) | | Segmentations only (DICOM, 10 kB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia?version=1&modificationDate=1593205562927&api=v2 |
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(Download requires NBIA Data Retriever ) | | 1p19q Status and Histologic Type | Image Removed(XLS, 53 kB) |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2 |
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Click the Versions tab for more info about data releases. |
Localtab |
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title | Detailed Description |
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| Detailed Description
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ModalitiesMRI | MR, SEG | , NIfTI | Number of PatientsParticipants | 159 | Number of Studies | 160 | Number of Series319 | 478 | Number of Images17360 | 17,519 | Image Size (GB) | 2.7 |
For the 1p/19q status "n/n" means neither 1p nor 19q were deleted. "d/d" means 1p and 19q are co-deleted.
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Localtab |
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title | Citations & Data Usage Policy |
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| 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: Tcia limited license policy |
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Info |
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| Erickson, Bradley; Akkus, Zeynettin; Sedlar, Jiri; Korfiatis, Panagiotis. B., Akkus, Z., Sedlar, J., & Korfiatis, P. (2017). Data From from LGG-1p19qDeletion (Version 2) [Data set]. The Cancer Imaging Archive. http https://doi.org/10.7937/K9/TCIA.2017.DWEHTZ9V |
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title | Publication Citation |
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| Akkus, Z., Ali, I., Sedlář, J., Agrawal, J. P., Parney, I. F., Giannini, C., & Erickson, B. J. (2017.dwehtz9v). Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence. In Journal of Digital Imaging (Vol. 30, Issue 4, pp. 469–476). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-017-9984-3 . PMCID: PMC5537096 |
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title | Publication Citation |
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| Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., & Philbrick, K. (2017). Toolkits and Libraries for Deep Learning. In Journal of Digital Imaging (Vol. 30, Issue 4, pp. 400–405). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-017-9965-6 |
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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, . In Journal of Digital Imaging , Volume (Vol. 26, Number Issue 6, December, 2013, pp 1045-1057. (paper) | Other Publications Using This DataTCIA maintains a list of publications which leverage our data. If you have a publication you'd like to add, pleasecontact TCIA's Helpdesk. - 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
- Bhattacharya, D., Sinha, N., & Saini, J. (2020). Radial Cumulative Frequency Distribution: A New Imaging Signature to Detect Predicting Deletion of Chromosomal Arms 1p/19q Co-deletion Status in Glioma. Paper presented at the International Conference on Computer Vision and Image Processing.
- 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
- 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.
- 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
- Kobayashi, T. (2022). RadiomicsJ: a library to compute radiomic features. Radiol Phys Technol, 15(3), 255-263. doi:10.1007/s12194-022-00664-4
- 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
- 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
- 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
- -Grade Gliomas from MR Images Using Machine Intelligence. Zeynettin Akkus, Issa Ali, Jiří Sedlář, Jay P. Agrawal, Ian F. Parney, Caterina Giannini,and Bradley J. Erickson. J Digit Imaging. 2017 Aug; 30(4): 469–476. Published online 2017 Jun 9. doi: 10.1007/s10278-017-9984-3. PMCID: PMC5537096Parekh, 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/10.1007/s10278-017-9965-6 Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick. Toolkits and Libraries for Deep Learning. Journal of Digital Imaging 2017 p1618-1627.
TCIA maintains a list of publications which leverage our data.If you have a publication you'd like to add please contact the TCIA Helpdesk.- .3390/cancers14061481
- 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.
- 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
- 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
- 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
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Localtab |
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| Version 2 (Current): Updated 2020/06/26 Data Type | Download all or Query/Filter |
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Images and Segmentations (2.7GB) | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia?version=1 |
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Tcia button generator |
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label | Search |
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url | https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=LGG-1p19qDeletion |
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(Download requires NBIA Data Retriever ) | Segmentations only (DICOM) |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia?version=1&modificationDate=1593205562927&api=v2 |
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(Download requires NBIA Data Retriever ) | 1p19q Status and Histologic Type |
Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2 |
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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 |
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Images (2.7GB) | | | Tcia button generator |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/LGG-1p19qDeletion-doiJNLP-Zr9PZSDF.tcia?version=1&modificationDate=1534787036556&api=v2 |
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(Download requires NBIA Data Retriever ) | Segmentations (NIfTISegmentations (NiFTi, 2.9GB) |
Tcia button generator |
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url | https://app.box.com/s/d0ew9t885nktg163ia4r8qntav9boevj |
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(Redirects to large-file storage "Box")Image Removed | 1p19q Status and Histologic Type |
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url | https://wiki.cancerimagingarchive.net/download/attachments/25789042/TCIA_LGG_cases_159.xlsx?version=1&modificationDate=1509045953290&api=v2 |
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