{"id":42643,"date":"2023-11-20T02:42:01","date_gmt":"2023-11-20T08:42:01","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/collection\/lgg-1p19qdeletion\/"},"modified":"2024-02-28T14:11:36","modified_gmt":"2024-02-28T20:11:36","slug":"lgg-1p19qdeletion","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/lgg-1p19qdeletion\/","title":{"rendered":"LGG-1P19QDELETION"},"featured_media":0,"template":"","class_list":["post-42643","tcia_collection","type-tcia_collection","status-publish"],"cancer_types":["Low Grade Glioma"],"citations":[42623,42625,42627,9225],"collection_doi":"10.7937\/K9\/TCIA.2017.DWEHTZ9V","collection_download_info":"Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\">TCIA Restricted License Agreement<\/a> to <a href=\"mailto:help@cancerimagingarchive.net\">help@cancerimagingarchive.net<\/a> before accessing the data.","collection_downloads":[42629,42631,42633],"versions":[42641],"additional_resources":"","cancer_locations":["Brain"],"collection_page_accessibility":"Limited","publications_related":"","version_change_log_archived":"<h3>Version 2 (Current): Updated 2020\/06\/26<\/h3><table><colgroup> <col \/> <col \/> <\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Images and Segmentations (2.7GB)<\/td><td colspan=\"1\"><div><p>\u00a0 <a href=\"\/wp-content\/uploads\/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia\" download=\"LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=LGG-1p19qDeletion\"><button><i><\/i> Search<\/button><\/a>\u00a0 \u00a0 \u00a0<\/p><p>(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a> )<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Segmentations only (DICOM)<\/td><td colspan=\"1\"><div><p><a href=\"\/wp-content\/uploads\/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia\" download=\"LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><p>(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a> )<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">1p19q Status and Histologic Type<\/td><td colspan=\"1\"><div><p><a href=\"\/wp-content\/uploads\/TCIA_LGG_cases_159.xlsx\" download=\"TCIA_LGG_cases_159.xlsx\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table><p>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).\u00a0 \u00a0In version 2 segmentations of the entire T2 signal abnormality are provided in DICOM-SEG format.<\/p><h3>Version 1: Updated 2017\/09\/30<\/h3><table><colgroup> <col \/> <col \/> <\/colgroup><tbody><tr><th colspan=\"1\">Data Type<\/th><th colspan=\"1\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\">Images (2.7GB)<\/td><td colspan=\"1\"><div><p>\u00a0 <a href=\"\/wp-content\/uploads\/LGG-1p19qDeletion-doiJNLP-Zr9PZSDF.tcia\" download=\"LGG-1p19qDeletion-doiJNLP-Zr9PZSDF.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 \u00a0 \u00a0<\/p><p>(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a> )<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">Segmentations (NIfTI, 2.9GB)<\/td><td colspan=\"1\"><div><p><a href=\"https:\/\/app.box.com\/s\/d0ew9t885nktg163ia4r8qntav9boevj\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><p>(Redirects to large-file storage \"Box\")<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\">1p19q Status and Histologic Type<\/td><td colspan=\"1\"><div><p><a href=\"\/wp-content\/uploads\/TCIA_LGG_cases_159.xlsx\" download=\"TCIA_LGG_cases_159.xlsx\"><button><i><\/i> Download<\/button><\/a>\u00a0<\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage our data. If you have a\u00a0publication\u00a0you'd like to add, please <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA's Helpdesk<\/a> .\r\n<ol>\r\n \t<li>Banerjee, S., Mitra, S., Masulli, F., &amp; Rovetta, S. (2020). Glioma Classification Using Deep Radiomics. SN Computer Science, 1(4), 209. doi:10.1007\/s42979-020-00214-y<\/li>\r\n \t<li>Bhattacharya, D., Sinha, N., &amp; 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.<\/li>\r\n \t<li>Casale, R., Lavrova, E., Sanduleanu, S., Woodruff, H. C., &amp; 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<\/li>\r\n \t<li>Du, R., &amp; 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), Montr\u00e9al, QC, Canada. Available from <a href=\"https:\/\/proceedings.mlr.press\/v121\/du20a.html\">https:\/\/proceedings.mlr.press\/v121\/du20a.html<\/a>.<\/li>\r\n \t<li>Gore, S., &amp; 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<\/li>\r\n \t<li>Kobayashi, T. (2022). RadiomicsJ: a library to compute radiomic features. Radiol Phys Technol, 15(3), 255-263. doi:10.1007\/s12194-022-00664-4<\/li>\r\n \t<li>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<\/li>\r\n \t<li>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<\/li>\r\n \t<li>\u00d6ks\u00fcz, C., Urhan, O., &amp; G\u00fcll\u00fc, 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<\/li>\r\n \t<li>Parekh, V. S., Pillai, J. J., Macura, K. J., LaViolette, P. S., &amp; Jacobs, M. A. (2022). Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning. Cancers (Basel), 14(6). doi:<a href=\"https:\/\/doi.org\/10.3390\/cancers14061481\">https:\/\/doi.org\/10.3390\/cancers14061481<\/a><\/li>\r\n \t<li>Rathore, S., Chaddad, A., Bukhari, N. H., &amp; 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.<\/li>\r\n \t<li>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<\/li>\r\n \t<li>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 <a href=\"http:\/\/hdl.handle.net\/10106\/29765\">http:\/\/hdl.handle.net\/10106\/29765<\/a><\/li>\r\n \t<li>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 &lt;em&gt;MGMT&lt;\/em&gt; Promoter Methylation Status. American Journal of Neuroradiology, 1-8. doi:10.3174\/ajnr.A7029<\/li>\r\n<\/ol>","related_analysis_results":false,"species":["Human"],"version_number":"2","collection_title":"LGG-1p19qDeletion","date_updated":"2020-06-26","related_collection":false,"subjects":"159","analysis_results":"","collection_short_title":"LGG-1p19qDeletion","data_types":["MR"],"version_change_log":"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).\u00a0 \u00a0In version 2 segmentations of the entire T2 signal abnormality are provided in DICOM-SEG format.","collection_browse_title":"LGG-1p19qDeletion","detailed_description":"","supporting_data":["Genomics","Segmentations"],"collection_featured_image":false,"collection_summary":"<p>These MRIs are pre-operative examinations performed in 159 subjects with Low Grade Gliomas (WHO grade II &amp; III). Segmentation of tumors in three axial slices that include the one with the largest tumor\u00a0diameter and ones below and above are provided in NiFTI format.\u00a0\u00a0Tumor grade and histologic type are also available.\u00a0\u00a0All of these subjects have biopsy proven 1p\/19q results, performed using FISH.\u00a0\u00a0For the 1p\/19q status \"n\/n\" means neither 1p nor 19q were deleted. \"d\/d\" means 1p and 19q are co-deleted.\u00a0<\/p>","collection_acknowledgements":"<p>Harmonization of the components of this dataset, including into\u00a0standard DICOM representation, was supported in part by the NCI\u00a0Imaging Data Commons consortium. NCI Imaging Data Commons consortium\u00a0is supported by the contract number 19X037Q from Leidos Biomedical\u00a0Research under Task Order HHSN26100071 from NCI.<\/p>","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42643","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42643\/revisions"}],"predecessor-version":[{"id":48019,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42643\/revisions\/48019"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}