{"id":5620,"date":"2023-09-04T03:13:11","date_gmt":"2023-09-04T03:13:11","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/lgg-1p19qdeletion\/"},"modified":"2023-09-13T12:00:10","modified_gmt":"2023-09-13T12:00:10","slug":"lgg-1p19qdeletion","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/lgg-1p19qdeletion\/","title":{"rendered":"LGG-1P19QDELETION"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Low Grade Glioma"],"citations":[4515,4516,4517,2925],"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.\n\n<br\/>\nClick the Versions tab for more info about data releases.","collection_downloads":[5102,5103,5104],"full_export":"<h2 id=\"LGG1p19qDeletion-Summary\">Summary<\/h2><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 &quot;n\/n&quot; means neither 1p nor 19q were deleted. &quot;d\/d&quot; means 1p and 19q are co-deleted.\u00a0<\/p><h3 class=\"xmsonormal\" id=\"LGG1p19qDeletion-Acknowledgements\"><span style=\"color: rgb(32,31,30);\">Acknowledgements <\/span><\/h3><p class=\"xmsonormal\"><span style=\"color: rgb(32,31,30);\">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.<\/span><\/p><p><br\/><\/p><div class=\"tab-style-builtin\"><div class=\"localtabs-macro\"><div class=\"aui-tabs horizontal-tabs\" role=\"application\" data-aui-responsive=\"true\"><ul class=\"tabs-menu\"><li class=\"menu-item bv-localtab  active-tab \"><a href=\"#257890426456c02bce974ad7bc00dd80c274c914\"><strong>Data Access<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#257890429d1bb42436f64924be2ba066df63c224\"><strong>Detailed Description<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#25789042ba40a6d5ace14b6ab7e09aadc4498e33\"><strong>Citations &amp; Data Usage Policy<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#2578904254be5bb80ad9475bb8b475dd2e0e4e23\"><strong>Versions<\/strong><\/a> <\/li><\/ul><div class=\"tabs-pane  active-pane \" id=\"257890426456c02bce974ad7bc00dd80c274c914\" active=\"true\" name=\"Data Access\" ><h3 id=\"LGG1p19qDeletion-DataAccess\">Data Access<\/h3><p>\nSome 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\" class=\"external-link\" rel=\"nofollow\">TCIA Restricted License Agreement<\/a> to <a href=\"mailto:help@cancerimagingarchive.net\" class=\"external-link\" rel=\"nofollow\">help@cancerimagingarchive.net<\/a> before accessing the data.<\/p><div class=\"table-wrap\"><table class=\"wrapped relative-table confluenceTable\" style=\"width: 43.559%;\"><colgroup><col style=\"width: 30.9661%;\"\/><col style=\"width: 35.4751%;\"\/><col style=\"width: 33.5263%;\"\/><\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><th class=\"confluenceTh\">License<\/th><\/tr><tr><td class=\"confluenceTd\">Images and Segmentations (2.7GB)<\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><span class=\"confluence-link\">\u00a0<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia?version=1&amp;modificationDate=1593205545466&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=LGG-1p19qDeletion\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p><span class=\"confluence-link\">\u00a0<\/span><span class=\"confluence-link\">(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a>)<\/span><\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><span class=\"confluence-link\"><span class=\"confluence-link\">\n<a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\" class=\"external-link\" rel=\"nofollow\">TCIA Restricted<\/a><br\/><\/span><\/span><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\">Segmentations only (DICOM)<\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia?version=1&amp;modificationDate=1593205562927&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p><span class=\"confluence-link\">(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a> )<\/span><\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p><span class=\"confluence-link\">\n<a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\" class=\"external-link\" rel=\"nofollow\">TCIA Restricted<\/a><br\/><\/span><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\">1p19q Status and Histologic Type (XLS)<\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/TCIA_LGG_cases_159.xlsx?version=1&amp;modificationDate=1509045953290&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\" class=\"external-link\" rel=\"nofollow\">CC BY 3.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><p><br\/><\/p><p>Click the Versions tab for more info about data releases.<\/p><\/div><div class=\"tabs-pane \" id=\"257890429d1bb42436f64924be2ba066df63c224\" name=\"Detailed Description\" ><h3 id=\"LGG1p19qDeletion-DetailedDescription\">Detailed Description<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th class=\"confluenceTh\"><div class=\"tablesorter-header-inner\"><p>Collection Statistics<\/p><\/div><\/th><th class=\"confluenceTh\"><div class=\"tablesorter-header-inner\"><p>Radiology imaging statistics<\/p><\/div><\/th><\/tr><tr><td class=\"confluenceTd\"><p>Modalities<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p>MR, SEG<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Participants<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p>159<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Studies<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\">160<\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Series<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p>478<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Images<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p>17519<\/p><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Image Size (GB)<\/td><td style=\"text-align: center;\" colspan=\"1\" class=\"confluenceTd\">2.7<\/td><\/tr><\/tbody><\/table><\/div><h4 id=\"LGG1p19qDeletion-SupportingDocumentationandMetadata\">Supporting Documentation and Metadata<\/h4><p><span style=\"color: rgb(0,0,0);\">For the 1p\/19q status &quot;n\/n&quot; means neither 1p nor 19q were deleted. &quot;d\/d&quot; means 1p and 19q are co-deleted.<\/span><\/p><p><br\/><\/p><\/div><div class=\"tabs-pane \" id=\"25789042ba40a6d5ace14b6ab7e09aadc4498e33\" name=\"Citations BITVOODOO_ANDamp; Data Usage Policy\" ><h3 id=\"LGG1p19qDeletion-Citations&amp;DataUsagePolicy\">Citations &amp; Data Usage Policy\u00a0<\/h3><p class=\"auto-cursor-target\">\n<p>\nUsers must abide by the <a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/c4hF\" class=\"external-link\" rel=\"nofollow\">TCIA Data Usage Policy and Restrictions<\/a>. Attribution should include references to the following citations:\n<\/p><\/p><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Data Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Erickson, B., Akkus, Z., Sedlar, J., &amp; Korfiatis, P. (2017). <strong>Data from LGG-1p19qDeletion (Version 2) [Data set]<\/strong>. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.DWEHTZ9V\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.DWEHTZ9V<\/a><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Publication Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Akkus, Z., Ali, I., Sedl\u00e1\u0159, J., Agrawal, J. P., Parney, I. F., Giannini, C., &amp; Erickson, B. J. (2017). <strong>Predicting Deletion of Chromosomal Arms 1p\/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence.<\/strong> In Journal of Digital Imaging (Vol. 30, Issue 4, pp. 469\u2013476). Springer Science and Business Media LLC. <a href=\"https:\/\/doi.org\/10.1007\/s10278-017-9984-3\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10278-017-9984-3<\/a> .\u00a0PMCID:\u00a0PMC5537096<\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">Publication Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Erickson, B. J., Korfiatis, P., Akkus, Z., Kline, T., &amp; Philbrick, K. (2017).<strong> Toolkits and Libraries for Deep Learning. <\/strong>In Journal of Digital Imaging (Vol. 30, Issue 4, pp. 400\u2013405). Springer Science and Business Media LLC. <a href=\"https:\/\/doi.org\/10.1007\/s10278-017-9965-6\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10278-017-9965-6<\/a><\/p><\/div><\/div><div class=\"confluence-information-macro confluence-information-macro-information\"><p class=\"title\">TCIA Citation<\/p><span class=\"aui-icon aui-icon-small aui-iconfont-info confluence-information-macro-icon\"><\/span><div class=\"confluence-information-macro-body\"><p>Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., &amp; Prior, F. (2013). <strong>The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. <\/strong>In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045\u20131057). Springer Science and Business Media LLC. <a href=\"https:\/\/doi.org\/10.1007\/s10278-013-9622-7\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1007\/s10278-013-9622-7<\/a><\/p><\/div><\/div><h3 id=\"LGG1p19qDeletion-OtherPublicationsUsingThisData\">Other Publications Using This Data<\/h3><p><span style=\"letter-spacing: 0.0px;\">TCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\" class=\"external-link\" rel=\"nofollow\">a list of publications<\/a> which leverage our data. <\/span><span style=\"letter-spacing: 0.0px;\"> <\/span><span style=\"letter-spacing: 0.0px;\">If you have a\u00a0<\/span><span style=\"letter-spacing: 0.0px;\">publication\u00a0<\/span><span style=\"letter-spacing: 0.0px;\">you'd like to add, please<\/span><span style=\"letter-spacing: 0.0px;\"> <\/span><a class=\"external-link\" href=\"http:\/\/www.cancerimagingarchive.net\/support\/\" style=\"letter-spacing: 0.0px;\" rel=\"nofollow\">\u00a0contact TCIA's Helpdesk<\/a><span style=\"letter-spacing: 0.0px;\"> <\/span><span style=\"letter-spacing: 0.0px;\">.<\/span><\/p><ol><li><span style=\"letter-spacing: 0.0px;\">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<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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.<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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\" class=\"external-link\" rel=\"nofollow\">https:\/\/proceedings.mlr.press\/v121\/du20a.html<\/a>.<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">Kobayashi, T. (2022). RadiomicsJ: a library to compute radiomic features. Radiol Phys Technol, 15(3), 255-263. doi:10.1007\/s12194-022-00664-4<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">\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<br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.3390\/cancers14061481<\/a><br\/><\/span><\/li><li><span style=\"letter-spacing: 0.0px;\">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. <\/span><\/li><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><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 style=\"letter-spacing: 0.0px;\" href=\"http:\/\/hdl.handle.net\/10106\/29765\" class=\"external-link\" rel=\"nofollow\">http:\/\/hdl.handle.net\/10106\/29765<\/a><span style=\"letter-spacing: 0.0px;\"> <\/span><\/li><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><\/ol><\/div><div class=\"tabs-pane \" id=\"2578904254be5bb80ad9475bb8b475dd2e0e4e23\" name=\"Versions\" ><h3 id=\"LGG1p19qDeletion-Version2:Updated6\/26\/2020\">Version 2: Updated 6\/26\/2020<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th colspan=\"1\" class=\"confluenceTh\">Data Type<\/th><th colspan=\"1\" class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Images and Segmentations (2.7GB)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p><span class=\"confluence-link\"> <span class=\"confluence-link\">\u00a0<\/span><\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia?version=1&amp;modificationDate=1593205545466&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/www.cancerimagingarchive.net\/nbia-search\/?CollectionCriteria=LGG-1p19qDeletion\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span><span class=\"confluence-link\"><span class=\"confluence-link auto-cursor-target\">\u00a0<\/span> <\/span>\u00a0<span class=\"confluence-link\"> <\/span><\/p><p><span class=\"confluence-link\">(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a> )<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Segmentations only (DICOM)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/LGG-1p19qDeletion_v2_SEGonly_Jun2020.tcia?version=1&amp;modificationDate=1593205562927&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p><span class=\"confluence-link\">(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a> )<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">1p19q Status and Histologic Type<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/TCIA_LGG_cases_159.xlsx?version=1&amp;modificationDate=1509045953290&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><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 id=\"LGG1p19qDeletion-Version1(Current):Updated2017\/09\/30\">Version 1 (Current): Updated 2017\/09\/30<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th colspan=\"1\" class=\"confluenceTh\">Data Type<\/th><th colspan=\"1\" class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Images (2.7GB)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p><span class=\"confluence-link\"> <span class=\"confluence-link\">\u00a0<\/span><\/span>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/LGG-1p19qDeletion-doiJNLP-Zr9PZSDF.tcia?version=1&amp;modificationDate=1534787036556&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><span class=\"confluence-link\"><span class=\"confluence-link auto-cursor-target\">\u00a0<\/span> <\/span>\u00a0<\/p><p><span class=\"confluence-link\">(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a> )<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Segmentations (NIfTI, 2.9GB)<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/app.box.com\/s\/d0ew9t885nktg163ia4r8qntav9boevj\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><br\/><\/p><p><span class=\"confluence-link\">(Redirects to large-file storage &quot;Box&quot;)<\/span><\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">1p19q Status and Histologic Type<\/td><td colspan=\"1\" class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\n\n<span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\">\n   <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/25789042\/TCIA_LGG_cases_159.xlsx?version=1&amp;modificationDate=1509045953290&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span><\/p><\/div><\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/div><\/div>","versions":false,"additional_resources":"","cancer_locations":["Brain"],"collection_page_accessibility":"Public","publications_related":"","version_change_log":"","version_change_log_archived":"","analysis_results":"","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\/\">\u00a0contact TCIA's Helpdesk<\/a> .\n<ol><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<br\/><\/li><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.<br\/><\/li><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<br\/><\/li><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>.<br\/><\/li><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<br\/><\/li><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<br\/><\/li><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<br\/><\/li><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<br\/><\/li><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<br\/><\/li><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><br\/><\/li><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><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><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><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><\/ol>","species":["Human"],"collection_title":"LGG-1p19qDeletion","detailed_description":"<h4>Supporting Documentation and Metadata<\/h4>\nFor the 1p\/19q status \"n\/n\" means neither 1p nor 19q were deleted. \"d\/d\" means 1p and 19q are co-deleted.\n<br\/>","related_analysis_results":false,"subjects":"159","collection_short_title":"LGG-1p19qDeletion","data_types":["MR"],"date_updated":"2023-09-13","collection_browse_title":"","supporting_data":["Image Analyses","Genomics"],"collection_featured_image":false,"collection_summary":"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.","collection_acknowledgements":"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.\n<br\/>","collection_funding":"","hide_from_browse_table":[],"_links":{"self":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections\/5620"}],"collection":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections"}],"about":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/types\/tcia_collection"}],"wp:attachment":[{"href":"https:\/\/cm.vastapps.dev\/api\/wp\/v2\/media?parent=5620"}],"wp:term":[{"taxonomy":"tcia_citation_tax","embeddable":true,"href":"https:\/\/cm.vastapps.dev\/api\/v1\/citation-tax?post=5620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}