{"id":5662,"date":"2023-09-04T03:16:10","date_gmt":"2023-09-04T03:16:10","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/qin-headneck\/"},"modified":"2023-09-13T12:02:04","modified_gmt":"2023-09-13T12:02:04","slug":"qin-headneck","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/qin-headneck\/","title":{"rendered":"QIN-HEADNECK"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Head and Neck Carcinomas"],"citations":[4591,4592,2925],"collection_doi":"10.7937\/K9\/TCIA.2015.K0F5CGLI","collection_download_info":"Summary\n: U24 CA180918 (<a href=\"http:\/\/qiicr.org\">http:\/\/qiicr.org<\/a>) and U01 CA140206.\nThe following schematic summarizes much of the work done within the QIICR grant to augment the PET\/CT scans with segmentations and clinical data using the DICOM standard: (click to enlarge)\n<div class=\"cm-content-image\"><a href=\"\/wp-content\/uploads\/Picture1.jpg\" rel=\"prettyPhoto noopener\" target=\"_blank\"><img src=\"\/wp-content\/uploads\/Picture1.jpg\"\/><\/a><\/div>\nThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the <a href=\"\/display\/Public\/Quantitative+Imaging+Network+Collections\">Quantitative Imaging Network Collections<\/a> page. Interested investigators can apply to the QIN at: <a href=\"https:\/\/imaging.cancer.gov\/programs_resources\/specialized_initiatives\/qin.htm\">Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150<\/a>.\n<br\/>","collection_downloads":[5205,5206],"full_export":"<div class=\"contentLayout2\">\n<div class=\"columnLayout two-right-sidebar\" data-layout=\"two-right-sidebar\">\n<div class=\"cell normal\" data-type=\"normal\">\n<div class=\"innerCell\">\n<p><span style=\"font-size: 20.0px;font-weight: bold;line-height: 1.5;\">Summary<\/span><\/p>This collection is a set of head and neck cancer patients' multiple positron emission tomography\/computed tomography (PET\/CT) 18F-FDG scans\u2013before and after therapy\u2013with follow up scans where clinically indicated.\u00a0The data was provided to help facilitate research activities of the National Cancer Institute's (NCI's) Quantitative Imaging Network (QIN). This collection was supported by Grants<span style=\"line-height: 1.42857;\">: U24 CA180918 (<a href=\"http:\/\/qiicr.org\" class=\"external-link\" rel=\"nofollow\">http:\/\/qiicr.org<\/a>) and <span>U01 CA140206<\/span>.<\/span><p>The following schematic summarizes much of the work done within the QIICR grant to augment the PET\/CT scans with segmentations and clinical data using the DICOM standard: (click to enlarge)<\/p><p><span class=\"confluence-embedded-file-wrapper confluence-embedded-manual-size\"><img class=\"confluence-embedded-image\" draggable=\"false\" width=\"500\" src=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/embedded-page\/Public\/QIN-HEADNECK\/Picture1.jpg?api=v2\"><\/span><\/p><h4 id=\"QINHEADNECK-AbouttheNCIQIN\">About the NCI QIN<\/h4><p>The mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the <a href=\"https:\/\/wiki.cancerimagingarchive.net\/pages\/viewpage.action?pageId=3277251\">Quantitative Imaging Network Collections<\/a> page. Interested investigators can apply to the QIN at: <a href=\"https:\/\/imaging.cancer.gov\/programs_resources\/specialized_initiatives\/qin.htm\" class=\"external-link\" rel=\"nofollow\">Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150<\/a>.<\/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=\"#6885289f658c07e2446485b8d9becde18e13c61\"><strong>Data Access<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#68852893cf76b36652a48e6aea11eb4e14c5ac0\"><strong>Detailed Description<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#68852893b2bae5b8f604791b3993d8d4dcb973e\"><strong>Citations &amp; Data Usage Policy<\/strong><\/a> <\/li><li class=\"menu-item bv-localtab \"><a href=\"#68852896e59b680268c40f3b2f6586cc2cee093\"><strong>Versions<\/strong><\/a> <\/li><\/ul><div class=\"tabs-pane  active-pane \" id=\"6885289f658c07e2446485b8d9becde18e13c61\" active=\"true\" name=\"Data Access\" ><h3 id=\"QINHEADNECK-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: 64.3972%;\"><colgroup><col style=\"width: 35.9535%;\"\/><col style=\"width: 35.4693%;\"\/><col style=\"width: 28.5691%;\"\/><\/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\"><span style=\"color: rgb(38,44,49);\">Images and Segmentations<\/span>\u00a0(DICOM, 201.2 GB)<\/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\/6885289\/QIN-HEADNECK%20NBIA-manifest.tcia_20200914.tcia?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:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;CollectionCriteria=QIN-HEADNECK\" class=\"external-link\" rel=\"nofollow\"><button class=\"tcia-btn tcia-search-color\"><i class=\"fa fa-search\" \/> Search<\/button><\/a>\u00a0\n<\/span>\u00a0<a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;CollectionCriteria=QIN-HEADNECK\" class=\"external-link\" rel=\"nofollow\">\u00a0<\/a><\/p><p>(Download requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><td class=\"confluenceTd\"><div class=\"content-wrapper\"><p>\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><\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Clinical Data (.xlsx 68 kB)<\/p><p>(See also Detailed Description)<\/p><\/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\/6885289\/Batch_01%20and%20Batch_02%20Clinical%20Data_aug242020.xlsx?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><br\/><\/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>Click the Versions tab for more info about data releases.<\/p><\/div><div class=\"tabs-pane \" id=\"68852893cf76b36652a48e6aea11eb4e14c5ac0\" name=\"Detailed Description\" ><h3 id=\"QINHEADNECK-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><br\/><\/p><\/div><\/th><\/tr><tr><td class=\"confluenceTd\"><p>Modalities<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p>PET, CT, SR, SEG, RWV<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Participants<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p>279<\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Studies<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p><span style=\"color: rgb(51,51,51);\">1032<\/span><\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Series<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p><span style=\"color: rgb(51,51,51);\">3837<\/span><\/p><\/td><\/tr><tr><td class=\"confluenceTd\"><p>Number of Images<\/p><\/td><td style=\"text-align: center;\" class=\"confluenceTd\"><p><span style=\"color: rgb(51,51,51);\">701,002<\/span><\/p><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Image Size (GB)<\/td><td style=\"text-align: center;\" colspan=\"1\" class=\"confluenceTd\"><span style=\"color: rgb(51,51,51);\">201.2<\/span><\/td><\/tr><\/tbody><\/table><\/div><h4 id=\"QINHEADNECK-AssociatedClinicalMetadata\">Associated Clinical Metadata<\/h4><ul><li>Structured Report DICOM objects (Modality SR), are available for a subset of these subjects in the DICOM downloads and can be distinguished from image files by the series description &quot;Clinical Data.&quot; Note, there is no image preview thumbnail for a Structured Report.<\/li><\/ul><\/div><div class=\"tabs-pane \" id=\"68852893b2bae5b8f604791b3993d8d4dcb973e\" name=\"Citations BITVOODOO_ANDamp; Data Usage Policy\" ><h3 id=\"QINHEADNECK-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>Beichel, R. R., Ulrich, E. J., Bauer, C., Wahle, A., Brown, B., Chang, T., Plichta, K., Smith, B., Sunderland, J., Braun, T., Fedorov, A., Clunie, D., Onken, M., Magnotta, V. A., Menda, Y., Riesmeier, J., Pieper, S., Kikinis, R., Graham, M.M., <span style=\"color: rgb(102,102,102);\"><span style=\"color: rgb(0,0,0);\">Casavant T. L., Sonka M,. &amp; <\/span><\/span>Buatti, J. (2015). <strong>Data From QIN-HEADNECK (Version 4) [Data set]<\/strong>. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.K0F5CGLI\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2015.K0F5CGLI<\/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>Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., Onken, M., Riesmeier, J., Pieper, S., Kikinis, R., Buatti, J., &amp; Beichel, R. R. (2016). <strong>DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET\/CT analysis results in head and neck cancer research<\/strong>. In PeerJ (Vol. 4, p. e2057). PeerJ. <a href=\"https:\/\/doi.org\/10.7717\/peerj.2057\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.7717\/peerj.2057<\/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><span style=\"color: rgb(0,0,0);\">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). <\/span><strong style=\"\">The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository.<\/strong><span style=\"color: rgb(0,0,0);\"> 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> PMCID: PMC3824915<\/span><\/p><\/div><\/div><h3 id=\"QINHEADNECK-OtherPublicationsUsingThisData\">Other Publications Using This Data<\/h3><p><span>TCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\" class=\"external-link\" rel=\"nofollow\">a list of publications<\/a> which leverage TCIA data. <\/span>If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\" class=\"external-link\" rel=\"nofollow\"> contact TCIA's Helpdesk<\/a>.\u00a0<\/p><ul><li>Ahmadvand, P., Duggan, N., B\u00e9nard, F., &amp; Hamarneh, G. (2016). Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface.\u00a0 International Workshop on Machine Learning in Medical Imaging. doi: <a href=\"https:\/\/doi.org\/10.1007\/978-3-319-47157-0_33\" class=\"external-link\" rel=\"nofollow\">10.1007\/978-3-319-47157-0_33<\/a><\/li><li>Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Clunie, D., Onken, M., Riesmeier, J., . . . Kikinis, R. (2020). Quantitative Imaging Informatics for Cancer Research. JCO Clin Cancer Inform, 4, 444-453. doi:<a href=\"https:\/\/doi.org\/10.1200\/CCI.19.00165\" class=\"external-link\" rel=\"nofollow\">https:\/\/doi.org\/10.1200\/CCI.19.00165<\/a><\/li><li>Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., . . . Beichel, R. R. (2016). DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET\/CT analysis results in head and neck cancer research. PeerJ, 4, e2057. doi: <a href=\"https:\/\/doi.org\/10.7717\/peerj.2057\" class=\"external-link\" rel=\"nofollow\">10.7717\/peerj.2057<\/a><\/li><li>Ghattas, A. E. (2017). Medical Imaging Segmentation Assessment via Bayesian Approaches to Fusion, Accuracy and Variability Estimation with Application to Head and Neck Cancer. (PhD). The University of Iowa, Retrieved from <a href=\"http:\/\/ir.uiowa.edu\/etd\/5759\" class=\"external-link\" rel=\"nofollow\">http:\/\/ir.uiowa.edu\/etd\/5759<\/a><\/li><li>Liang, X., Bassenne, M., Hristov, D. H., Islam, M. T., Zhao, W., Jia, M., . . . Xing, L. (2022). Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Comput Biol Med, 141, 105139. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105139\" class=\"external-link\" rel=\"nofollow\">10.1016\/j.compbiomed.2021.105139<\/a><\/li><li>Lv, W., Zhou, Z., Peng, J., Peng, L., Lin, G., Wu, H., . . . Lu, L. (2023). Functional-structural Sub-region Graph Convolutional Network (FSGCN): Application to the Prognosis of Head and Neck Cancer with PET\/CT imaging. Computer Methods and Programs in Biomedicine. doi:\u00a0 <a href=\"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107341\" class=\"external-link\" rel=\"nofollow\">10.1016\/j.cmpb.2023.107341<\/a><\/li><li>Sinha, A. (2018). Deformable registration using shape statistics with applications in sinus surgery. (Ph. D.). Johns Hopkins University, Retrieved from <a href=\"http:\/\/jhir.library.jhu.edu\/handle\/1774.2\/59202\" class=\"external-link\" rel=\"nofollow\">http:\/\/jhir.library.jhu.edu\/handle\/1774.2\/59202<\/a><\/li><li>Sinha, A., Billings, S. D., Reiter, A., Liu, X., Ishii, M., Hager, G. D., &amp; Taylor, R. H. (2019). The deformable most-likely-point paradigm. Medical image analysis, 55, 148-164. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.media.2019.04.013\" class=\"external-link\" rel=\"nofollow\">10.1016\/j.media.2019.04.013<\/a><\/li><li>Sinha et al. Towards automatic initialization of registration algorithms using simulated endoscopy images.\u00a0 <a href=\"https:\/\/paperswithcode.com\/paper\/towards-automatic-initialization-of\" class=\"external-link\" rel=\"nofollow\"> link to article <\/a><\/li><li>Sinha, A., Ishii, M., Hager, G. D., &amp; Taylor, R. H. (2019). Endoscopic navigation in the clinic: registration in the absence of preoperative imaging. Int J Comput Assist Radiol Surg, 14(9), 1495-1506. doi: <a href=\"https:\/\/doi.org\/10.1007\/s11548-019-02005-0\" class=\"external-link\" rel=\"nofollow\">10.1007\/s11548-019-02005-0<\/a><\/li><li>Smith, B. J., Buatti, J. M., Bauer, C., Ulrich, E. J., Ahmadvand, P., Budzevich, M. M., . . . Beichel, R. R. (2020). Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. Tomography, 6(2), 65-76. doi: <a href=\"https:\/\/doi.org\/10.18383\/j.tom.2020.00004\" class=\"external-link\" rel=\"nofollow\">10.18383\/j.tom.2020.00004<\/a><\/li><li>Stoll, M., Stoiber, E. M., Grimm, S., Debus, J., Bendl, R., &amp; Giske, K. (2016). Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations. PLoS One, 11(12), e0168916. doi: <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0168916\" class=\"external-link\" rel=\"nofollow\">10.1371\/journal.pone.0168916<\/a><\/li><li>Taghanaki, S. A., Duggan, N., Ma, H., Hou, X., Celler, A., Benard, F., &amp; Hamarneh, G. (2018). Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Comput Med Imaging Graph, 63, 52-66. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.compmedimag.2017.12.004\" class=\"external-link\" rel=\"nofollow\">10.1016\/j.compmedimag.2017.12.004<\/a><\/li><li>Thomas, R., Schalck, E., Fourure, D., Bonnefoy, A., &amp; Cervera-Marzal, I. (2021). 2Be3-Net: Combining 2D and 3D Convolutional Neural Networks for 3D PET Scans Predictions. Paper presented at the International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). doi: <a href=\"https:\/\/doi.org\/10.1007\/978-981-16-3880-0_27\" class=\"external-link\" rel=\"nofollow\">10.1007\/978-981-16-3880-0_27<\/a><\/li><li>Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi: <a href=\"https:\/\/doi.org\/10.3389\/fonc.2021.637804\" class=\"external-link\" rel=\"nofollow\">10.3389\/fonc.2021.637804<\/a><\/li><li>Vrtovec, T., Mo\u010dnik, D., Strojan, P., Pernu\u0161, F., &amp; Ibragimov, B. (2020). Auto\u2010segmentation of organs at risk for head and neck radiotherapy planning: from atlas\u2010based to deep learning methods. Medical Physics, 47, e929-e950. doi: <a href=\"https:\/\/doi.org\/10.1002\/mp.14320\" class=\"external-link\" rel=\"nofollow\">10.1002\/mp.14320<\/a><\/li><li>Zschaeck, S., Li, Y., Lin, Q., Beck, M., Amthauer, H., Bauersachs, L., . . . Hofheinz, F. (2020). Prognostic value of baseline [18F]-fluorodeoxyglucose positron emission tomography parameters MTV, TLG and asphericity in an international multicenter cohort of nasopharyngeal carcinoma patients. PLoS One, 15(7), e0236841. doi: <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0236841\" class=\"external-link\" rel=\"nofollow\">10.1371\/journal.pone.0236841<\/a><\/li><\/ul><p><br\/><\/p><\/div><div class=\"tabs-pane \" id=\"68852896e59b680268c40f3b2f6586cc2cee093\" name=\"Versions\" ><h3 id=\"QINHEADNECK-Version4(Current):Updated2020\/09\/15\">Version 4 (Current) : Updated 2020\/09\/15<\/h3><div class=\"table-wrap\"><table class=\"wrapped relative-table confluenceTable\" style=\"width: 55.5202%;\"><colgroup> <col style=\"width: 41.4488%;\"\/> <col style=\"width: 58.5488%;\"\/> <\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\"><span style=\"color: rgb(38,44,49);\">Images and Segmentations<\/span>\u00a0(DICOM\u00a0201.2 GB)<\/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\/6885289\/QIN-HEADNECK%20NBIA-manifest.tcia_20200914.tcia?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:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;CollectionCriteria=QIN-HEADNECK\" 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>(Download requires the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\" rel=\"nofollow\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><\/tr><tr><td colspan=\"1\" class=\"confluenceTd\">Clinical Data\u00a0 (.xlsx 68 kB)<\/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\/6885289\/Batch_01%20and%20Batch_02%20Clinical%20Data_aug242020.xlsx?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><span style=\"color: rgb(33,37,41);\">Added 123 new subjects (Patient IDs = QIN-HeadNeck-02-####).\u00a0 Added missing PT or CT pre-treatment and follow up scans to 28 of the previously existing QIN-HeadNeck-01-#### subjects.\u00a0 Added supporting clinical data in XLSX format for all patients.<\/span><\/p><h3 id=\"QINHEADNECK-Version3:Updated2019\/07\/24\">Version 3: Updated 2019\/07\/24<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td class=\"confluenceTd\">Images (DICOM, 103.5 GB)<\/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\/6885289\/TCIA_QIN-HEADNECK_2019-07-24.tcia?version=3&amp;modificationDate=1563997513437&amp;api=v2\" rel=\"nofollow\"><button class=\"tcia-btn tcia-download-color\"><i class=\"fa fa-cloud-download\" \/> Download<\/button><\/a>\u00a0\n<\/span>\u00a0\u00a0<\/p><\/div><\/td><\/tr><tr><td class=\"confluenceTd\">DICOM Metadata Digest (CSV)<\/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\/6885289\/QIN-HEADNECK_TCIAmanifest_metadata_Jul2019.csv?version=2&amp;modificationDate=1563997454543&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>Lifted restriction from SR object data download.<\/p><h3 id=\"QINHEADNECK-Version2:Updated2017\/12\/06\">Version 2: Updated 2017\/12\/06<\/h3><p><span>Downloads require the <\/span> <a rel=\"nofollow\" href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a> <span>.<\/span><\/p><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 (DICOM, 104 GB)<\/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\/6885289\/QIN-HEADNECK-12-06-2017-doiJNLP-Hb7bP6pf.tcia?version=1&amp;modificationDate=1534786972624&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><tr><td colspan=\"1\" class=\"confluenceTd\">DICOM Metadata Digest (CSV)<\/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\/6885289\/QIN-HEADNECK_MetaData.csv?version=1&amp;modificationDate=1495728510607&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 class=\"auto-cursor-target\">Added associated DICOM SEG, SR, and RWV objects<\/p><h3 id=\"QINHEADNECK-Version1:Updated2015\/08\/20\">Version 1: Updated 2015\/08\/20<\/h3><div class=\"table-wrap\"><table class=\"wrapped confluenceTable\"><colgroup> <col\/> <col\/> <\/colgroup><tbody><tr><th class=\"confluenceTh\">Data Type<\/th><th class=\"confluenceTh\">Download all or Query\/Filter<\/th><\/tr><tr><td class=\"confluenceTd\">Images (DICOM, 102.76 GB)<\/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\/6885289\/TCIA_QIN-HEADNECK_06-22-2015.tcia?version=1&amp;modificationDate=1534787423317&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><tr><td class=\"confluenceTd\">DICOM Metadata Digest (CSV)<\/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\/6885289\/QIN-HEADNECK_MetaData.csv?version=1&amp;modificationDate=1495728510607&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><\/div>\n<\/div>\n<div class=\"cell aside\" data-type=\"aside\">\n<div class=\"innerCell\">\n<p><br\/><\/p><\/div>\n<\/div>\n<\/div>\n<\/div>","versions":false,"additional_resources":"","cancer_locations":["Head-Neck"],"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 TCIA data. If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\"> contact TCIA's Helpdesk<\/a>.\u00a0\n<ul><li>Ahmadvand, P., Duggan, N., B\u00e9nard, F., &amp; Hamarneh, G. (2016). Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface.\u00a0 International Workshop on Machine Learning in Medical Imaging. doi: <a href=\"https:\/\/doi.org\/10.1007\/978-3-319-47157-0_33\">10.1007\/978-3-319-47157-0_33<\/a><\/li><li>Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Clunie, D., Onken, M., Riesmeier, J., . . . Kikinis, R. (2020). Quantitative Imaging Informatics for Cancer Research. JCO Clin Cancer Inform, 4, 444-453. doi:<a href=\"https:\/\/doi.org\/10.1200\/CCI.19.00165\">https:\/\/doi.org\/10.1200\/CCI.19.00165<\/a><\/li><li>Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., . . . Beichel, R. R. (2016). DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET\/CT analysis results in head and neck cancer research. PeerJ, 4, e2057. doi: <a href=\"https:\/\/doi.org\/10.7717\/peerj.2057\">10.7717\/peerj.2057<\/a><\/li><li>Ghattas, A. E. (2017). Medical Imaging Segmentation Assessment via Bayesian Approaches to Fusion, Accuracy and Variability Estimation with Application to Head and Neck Cancer. (PhD). The University of Iowa, Retrieved from <a href=\"http:\/\/ir.uiowa.edu\/etd\/5759\">http:\/\/ir.uiowa.edu\/etd\/5759<\/a><\/li><li>Liang, X., Bassenne, M., Hristov, D. H., Islam, M. T., Zhao, W., Jia, M., . . . Xing, L. (2022). Human-level comparable control volume mapping with a deep unsupervised-learning model for image-guided radiation therapy. Comput Biol Med, 141, 105139. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.compbiomed.2021.105139\">10.1016\/j.compbiomed.2021.105139<\/a><\/li><li>Lv, W., Zhou, Z., Peng, J., Peng, L., Lin, G., Wu, H., . . . Lu, L. (2023). Functional-structural Sub-region Graph Convolutional Network (FSGCN): Application to the Prognosis of Head and Neck Cancer with PET\/CT imaging. Computer Methods and Programs in Biomedicine. doi:\u00a0 <a href=\"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107341\">10.1016\/j.cmpb.2023.107341<\/a><\/li><li>Sinha, A. (2018). Deformable registration using shape statistics with applications in sinus surgery. (Ph. D.). Johns Hopkins University, Retrieved from <a href=\"http:\/\/jhir.library.jhu.edu\/handle\/1774.2\/59202\">http:\/\/jhir.library.jhu.edu\/handle\/1774.2\/59202<\/a><\/li><li>Sinha, A., Billings, S. D., Reiter, A., Liu, X., Ishii, M., Hager, G. D., &amp; Taylor, R. H. (2019). The deformable most-likely-point paradigm. Medical image analysis, 55, 148-164. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.media.2019.04.013\">10.1016\/j.media.2019.04.013<\/a><\/li><li>Sinha et al. Towards automatic initialization of registration algorithms using simulated endoscopy images.\u00a0 <a href=\"https:\/\/paperswithcode.com\/paper\/towards-automatic-initialization-of\"> link to article <\/a><\/li><li>Sinha, A., Ishii, M., Hager, G. D., &amp; Taylor, R. H. (2019). Endoscopic navigation in the clinic: registration in the absence of preoperative imaging. Int J Comput Assist Radiol Surg, 14(9), 1495-1506. doi: <a href=\"https:\/\/doi.org\/10.1007\/s11548-019-02005-0\">10.1007\/s11548-019-02005-0<\/a><\/li><li>Smith, B. J., Buatti, J. M., Bauer, C., Ulrich, E. J., Ahmadvand, P., Budzevich, M. M., . . . Beichel, R. R. (2020). Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. Tomography, 6(2), 65-76. doi: <a href=\"https:\/\/doi.org\/10.18383\/j.tom.2020.00004\">10.18383\/j.tom.2020.00004<\/a><\/li><li>Stoll, M., Stoiber, E. M., Grimm, S., Debus, J., Bendl, R., &amp; Giske, K. (2016). Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations. PLoS One, 11(12), e0168916. doi: <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0168916\">10.1371\/journal.pone.0168916<\/a><\/li><li>Taghanaki, S. A., Duggan, N., Ma, H., Hou, X., Celler, A., Benard, F., &amp; Hamarneh, G. (2018). Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Comput Med Imaging Graph, 63, 52-66. doi: <a href=\"https:\/\/doi.org\/10.1016\/j.compmedimag.2017.12.004\">10.1016\/j.compmedimag.2017.12.004<\/a><\/li><li>Thomas, R., Schalck, E., Fourure, D., Bonnefoy, A., &amp; Cervera-Marzal, I. (2021). 2Be3-Net: Combining 2D and 3D Convolutional Neural Networks for 3D PET Scans Predictions. Paper presented at the International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). doi: <a href=\"https:\/\/doi.org\/10.1007\/978-981-16-3880-0_27\">10.1007\/978-981-16-3880-0_27<\/a><\/li><li>Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T. N., Apfaltrer, P., Tareco Bucho, T. M., . . . Beets-Tan, R. G. H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front Oncol, 11, 637804. doi: <a href=\"https:\/\/doi.org\/10.3389\/fonc.2021.637804\">10.3389\/fonc.2021.637804<\/a><\/li><li>Vrtovec, T., Mo\u010dnik, D., Strojan, P., Pernu\u0161, F., &amp; Ibragimov, B. (2020). Auto\u2010segmentation of organs at risk for head and neck radiotherapy planning: from atlas\u2010based to deep learning methods. Medical Physics, 47, e929-e950. doi: <a href=\"https:\/\/doi.org\/10.1002\/mp.14320\">10.1002\/mp.14320<\/a><\/li><li>Zschaeck, S., Li, Y., Lin, Q., Beck, M., Amthauer, H., Bauersachs, L., . . . Hofheinz, F. (2020). Prognostic value of baseline [18F]-fluorodeoxyglucose positron emission tomography parameters MTV, TLG and asphericity in an international multicenter cohort of nasopharyngeal carcinoma patients. PLoS One, 15(7), e0236841. doi: <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0236841\">10.1371\/journal.pone.0236841<\/a><\/li><\/ul>\n<br\/>","species":["Human"],"collection_title":"QIN-HEADNECK","detailed_description":"<h4>Associated Clinical Metadata<\/h4>\n<ul><li>Structured Report DICOM objects (Modality SR), are available for a subset of these subjects in the DICOM downloads and can be distinguished from image files by the series description \"Clinical Data.\" Note, there is no image preview thumbnail for a Structured Report.<\/li><\/ul>","related_analysis_results":false,"subjects":"279","collection_short_title":"QIN-HEADNECK","data_types":["PT","CT","SR","SEG","RWV"],"date_updated":"2023-09-13","collection_browse_title":"","supporting_data":["Clinical","Image Analyses"],"collection_featured_image":false,"collection_summary":"","collection_acknowledgements":"","collection_funding":"","hide_from_browse_table":[],"_links":{"self":[{"href":"https:\/\/cm.vastapps.dev\/api\/v1\/collections\/5662"}],"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=5662"}],"wp:term":[{"taxonomy":"tcia_citation_tax","embeddable":true,"href":"https:\/\/cm.vastapps.dev\/api\/v1\/citation-tax?post=5662"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}