{"id":42597,"date":"2023-11-20T02:39:19","date_gmt":"2023-11-20T08:39:19","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/collection\/pdmr-833975-119-r\/"},"modified":"2024-02-28T12:17:40","modified_gmt":"2024-02-28T18:17:40","slug":"pdmr-833975-119-r","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/pdmr-833975-119-r\/","title":{"rendered":"PDMR-833975-119-R"},"featured_media":7187,"template":"","class_list":["post-42597","tcia_collection","type-tcia_collection","status-publish","has-post-thumbnail"],"cancer_types":["Pancreatic Ductal Adenocarcinoma"],"citations":[42589,9225],"collection_doi":"10.7937\/TCIA.0ECK-C338","collection_download_info":"","collection_downloads":[42591,42593,42595],"versions":false,"additional_resources":"The National Cancer Institute (NCI) has developed a national repository of Patient-Derived Models (PDMs) comprised of patient-derived xenografts (PDXs), in vitro patient-derived tumor cell cultures (PDCs) and cancer associated fibroblasts (CAFs) as well as patient-derived organoids (PDOrg). These models serve as a resource for public-private partnerships and for academic drug discovery efforts. These PDMs are clinically-annotated with molecular information and made available in the <a href=\"https:\/\/pdmr.cancer.gov\/default.htm\">Patient-Derived Model Repository<\/a>. Data related to the specific subjects in this Collection can be found at:\r\n<ul>\r\n \t<li><a href=\"https:\/\/pdmdb.cancer.gov\/web\/apex\/f?p=101:4:::NO:4:P4_SPECIMENSEQNBR:302\">PDMR-833975-119-R<\/a><\/li>\r\n<\/ul>\r\n&nbsp;\r\n\r\nThe NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=pdmr_833975_119_r\">Imaging Data Commons (IDC)<\/a>\u00a0(Imaging Data)<\/li>\r\n<\/ul>","cancer_locations":["Abdomen"],"collection_page_accessibility":"Public","publications_related":"","version_change_log_archived":"<h3>Version 1 (Current): Updated 2020\/10\/05<\/h3><table><colgroup><col \/><col \/><\/colgroup><tbody><tr><th>Data Type<\/th><th>Download all or Query\/Filter<\/th><\/tr><tr><td>Images (DICOM, 1.7 GB)<\/td><td><div><p><a href=\"\/wp-content\/uploads\/PDMR-833975-119-R-NCI.tcia\" download=\"PDMR-833975-119-R-NCI.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"\/wp-content\/uploads\/PDMR-833975-119-R-NCI.tcia\" download=\"PDMR-833975-119-R-NCI.tcia\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><p>(Download requires\u00a0the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><\/tr><tr><td><p>SOP50101: MRI T2 Weighted Non-Contrast Protocol:\u00a0Single Mouse Pulmonary Gated and Multi-Mouse Non-Gated<\/p><\/td><td><div><p><a href=\"\/wp-content\/uploads\/SOP50101_MRI-T2-Weighted-Non-Contrast-Protocol_Single-Mouse-Pulmonary-Gated-and-Multi-Mouse-Non-Gated.pdf\" download=\"SOP50101_MRI-T2-Weighted-Non-Contrast-Protocol_Single-Mouse-Pulmonary-Gated-and-Multi-Mouse-Non-Gated.pdf\"><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 TCIA data. If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\"> contact the TCIA Helpdesk<\/a>.","related_analysis_results":false,"species":["Mouse"],"version_number":"1","collection_title":"Imaging tissue characterization of a patient derived xenograft model of adenocarcinoma pancreas:","date_updated":"2020-10-05","related_collection":false,"subjects":"20","analysis_results":"","collection_short_title":"PDMR-833975-119-R","data_types":["MR","SR"],"version_change_log":"","collection_browse_title":"PDMR-833975-119-R","detailed_description":"In addition to images, this collection includes Raw Data Storage SOP Class instances <strong>with MR Modality<\/strong>, generated by a Philips MR scanner; this data is not useful to anyone without the proprietary software to interpret it.","supporting_data":["Clinical"],"collection_featured_image":{"ID":"7187","post_author":"29","post_date":"2023-09-13 09:34:23","post_date_gmt":"2023-09-13 14:34:23","post_content":"","post_title":"PDMR-833975-119-R","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"pdmr-833975-119-r-2","to_ping":"","pinged":"","post_modified":"2023-11-30 15:06:05","post_modified_gmt":"2023-11-30 21:06:05","post_content_filtered":"","post_parent":"42597","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/PDMR-833975-119-R.png","menu_order":"0","post_type":"attachment","post_mime_type":"image\/png","comment_count":"0","pod_item_id":"7187"},"collection_summary":"<p>Characterization of tissue using <em>in vivo<\/em> non-invasive imaging is the foundation of Radiology and is clinically used for detection and measurement of disease burden in oncology.\u00a0 With the migration of imaging to digital media the possibility for advanced mathematically based imaging biomarkers was realized. As part of this endeavor, researchers have developed various algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes).\u00a0 However, large datasets are a requirement in this research endeavor in part due to the genomic heterogeneity of tumors in the same histologic classification \u2013 many tumors from different patients are required to have enough with the same genomic characteristics to adequately evaluate the range of imaging variability for a specific genomic pattern.\u00a0 Pre-clinical animal models of patient derived xenografts may be an important resource by providing collections with a more homogenous tumor genome across the collection with companion extensive tumor genomic characterization available, allowing determination of the variability of imaging characteristics for that pattern in different individuals. This dataset of a patient derived xenograft\u00a0model adenocarcinoma pancreas <a href=\"https:\/\/pdmdb.cancer.gov\/web\/apex\/f?p=101:3:::NO:3:P3_PATIENTSEQNBR:236\">PDMR: 833975-119-R<\/a> can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and regrowth after surgical resection.\u00a0\u00a0<\/p><p>In this study we performed a detailed imaging characterization (workflow below) of this model, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm<sup>3<\/sup> size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.<\/p><p>&nbsp;<\/p><p>T2w MRI at 56 days post implant demonstrated a heterogenous tumor with an apparent central hemorrhage and the implant appears to have a defined capsule.\u00a0 On day 98 (post-implant) the tumor increased in size (618%) with very similar imaging characteristics as displayed on day 56.<\/p><p>In contrast, the regrowth following surgical resection of the primary xenograft (105 days post resection) is homogenous, but also appears to have a well-defined capsule.<\/p><p>PET\/CT Characterization of the primary tumor:\u00a0 Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm<sup>3<\/sup>.\u00a0\u00a0 Average\u00a0SUVbw_max values (n=5) were calculated; [<sup>18<\/sup>F]FDG: 1.8 \u00b1 0.2 and [<sup>18<\/sup>F]FLT: 2.8 \u00b1 0.7.<\/p><p>The imaging characteristics of\u00a0this model, which is available from the National Cancer Institute Patient-Derived Models\u00a0Repository (<a href=\"https:\/\/pdmr.cancer.gov\/\">https:\/\/pdmr.cancer.gov\/<\/a>), is highly favorable for preclinical research studies when used in conjunction with non-contrast T2 weighted MRI.<\/p>","collection_acknowledgements":"<p>We would like to acknowledge the individuals and institutions that have provided data for this collection:<\/p><ul><li><p>Frederick National Laboratory for Cancer Research \u2013 Special Thanks to Joseph D. Kalen, PhD, Lilia V. Ileva, MS, Lisa A Riffle, Nimit Patel, Keita Saito, PhD, Yvonne Evrard, PhD, Jessica Phillips, Simone Difilippantonio, PhD, Chelsea Sanders, Amy James, Lia Thang, Ulrike Wagner, Yanling Liu, PhD, John B. Freymann, and Justin Kirby.<\/p><\/li><li><p>Division of Cancer Therapeutics and Diagnosis\/National Cancer Institute - James L. Tatum, MD, Paula M Jacobs, PhD, Melinda G. Hollingshead, DVM, and James H. Doroshow, MD<\/p><\/li><li><p>PixelMed Publishing \u2013 Special Thanks to David A. Clunie, MD<\/p><\/li><li><p>University of Arkansas for Medical Sciences \u2013 Special Thanks to Kirk E. Smith<\/p><\/li><li><p>This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract Number HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.<\/p><\/li><\/ul>","collection_funding":"","hide_from_browse_table":"0","program":["PDMR"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42597","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\/42597\/revisions"}],"predecessor-version":[{"id":47885,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/42597\/revisions\/47885"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/7187"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=42597"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}