{"id":45881,"date":"2023-11-20T05:40:01","date_gmt":"2023-11-20T11:40:01","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/rider-lungct-seg\/"},"modified":"2025-03-20T13:39:01","modified_gmt":"2025-03-20T18:39:01","slug":"rider-lungct-seg","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/rider-lungct-seg\/","title":{"rendered":"RIDER-LUNGCT-SEG"},"featured_media":7119,"template":"","class_list":["post-45881","tcia_analysis_result","type-tcia_analysis_result","status-publish","has-post-thumbnail"],"cancer_types":["Lung"],"citations":[45867,45869,9225],"result_doi":"10.7937\/tcia.2020.jit9grk8","result_download_info":"","result_downloads":[45871],"version_change_log_archived":"Version 2 (Current): Updated 2021\/10\/28\r\nData TypeDownload all or Query\/FilterGross Tumor Volume Segmentation - (DICOM RTSTRUCT and SEG,\u00a0 912 MB)Corresponding Original CT Images\u00a0from\u00a0RIDER Lung CT - (DICOM, 7 GB)\r\nThe authors of this dataset agreed to change the license to permit commercial use.\u00a0 The actual dataset remains unchanged.Version 1: Updated 2020\/02\/13\r\nData TypeDownload all or Query\/FilterGross Tumor Volume Segmentation - (DICOM RTSTRUCT and SEG,\u00a0 912 MB)Corresponding Original CT Images\u00a0from\u00a0RIDER Lung CT - (DICOM, 7 GB)","versions":[45879],"additional_resources":"","cancer_locations":["Chest"],"publications_related":"The Collection authors recommend these readings to give context to this dataset","result_page_accessibility":"Public","detailed_description":"<ul>\r\n \t<li>(RIDER-2283289298) only has segmentations associated with the retest.<\/li>\r\n \t<li>(RIDER-5195703382) only has segmentations associated with the test.<\/li>\r\n \t<li>(RIDER-8509201188) only has segmentations associated with the test.<\/li>\r\n \t<li>(RIDER-9762593735) not included in the data set due to missing delineations.<\/li>\r\n<\/ul>","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/commons.datacite.org\/doi.org\/10.7937\/tcia.2020.jit9grk8\">a list of publications<\/a>\u00a0that leveraged this dataset. If you have a manuscript you\u2019d like to add please\u00a0<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA\u2019s Helpdesk<\/a>.","result_title":"RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","species":["Human"],"version_number":"2","date_updated":"2021-10-28","related_collections":[43451],"result_short_title":"RIDER-LungCT-Seg","subjects":"31","related_analysis_results":false,"result_browse_title":"RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach (RIDER-LungCT-Seg)","supporting_data":["Tumor segmentations"],"version_change_log":"<p class=\"auto-cursor-target\">The authors of this dataset agreed to change the license to permit commercial use.\u00a0 The actual dataset remains unchanged.<\/p>","collections":"Below is a list of the Collections used in these analyses:\r\n<table><colgroup> <col \/> <col \/> <col \/><\/colgroup>\r\n<tbody>\r\n<tr>\r\n<th>Source Data Type<\/th>\r\n<th>Download all or Query\/Filter<\/th>\r\n<th>License<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>Corresponding Original CT Images\u00a0from\u00a0<a href=\"https:\/\/cancerimagingarchive.net\/collection\/rider-lung-ct\/\" target=\"_blank\" rel=\"noopener\">RIDER Lung CT<\/a> - (DICOM, 7 GB)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/RIDER-Lung-CT-Original-Scans-for-Leonard-Wee-Feb-10-2020-.tcia\" download=\"RIDER-Lung-CT-Original-Scans-for-Leonard-Wee-Feb-10-2020-.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n\r\n(Requires\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>.)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<ul>\r\n \t<li><a href=\"https:\/\/cancerimagingarchive.net\/collection\/rider-lung-ct\/\" target=\"_blank\" rel=\"noopener\">RIDER Lung CT<\/a><\/li>\r\n<\/ul>","result_summary":"This dataset contains images from 31 out of the 32 non-small cell lung cancer (NSCLC) patients in the\u00a0<a href=\"\/collection\/rider-lung-ct\/\" target=\"_blank\" rel=\"noopener\">RIDER Lung CT<\/a>\u00a0collection on TCIA. For these subjects a radiation oncologist was blinded to the all delineations of the 3D volume of the gross tumor volume. They were then asked to manually delineate the gross tumour volume in both the test image and the re-test image. The process was repeated using an in-house autosegmentation method. There is no clinical outcome data associated with this dataset.\r\n\r\nThis dataset refers to the RIDER dataset of the study published in Nature Communications (<a href=\"http:\/\/doi.org\/10.1038\/ncomms5006\">http:\/\/doi.org\/10.1038\/ncomms5006<\/a>). In short, this publication used the dataset to select for repeatable radiomics features in a test-retest context. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In the published analysis, 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.\r\n\r\n&nbsp;\r\n\r\nOther data sets in the Cancer Imaging Archive that were used in the same\u00a0<a href=\"http:\/\/www.nature.com\/ncomms\/2014\/140603\/ncomms5006\/full\/ncomms5006.html\">study published in Nature Communications<\/a>:\u00a0<a href=\"\/collection\/nsclc-radiomics\/\" target=\"_blank\" rel=\"noopener\">NSCLC-Radiomics<\/a>,\u00a0<a href=\"\/collection\/nsclc-radiomics-genomics\/\" target=\"_blank\" rel=\"noopener\">NSCLC-Radiomics-Genomics<\/a>,\u00a0<a href=\"\/collection\/nsclc-radiomics-interobserver1\/\" target=\"_blank\" rel=\"noopener\">NSCLC-Radiomics-Interobserver1<\/a>,\u00a0<a href=\"\/collection\/head-neck-radiomics-hn1\/\" target=\"_blank\" rel=\"noopener\">HEAD-NECK-RADIOMICS-HN1<\/a>.","collection_downloads":[45873],"result_featured_image":{"ID":"7119","post_author":"29","post_date":"2023-09-13 09:32:26","post_date_gmt":"2023-09-13 14:32:26","post_content":"","post_title":"NSCLC-RADIOMICS-GRAPHIC","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"nsclc-radiomics-graphic","to_ping":"","pinged":"","post_modified":"2023-12-04 12:50:07","post_modified_gmt":"2023-12-04 18:50:07","post_content_filtered":"","post_parent":"42417","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/NSCLC-RADIOMICS-GRAPHIC.jpg","menu_order":"0","post_type":"attachment","post_mime_type":"image\/jpeg","comment_count":"0","pod_item_id":"7119"},"result_acknowledgements":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45881","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_analysis_result"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45881\/revisions"}],"predecessor-version":[{"id":47047,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45881\/revisions\/47047"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/7119"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45881"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}