{"id":295,"date":"2023-11-03T00:49:35","date_gmt":"2023-11-03T00:49:35","guid":{"rendered":"https:\/\/xnat-repository.icr.ac.uk\/?page_id=295"},"modified":"2023-12-08T23:29:11","modified_gmt":"2023-12-08T23:29:11","slug":"research-powered-by-xnat-2","status":"publish","type":"page","link":"https:\/\/xnat-repository.icr.ac.uk\/?page_id=295","title":{"rendered":"RADSARC-R"},"content":{"rendered":"<div class=\"n2_clear\"><ss3-force-full-width data-overflow-x=\"body\" data-horizontal-selector=\"body\"><div class=\"n2-section-smartslider fitvidsignore  n2_clear\" data-ssid=\"8\"><div id=\"n2-ss-8-align\" class=\"n2-ss-align\"><div class=\"n2-padding\"><div id=\"n2-ss-8\" data-creator=\"Smart Slider 3\" data-responsive=\"fullwidth\" class=\"n2-ss-slider n2-ow n2-has-hover n2notransition  \">\n        <div class=\"n2-ss-slider-1 n2-ow\">\n            <div class=\"n2-ss-slider-2 n2-ow\">\n                <div class=\"n2-ss-slide-backgrounds n2-ow-all\"><div class=\"n2-ss-slide-background\" data-public-id=\"1\" data-mode=\"fill\"><div class=\"n2-ss-slide-background-image\" data-blur=\"0\" data-opacity=\"100\" data-x=\"50\" data-y=\"50\" data-alt=\"\" data-title=\"\"><picture class=\"skip-lazy\" data-skip-lazy=\"1\"><img decoding=\"async\" src=\"\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/10\/Christina_and_Vonnie_grey_1900x600.png\" alt=\"\" title=\"\" loading=\"lazy\" class=\"skip-lazy\" data-skip-lazy=\"1\"><\/picture><\/div><div data-color=\"RGBA(255,255,255,0)\" style=\"background-color: RGBA(255,255,255,0);\" class=\"n2-ss-slide-background-color\"><\/div><\/div><\/div>                <div class=\"n2-ss-slider-3 n2-ow\">\n                    <svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 1900 600\" data-related-device=\"desktopPortrait\" class=\"n2-ow n2-ss-preserve-size n2-ss-preserve-size--slider n2-ss-slide-limiter\"><\/svg><div data-first=\"1\" data-slide-duration=\"0\" data-id=\"20\" data-slide-public-id=\"1\" data-title=\"Christina_and_Vonnie_grey_1900x600\" class=\"n2-ss-slide n2-ow  n2-ss-slide-20\"><div role=\"note\" class=\"n2-ss-slide--focus\" tabindex=\"-1\">Christina_and_Vonnie_grey_1900x600<\/div><div class=\"n2-ss-layers-container n2-ss-slide-limiter n2-ow\"><div class=\"n2-ss-layer n2-ow n-uc-MYtlW9V16DBZ\" data-sstype=\"slide\" data-pm=\"default\"><\/div><\/div><\/div>                <\/div>\n                            <\/div>\n        <\/div>\n        <\/div><ss3-loader><\/ss3-loader><\/div><\/div><div class=\"n2_clear\"><\/div><\/div><\/ss3-force-full-width><\/div>\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Research powered by XNAT: RADSARC-R<\/h2>\n\n\n\n<p>XNAT is an advanced management system for research images. It uses multiple technologies to provide researchers with the tools they need to process data and view data. The ICR has been at the forefront of developing new capabilities for XNAT, in particular, the ICR-XNAT-OHIF viewer (see <a href=\"https:\/\/www.mdpi.com\/2379-139X\/8\/1\/40\">Doran et al. <em>Tomography<\/em>&nbsp;8.1 (2022<\/a>). The XNAT Team has the privilege of working with outstanding scientists both locally and internationally, and our tools help them to deliver high-impact clinical studies. This page describes work by the Institute of Cancer Research and Royal Marsden recently published in Lancet Oncology and led by <a rel=\"noreferrer noopener\" href=\"https:\/\/www.royalmarsden.nhs.uk\/our-consultants-units-and-wards\/consultant-directory\/prof-christina-messiou\" target=\"_blank\">Prof Christina Messiou<\/a>.<\/p>\n\n\n\n<p>Amani Arthur; Matthew Orton; Robby Emsley; Sharon Vit; Christian Morland-Kelly; Dirk Strauss; Jason Lunn; Simon Doran; Hafida Lmalem; Axelle Nzokirantevye; Saskia Litiere; Sylvie Bonvalot; Rick Haas; Alessandro Gronchi; Dirk Van Gestel; Anne Ducassou; Chandrajit P Raut; Pierre Meeus; Mateusz Spalek; Matthew Hatton; Khin Thway; Cyril Fisher; Robin Jones; Paul Huang; Christina Messiou. Radiomics in sarcoma of the retroperitoneum (RADSARC-R): A novel externally validated CT-based radiomics model for the prediction of histological subtype and tumor grade<em> <a href=\"https:\/\/www.thelancet.com\/journals\/lanonc\/article\/PIIS1470-2045(23)00462-X\/fulltext#%20\" target=\"_blank\" rel=\"noreferrer noopener\">Lancet Oncology<\/a><\/em> <\/p>\n\n\n\n<div class=\"wp-block-group alignfull gutena-pattern has-background-secondary-background-color has-background is-layout-flow wp-container-core-group-is-layout-560a5abf wp-block-group-is-layout-flow\" style=\"padding-top:100px;padding-right:30px;padding-bottom:100px;padding-left:30px\">\n<p class=\"has-text-align-center has-primary-color has-text-color\">FEATURED PUBLICATION: RADSARC-R study<\/p>\n\n\n\n<div class=\"wp-block-media-text has-media-on-the-right is-stacked-on-mobile\" style=\"grid-template-columns:auto 37%\"><div class=\"wp-block-media-text__content\">\n<p>RADSARC-R made the headlines on the Today programme. The BBC&#8217;s Medical Editor, Fergus Walsh, explains more about the significance of this study.<\/p>\n\n\n\n<figure class=\"wp-block-audio\"><audio controls src=\"https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/headline_and_Fergus_Walsh-1.mp3\"><\/audio><\/figure>\n\n\n\n<p>Retroperitoneal sarcomas (RPS) are tumors with unaddressed challenges and a dismal prognosis. Upfront characterisation of the tumor is difficult, and undergrading is common. Radiomics has the potential of non-invasively and globally characterising the \u201cradiological phenotype\u201d of tumours. We aimed to develop and independently validate a radiomics model for the prediction of tumor subtype and grade, based on computed tomography images, for patients with retroperitoneal leiomyosarcoma (LMS) and liposarcoma (LPS).<\/p>\n\n\n\n<p>XNAT was used to assemble a retrospective discovery cohort, containing 170 eligible patients from our tertiary referral centre, and an independent validation cohort of 89 from patients recruited in the EORTC-sponsored STRASS I study. Using the discovery dataset, a radiomics workflow was developed that included manual delineation using the ICR XNAT-OHIF viewer, sub-segmentation, feature extraction and predictive model building, including repeatability testing.<\/p>\n\n\n\n<p>The work resulted in a probabilistic classifier for prediction of sarcoma subtype with an area under the receiver operator characteristic curve (AUROC) of 0.928, and a second predictor of low versus intermediate\/high grade (grade 1 versus grade &gt;1) with AUROC 0.882.<\/p>\n\n\n\n<p>To our knowledge, this is the largest RPS cohort that has been analysed by radiomics and the only one that has been validated in an independent cohort. We anticipate that this study will drive further work on integration with other datasets including molecular or digital pathology and the combination with other prognostic tools. This excellent performance could have significant implications in improving accuracy of diagnosis and risk stratification of RPS patients.<\/p>\n\n\n\n<p>For more press reaction to these exciting discoveries, navigate to:<\/p>\n\n\n\n<p><a rel=\"noreferrer noopener\" href=\"https:\/\/www.bbc.co.uk\/sounds\/play\/p0gpybds\" target=\"_blank\">AI and Cancer Diagnosis &#8211; &#8220;An absolute game changer&#8221; &#8211; BBC Sounds<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.theguardian.com\/science\/2023\/oct\/31\/ai-better-than-biopsy-at-assessing-some-cancers-study-finds\" target=\"_blank\" rel=\"noreferrer noopener\">AI better than biopsy at assessing some cancers, study finds &#8211; Guardian<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.bbc.co.uk\/news\/health-67264350\">Scientists excited by AI tool that grades severity of rare cancer &#8211; BBC News<\/a><\/p>\n\n\n\n<p>Prof Christina Messiou interviewed by Amol Rajan &#8211; BBC Radio 4 <em>Today<\/em><\/p>\n\n\n\n<figure class=\"wp-block-audio\"><audio controls src=\"https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/Christina_Messio_interviewed_by_Amol_Rajan.mp3\"><\/audio><\/figure>\n<\/div><figure class=\"wp-block-media-text__media\"><img loading=\"lazy\" decoding=\"async\" width=\"386\" height=\"1024\" src=\"https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller-386x1024.png\" alt=\"\" class=\"wp-image-304 size-full\" srcset=\"https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller-386x1024.png 386w, https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller-151x400.png 151w, https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller-768x2039.png 768w, https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller-579x1536.png 579w, https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller-771x2048.png 771w, https:\/\/xnat-repository.icr.ac.uk\/wp-content\/uploads\/2023\/11\/RADSARC_taller.png 1112w\" sizes=\"auto, (max-width: 386px) 100vw, 386px\" \/><\/figure><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Research powered by XNAT: RADSARC-R XNAT is an advanced management system for research images. It uses multiple technologies to provide researchers with the tools they need to process data and view data. The ICR has been at the forefront of developing new capabilities for XNAT, in particular, the ICR-XNAT-OHIF viewer (see Doran et al. Tomography&nbsp;8.1 [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":196,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-295","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/295","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=295"}],"version-history":[{"count":14,"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/295\/revisions"}],"predecessor-version":[{"id":783,"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/295\/revisions\/783"}],"up":[{"embeddable":true,"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/196"}],"wp:attachment":[{"href":"https:\/\/xnat-repository.icr.ac.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=295"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}