{"id":1767,"date":"2025-04-23T09:49:00","date_gmt":"2025-04-23T06:49:00","guid":{"rendered":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/?p=1767"},"modified":"2026-04-03T11:11:49","modified_gmt":"2026-04-03T08:11:49","slug":"new-publication-christoforou-a-t-et-al-2025-computers-in-biology-and-medicine","status":"publish","type":"post","link":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/new-publication-christoforou-a-t-et-al-2025-computers-in-biology-and-medicine\/","title":{"rendered":"New Publication Christoforou A.T., et al. 2025 Computers in Biology and Medicine"},"content":{"rendered":"<p>Data-driven decision-making in radiation oncology (RO) relies on integrating real-world data effectively. Synthetic data (SD), generated through machine learning, offers a solution by mimicking real-world data without compromising privacy. This paper presents a general framework for generating, evaluating, and selecting high-quality tabular SD for clinical use, focusing on survival datasets in RO. Five retrospectively collected survival-based RO datasets (n = 1038 recurrent prostate cancer, n = 117 primary localised prostate cancer, n = 48 primary nodal positive (metastasised) prostate cancer, n = 1269 head and neck cancer, n = 353 gliomas) underwent cleaning and preparation. SD was generated using four different machine-learning models, with each model producing multiple variants. These were evaluated for privacy, clinical behaviour, and feature distribution using robust and interpretable metrics, with a single SDset being selected for each real-world dataset using a weighted ranking system.\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110198\" target=\"_blank\" rel=\"noopener\">Read more<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data-driven decision-making in radiation oncology (RO) relies on integrating real-world data effectively. Synthetic data (SD), generated through machine learning, offers a solution by mimicking real-world data<span class=\"excerpt-hellip\"> [\u2026]<\/span><\/p>\n","protected":false},"author":398,"featured_media":1768,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-1767","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-and-events"],"modified_by":"kelvege","publishpress_future_action":{"enabled":false,"date":"2026-06-12 08:58:40","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category","extraData":[]},"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/posts\/1767","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/users\/398"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/comments?post=1767"}],"version-history":[{"count":1,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/posts\/1767\/revisions"}],"predecessor-version":[{"id":1769,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/posts\/1767\/revisions\/1769"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/media\/1768"}],"wp:attachment":[{"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/media?parent=1767"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/categories?post=1767"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ucy.ac.cy\/cancer-biophysics\/wp-json\/wp\/v2\/tags?post=1767"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}