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Med. Biol."],"published-print":{"date-parts":[[2026,2,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Patients with locally advanced non-small cell lung cancer (LA-NSCLC) exhibit heterogeneous prognoses despite receiving standard treatments, highlighting the need for more reliable prognostic biomarkers. This study aims to develop and validate OmicsMap model, a deep radiomics biomarkers derived from computed tomography images for the prediction of progression-free survival (PFS) in LA-NSCLC patients.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We retrospectively analyzed data from 329 LA-NSCLC patients who underwent definitive radiotherapy. The cohort was randomly divided into development (\n                    <jats:italic>N<\/jats:italic>\n                    = 220) and independent testing set (\n                    <jats:italic>N<\/jats:italic>\n                    = 109). The prognostic signature was derived from integrated radiomics features extracted from both the primary tumor and involved lymph nodes, and inter-patient radiomics feature interactions. To achieve this, high-dimensional radiomics data from all patients were transformed into structured two-dimensional representations, termed OmicsMap, wherein radiomics feature interactions were encoded within the pixelated configuration. Deep radiomics features from the OmicsMaps were then extracted using a convolutional neural network for prognostic prediction. Model performance was evaluated by time-dependent area under the receiver operating characteristic curves area under the curve (AUC). Kaplan-Meier curves were plotted and hazard ratios (HR) were calculated via Cox proportional hazards model.\n                    <jats:italic>Main results.<\/jats:italic>\n                    The OmicsMap model achieved time-dependent AUCs of 0.76, 0.78 and 0.76 at 1, 2 and 3 years in the independent testing set, significantly outperforming the clinical model (AUC: 0.57, 0.57, 0.64;\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.05). The proposed model improved predictive discrimination with 7.69% increase in C-index over conventional radiomics approaches. It effectively stratified patients into high-risk and low-risk subgroups for both PFS (\n                    <jats:italic>p<\/jats:italic>\n                    &lt; 0.001, HR = 0.380) and overall survival (\n                    <jats:italic>p<\/jats:italic>\n                    = 0.0021, HR = 0.525) in the testing set.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The proposed OmicsMap model provides a novel paradigm for enhancing prognostic prediction in patients with LA-NSCLC. By improving risk stratification, the framework may help inform clinical decision-making and support future efforts toward more individualized management strategies.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae3b94","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T22:53:21Z","timestamp":1769036001000},"page":"035007","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep radiomics for prognostic prediction in locally advanced non-small cell lung cancer by leveraging OmicsMap-based image representation"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8622-4770","authenticated-orcid":true,"given":"Runping","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wuyan","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6259-632X","authenticated-orcid":true,"given":"Md Tauhidual","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueru","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuwei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6725-6906","authenticated-orcid":true,"given":"Xuejun","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2536-5359","authenticated-orcid":true,"given":"Lei","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"pmbae3b94bib1","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1200\/JCO.2009.26.2543","type":"journal-article","article-title":"Meta-analysis of concomitant versus sequential radiochemotherapy in locally advanced non-small-cell lung cancer","volume":"28","author":"Auperin","year":"2010","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-10-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-01-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-02-04","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}