{"id":1433,"date":"2026-03-15T11:13:37","date_gmt":"2026-03-15T11:13:37","guid":{"rendered":"https:\/\/perfusfind.com\/ic\/?p=1433"},"modified":"2026-03-15T11:18:15","modified_gmt":"2026-03-15T11:18:15","slug":"radiomics-enhanced-modelling-approach-for-predicting-the-need-for-ecmo-in-ards-patients-a-retrospective-cohort-study","status":"publish","type":"post","link":"https:\/\/perfusfind.com\/ic\/index.php\/2026\/03\/15\/radiomics-enhanced-modelling-approach-for-predicting-the-need-for-ecmo-in-ards-patients-a-retrospective-cohort-study\/","title":{"rendered":"Radiomics-enhanced modelling approach for predicting the need for ECMO in ARDS patients: a retrospective cohort study"},"content":{"rendered":"<h3 id=\"ember63\" class=\"ember-view reader-text-block__heading-3\">Predicting ECMO Before It\u2019s Too Late: When Radiomics Meets Critical Care<\/h3>\n<hr class=\"reader-divider-block__horizontal-rule\" \/>\n<h3 id=\"ember64\" class=\"ember-view reader-text-block__heading-3\">\ud83e\ude7a Abstract<\/h3>\n<p id=\"ember65\" class=\"ember-view reader-text-block__paragraph\">The decision to initiate ECMO in patients with severe ARDS remains one of the most challenging and time-sensitive in critical care. In this retrospective cohort of 375 adults with COVID-19\u2013associated ARDS, researchers from Germany explored whether combining <strong>quantitative CT radiomics<\/strong> with <strong>clinical parameters<\/strong> could predict the future need for ECMO at the time of ICU admission. Three logistic regression models were developed \u2014 imaging-only, clinical-only, and a combined model \u2014 and validated in a temporally separate cohort. The combined model demonstrated the best performance (AUROC 0.705), identifying patients more than twice as likely to require ECMO.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-1439 size-large\" src=\"https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886437496-706x1024.png\" alt=\"\" width=\"706\" height=\"1024\" srcset=\"https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886437496-706x1024.png 706w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886437496-207x300.png 207w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886437496-768x1114.png 768w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886437496.png 1034w\" sizes=\"(max-width: 706px) 100vw, 706px\" \/><\/p>\n<h3 id=\"ember67\" class=\"ember-view reader-text-block__heading-3\">10 Key Insights:<\/h3>\n<p id=\"ember69\" class=\"ember-view reader-text-block__paragraph\"><strong>1\ufe0f\u20e3 Study Design:<\/strong> This was a single-center, retrospective study conducted in a tertiary ICU, with two cohorts separated in time to strengthen external validity. Inclusion required confirmed COVID-19, ARDS (Berlin definition), and ICU admission. Patients were analyzed from March 2020 to March 2022.<\/p>\n<p id=\"ember70\" class=\"ember-view reader-text-block__paragraph\"><strong>2\ufe0f\u20e3 Model Architecture:<\/strong> Three prediction models were developed using logistic regression:<\/p>\n<ul>\n<li><strong>Imaging model:<\/strong> based solely on CT-radiomics features.<\/li>\n<li><strong>Clinical model:<\/strong> age, mean airway pressure (Pmean), lactate, and C-reactive protein (CRP).<\/li>\n<li><strong>Combined model:<\/strong> integration of both data types to leverage physiologic and structural information.<\/li>\n<\/ul>\n<p id=\"ember72\" class=\"ember-view reader-text-block__paragraph\"><strong>3\ufe0f\u20e3 Radiomics Methodology:<\/strong> Using semi-automated segmentation, 42 lung regions of interest were analyzed. Over <strong>590 quantitative CT features<\/strong>\u2014including lung aeration, geometric distribution, and tissue density\u2014were extracted and refined through correlation analysis and machine learning (MRMR feature selection).<\/p>\n<p id=\"ember73\" class=\"ember-view reader-text-block__paragraph\"><strong>4\ufe0f\u20e3 Imaging Biomarker:<\/strong> The <strong>proportion of normally aerated lung tissue<\/strong> emerged as the single most predictive imaging variable. This aligns with the concept that patients with reduced aerated lung volume have less recruitable lung and are more likely to require extracorporeal support.<\/p>\n<p id=\"ember74\" class=\"ember-view reader-text-block__paragraph\"><strong>5\ufe0f\u20e3 Clinical Predictors:<\/strong> Age, Pmean, lactate, and CRP independently correlated with ECMO need. Elevated lactate and inflammatory markers paralleled systemic severity, while higher Pmean reflected increased respiratory system load and mechanical stress.<\/p>\n<p id=\"ember75\" class=\"ember-view reader-text-block__paragraph\"><strong>6\ufe0f\u20e3 Performance Metrics:<\/strong> In the validation cohort, AUROC values were:<\/p>\n<ul>\n<li>Imaging model: <strong>0.639<\/strong><\/li>\n<li>Clinical model: <strong>0.674<\/strong><\/li>\n<li>Combined model: <strong>0.705<\/strong><\/li>\n<\/ul>\n<p id=\"ember77\" class=\"ember-view reader-text-block__paragraph\">The combined model achieved <strong>68% sensitivity<\/strong> and <strong>59% specificity<\/strong>, suggesting moderate predictive accuracy suitable for clinical triage rather than absolute determination.<\/p>\n<p id=\"ember78\" class=\"ember-view reader-text-block__paragraph\"><strong>7\ufe0f\u20e3 Time-to-ECMO Analysis:<\/strong> Kaplan\u2013Meier and competing-risk models revealed a significant difference in ECMO-free survival between predicted \u201chigh-risk\u201d and \u201clow-risk\u201d groups. The subhazard ratio for ECMO was 2.11 in the high-risk cohort, while the risk of death before ECMO was lower (SHR 0.41), indicating early identification of progression-prone patients.<\/p>\n<p id=\"ember79\" class=\"ember-view reader-text-block__paragraph\"><strong>8\ufe0f\u20e3 Clinical Implications:<\/strong> The model could enable earlier <strong>transfer to ECMO-capable centers<\/strong>, improved <strong>resource allocation<\/strong>, and better <strong>patient stratification<\/strong> during crises like the COVID-19 pandemic. Importantly, all model inputs are available within 24 hours of ICU admission, supporting real-world usability.<\/p>\n<p id=\"ember80\" class=\"ember-view reader-text-block__paragraph\"><strong>9\ufe0f\u20e3 Limitations:<\/strong> Single-center design, COVID-specific cohort, and absence of prospective validation limit generalizability. The authors caution that radiomics results may vary with CT protocols and segmentation algorithms.<\/p>\n<p id=\"ember81\" class=\"ember-view reader-text-block__paragraph\"><strong>\ud83d\udd1f Future Outlook:<\/strong> This study underscores the growing potential of <strong>AI-driven imaging analytics<\/strong> to guide ECMO triage. Future multicenter trials should explore model calibration across non-COVID ARDS and incorporate longitudinal physiological data for adaptive prediction.<\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-1440 size-large\" src=\"https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-755x1024.png\" alt=\"\" width=\"755\" height=\"1024\" srcset=\"https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-755x1024.png 755w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-221x300.png 221w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-768x1042.png 768w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397.png 1106w\" sizes=\"(max-width: 755px) 100vw, 755px\" \/><\/p>\n<h3 id=\"ember83\" class=\"ember-view reader-text-block__heading-3\">Clinical Takeaways<\/h3>\n<ul>\n<li>Early identification of ECMO candidates remains vital \u2014 <strong>time is lung.<\/strong><\/li>\n<li><strong>Hybrid models<\/strong> integrating radiomics and simple labs can modestly outperform traditional scoring systems.<\/li>\n<li>This approach supports <em>data-informed clinical judgment<\/em>, not its replacement.<\/li>\n<li>Broader validation could transform how intensivists anticipate ECMO needs within ARDS care pathways.<\/li>\n<\/ul>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-1440 size-large\" src=\"https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-755x1024.png\" alt=\"\" width=\"755\" height=\"1024\" srcset=\"https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-755x1024.png 755w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-221x300.png 221w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397-768x1042.png 768w, https:\/\/perfusfind.com\/ic\/wp-content\/uploads\/2026\/03\/1759886488397.png 1106w\" sizes=\"(max-width: 755px) 100vw, 755px\" \/><\/p>\n<h3 id=\"ember86\" class=\"ember-view reader-text-block__heading-3\">Learn More<\/h3>\n<p id=\"ember87\" class=\"ember-view reader-text-block__paragraph\">\ud83d\udcd6 Read the full open-access article: <a class=\"aAIDarVlJjjXEMUykBwFgJhXtzETCscLwRim \" tabindex=\"0\" href=\"https:\/\/doi.org\/10.1038\/s41598-025-21287-w\" target=\"_self\" data-test-app-aware-link=\"\">Scientific Reports, 2025<\/a> \ud83c\udfa5 Watch our upcoming deep-dive discussion this week on YouTube and LinkedIn Live: <em>\u201cRadiomics and ARDS: Predicting the Future of ECMO Triage.\u201d<\/em><\/p>\n<hr class=\"reader-divider-block__horizontal-rule\" \/>\n<h3 id=\"ember88\" class=\"ember-view reader-text-block__heading-3\">\ud83d\udde3\ufe0f Discussion<\/h3>\n<p id=\"ember89\" class=\"ember-view reader-text-block__paragraph\">Can radiomics-guided algorithms truly anticipate ECMO need early enough to change outcomes\u2014or will clinical gestalt remain the gold standard in deciding when to escalate support?<\/p>\n<p>&nbsp;<\/p>\n<p id=\"ember94\" class=\"ember-view reader-text-block__paragraph\"><strong>Open Access<\/strong> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <a class=\"aAIDarVlJjjXEMUykBwFgJhXtzETCscLwRim \" tabindex=\"0\" href=\"http:\/\/creativecommons.org\/licenses\/by\/4.0\/\" target=\"_self\" data-test-app-aware-link=\"\">http:\/\/creativecommons.org\/licenses\/by\/4.0\/<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Predicting ECMO Before It\u2019s Too Late: When Radiomics Meets Critical Care \ud83e\ude7a Abstract The decision to initiate ECMO in patients with severe ARDS remains one of the most challenging and time-sensitive in critical care. In this retrospective cohort of 375 adults with COVID-19\u2013associated ARDS, researchers from Germany explored whether combining quantitative CT radiomics with clinical [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1441,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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