{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:49:42Z","timestamp":1782452982270,"version":"3.54.5"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute kidney injury (AKI) is common and prognostically relevant after out-of-hospital cardiac arrest (OHCA), yet early risk prediction at admission remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To assess whether radiomics features from dual-phase contrast-enhanced CT obtained at admission predict AKI after non-traumatic OHCA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective single-center study included consecutive non-traumatic OHCA patients with return of spontaneous circulation undergoing standardized admission dual-phase CT. AKI within 5 days was defined by KDIGO criteria or renal replacement therapy. Bilateral whole-kidney masks were generated using TotalSegmentator, reviewed, and corrected if necessary. Radiomics features were extracted with PyRadiomics within 3DSlicer. Features with poor reproducibility (CCC\u2009&amp;lt;\u20090.75) were excluded. Arterial-venous difference features were computed. Data were split into a training cohort and test cohort. Feature selection used mRMR followed by LASSO regression, yielding seven predictors for training of a logistic regression (LR) model, a support vector machine (SVM), and a k-nearest neighbors (kNN) model. Additionally, a limited clinical variable LR model and a combined model were evaluated. Stability was assessed by\n                      <jats:italic>post hoc<\/jats:italic>\n                      repeated resampling of the primary radiomics LR model across 100 stratified 70\/30 splits, summarizing repetitions yielding 3\u20139 LASSO-selected features.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 383 screened patients, 155 were included, of which 47 (30.3%) developed AKI. In the test cohort, radiomics-LR showed the highest AUC (0.783), with kNN (0.778) and SVM (0.757) performing comparable. The limited clinical model performed poorly (AUC 0.549), and the combined model (AUC 0.779) did not materially improve upon radiomics alone. In the repeated resampling analysis, 29 of 100 repetitions fulfilled the predefined feature-count criterion. Among these repetitions, median test-set AUC was 0.663 (IQR, 0.623\u20130.699), indicating moderate but variable discriminatory performance and limited feature-selection stability. Most selected predictors were arterial-venous difference features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This exploratory single-center proof-of-concept study suggests that admission dual-phase CT radiomics may contain information relevant to early AKI risk stratification after OHCA, with arterial-venous difference features appearing particularly informative. However, limited stability of feature selection and model performance indicates that these findings should be considered hypothesis-generating and require methodological refinement, comparison with more comprehensive clinical models, and external validation in prospective multicenter cohorts before clinical application can be considered.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fradi.2026.1875412","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:44:40Z","timestamp":1782452680000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-phase CT radiomics for acute kidney injury prediction after out-of-hospital cardiac arrest"],"prefix":"10.3389","volume":"6","author":[{"given":"Michael","family":"Scheschenja","sequence":"first","affiliation":[{"name":"Diagnostic and Interventional Radiology, Marburg University Hospital, Philipps-University 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for Emergency Medicine, Marburg University Hospital, Philipps-University Marburg","place":["Marburg, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jarmila","family":"Jedelsk\u00e1","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology, Marburg University Hospital, Philipps-University Marburg","place":["Marburg, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"K\u00f6nig","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology, Marburg University Hospital, Philipps-University Marburg","place":["Marburg, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas H.","family":"Mahnken","sequence":"additional","affiliation":[{"name":"Diagnostic and Interventional Radiology and Nuclear Medicine, St. Josef-Hospital, University Hospital Bochum","place":["Bochum, 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