{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:04:11Z","timestamp":1789535051908,"version":"build-2803163510"},"reference-count":17,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and internally validate a T2-fluid-attenuated inversion recovery (T2-FLAIR) white matter hyperintensity (WMH) radiomics model, alone and in combination with baseline clinical variables, for predicting diffusion-weighted imaging (DWI)-confirmed acute ischemic stroke (AIS) within 90 days after an index DWI-negative transient ischemic attack (TIA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This single-center retrospective study included 398 patients initially diagnosed with DWI-negative TIA between January 2021 and May 2025 who underwent repeat brain magnetic resonance imaging with DWI within 90 days because of recurrent transient neurological symptoms and were classified into AIS (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009125) and non-AIS (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009273) groups according to follow-up DWI and apparent diffusion coefficient findings. Baseline WMH regions of interest were segmented on index T2-FLAIR images, and 1,781 radiomics features were extracted. Reproducible features were screened by least absolute shrinkage and selection operator regression, and 21 features were retained to calculate a radiomics score. Clinical, radiomics, and combined models were constructed using a support vector machine, with AIS defined as the positive class.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the training cohort, the combined model achieved the highest area under the curve (AUC) (0.900, 95% CI: 0.860\u20130.940), followed by the radiomics model (0.862, 95% CI: 0.812\u20130.912) and the clinical model (0.775, 95% CI: 0.720\u20130.830). In the testing cohort, the radiomics model achieved the highest AUC (0.854, 95% CI: 0.757\u20130.950), followed by the combined model (0.841, 95% CI: 0.748\u20130.935) and the clinical model (0.696, 95% CI: 0.568\u20130.824).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These preliminary findings indicate that T2-FLAIR WMH radiomics may provide promising internal discrimination for predicting 90-day DWI-confirmed AIS after DWI-negative TIA in this selected high-risk cohort. The incremental value of adding clinical variables to radiomics was not confirmed in the internal testing cohort, and prospective multicenter validation is required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1913196","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T04:29:48Z","timestamp":1789532988000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["White matter hyperintensity radiomics predicts 90-day diffusion-weighted imaging-confirmed acute ischemic stroke after diffusion-weighted imaging-negative transient ischemic attack: preliminary findings from a retrospective machine-learning study"],"prefix":"10.3389","volume":"13","author":[{"given":"Lian","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, Jiangjin Hospital, Chongqing University of Chinese Medicine","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyun","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Jiangjin Hospital, Chongqing University of Chinese Medicine","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geriatrics, Jiangjin Hospital, Chongqing University of Chinese Medicine","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Jiangjin Hospital, Chongqing University of Chinese Medicine","place":["Chongqing, 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