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
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.
Abstract
Objective 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). Methods 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 = 125) and non-AIS (n = 273) 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. Results In the training cohort, the combined model achieved the highest area under the curve (AUC) (0.900, 95% CI: 0.860–0.940), followed by the radiomics model (0.862, 95% CI: 0.812–0.912) and the clinical model (0.775, 95% CI: 0.720–0.830). In the testing cohort, the radiomics model achieved the highest AUC (0.854, 95% CI: 0.757–0.950), followed by the combined model (0.841, 95% CI: 0.748–0.935) and the clinical model (0.696, 95% CI: 0.568–0.824). Conclusion 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.
Background/Objectives: Multimodal large language models are emerging tools for medical image interpretation, but their performance on isolated diffusion-weighted imaging (DWI) for acute ischemic stroke remains largely unexplored. We evaluated GPT-5 for stroke detection and vascular territory classification on preselect...
Emre Gökçen, Mikail Kuşdoğan, M. Erkoç et al.· Journal of Clinical Medicine· 0 citations
OBJECTIVE
On non-contrast computed tomography (NCCT), progressive hypoattenuation can be quantified as percent net water uptake (%NWU), whereas relative fluid attenuated inversion recovery (rFLAIR)-magnetic resonance imaging hyperintensity marks later-stage vasogenic edema and blood-brain-barrier disruption. We compare...
Gabriel Broocks, A. Kemmling, René Werner et al.· Annals of Neurology· 0 citations
Background Standard CT perfusion mismatch and hypoperfusion intensity ratio formulas are undefined when ischemic core or Tmax>6 s equals zero. Such “imaging-quiescent” patients remain uncharacterized. We quantified their prevalence and used DWI to separate CTP-undetected ischemia from DWI-negative presentations. Method...
Song-Qi Hou, Rui Wang, Xiao-Bin Chen et al.· Frontiers in Neuroscience· 0 citations
Objective To develop an interpretable machine learning model based on Diffusion-Weighted Imaging (DWI) radiomics and D-dimer for 90-day outcome prediction after intravenous thrombolysis in acute ischemic stroke (AIS), and to explore candidate upstream transcriptional programs of D-dimer using publicly available rat mid...
Yu-Peng Bai, Qi-Feng Liu, Ruo-Nan Zhan et al.· Frontiers in Neurology· 0 citations
Background and Objectives Diffusion-weighted imaging (DWI)-positive lesions are increasingly recognized as markers of cerebral small vessel disease (cSVD) in acute intracerebral hemorrhage (ICH), but their underlying mechanisms, clinical-radiologic associations, and prognostic significance remain uncertain. We therefor...
Martina Locatelli, Hatice Ozkan, Giulia Avola et al.· Neurology· 0 citations
OBJECTIVE
T2 Fluid-attenuated Inversion Recovery (FLAIR) vascular hyperintensity (FVH) refers to a slow blood flow through the leptomeningeal collaterals. Whether FVH relates to cerebral edema in acute ischemic stroke (AIS) remains unclear. We aimed to i) explore the association between FVH and cerebral edema in patien...
Xiao-Min Wu, Shuang-Fang Fang, Mu-Lei Yang et al.· European Journal of Radiolog...· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.