Combined clinical and DWI radiomics model for predicting 90-day functional outcomes after intravenous thrombolysis in acute ischemic stroke patients - Yupeng Bai; Qifeng Liu; Ruonan Zhan; Qiaoqiao Xu; Chunhua Xia; Yinfeng Qian (Frontiers in Neurology, 2026)
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 middle cerebral artery occlusion (MCAO) datasets as an exploratory, hypothesis-generating step. Methods This retrospective study included 115 AIS patients treated with intravenous thrombolysis (January 2024–September 2025). Patients were stratified by 90-day modified Rankin Scale (mRS) scores into favorable (0–2) and unfavorable ( 1 – 4 ) outcome groups. DWI radiomic features were extracted and selected to construct four machine learning models (Logistic Regression, Decision Tree, LDA, LightGBM). Independent clinical risk factors were identified to build a clinical model. The best machine learning model was combined with clinical factors to create a combined model (Combined-LR), interpreted via SHAP. Additionally, transcriptomic analysis of two rat MCAO datasets was conducted to investigate D-dimer-related molecular mechanisms. Results The Radiomics-LR model showed superior performance (training AUC 0.926, testing AUC 0.857). D-dimer was an independent risk factor. Combined-LR achieved the best performance (training AUC 0.939, testing AUC 0.862; optimism-corrected AUC 0.838 by bootstrap, 0.772 by cross-validation), significantly outperforming the clinical model (training P < 0.001, testing P = 0.036). Decision curve analysis indicated that the Combined-LR provided higher net clinical benefit across multiple risk threshold intervals, suggesting greater practical utility for clinical prognostication. Transcriptomic analysis revealed upregulation of complement/coagulation genes, including Serpine1 (PAI-1), C5ar1 , and Itgam (CD11b). Conclusion We established an interpretable machine learning model integrating DWI radiomics and clinical variables for predicting 90-day post-thrombolysis outcomes in AIS. Transcriptomic analyses provided hypothesis-generating insights into D-dimer-related mechanisms. External multi-center validation is required.