Multimodal data fusion of dual-modal DWI-ADC MRI and clinical variables for prognostic prediction in acute ischemic stroke.

Objectives: To develop and internally evaluate a probability-level stacked ensemble model integrating diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) magnetic resonance imaging (MRI)-based imaging predictions with routinely available clinical predictions to predict 90-day poor functional outcome in patients with acute ischemic stroke. Methods: This retrospective single-center study included 562 patients with acute ischemic stroke who underwent DWI and ADC MRI within 1 to 7 days after symptom onset. Poor outcome was defined as 90-day modified Rankin Scale >2. Patients were assigned at the patient level to training ( Results: In the held-out internal test set, the fusion model achieved the highest numerical AUC (0.951, 95% CI: 0.908-0.994), with sensitivity of 0.889 and specificity of 0.911. The DeLong test showed that the fusion model significantly outperformed the imaging model ( Conclusion: The fusion model showed high internal discrimination and improved performance compared with imaging alone, but its incremental value over clinical models was limited in calibration and decision curve analyses. External validation, recalibration, and prospective evaluation are required before broader clinical use.