Streamlined 2D multimodal fusion framework predicts 90 day functional outcomes in acute ischemic stroke

Functional outcome prediction after endovascular therapy can support early risk stratification in acute ischemic stroke. We developed a 2D multimodal fusion framework using multicenter data from 405 patients with post-interventional cerebral hyperdensity on immediate non-contrast CT. Radiomic and deep transfer learning features were extracted from the single CT slice showing the largest hyperdensity area and combined with clinical variables using an Extra Trees classifier. In the external test cohort, the fusion model predicted unfavorable 90-day outcome, defined as a modified Rankin Scale score greater than 2, with an AUC of 0.803, specificity of 88.0%, positive predictive value of 93.3%, and sensitivity of 60.9%. SHAP analysis identified malignant cerebral edema, age, baseline NIHSS, reperfusion status, and imaging intensity descriptors as influential factors. By reducing dependence on 3D segmentation, this framework provides an early phase (within 5 days) prognostic assessment tool for individualized in-hospital risk stratification after thrombectomy.