ArticleFrontiers in neurology2026
Prediction of functional outcome after mechanical thrombectomy using radiomics and deep learning features derived from intra-thrombus and peri-thrombus regions.
Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Although mechanical thrombectomy (MT) achieves high recanalization rates in acute ischemic stroke (AIS), functional outcomes remain highly inconsistent. We aimed to evaluate the effectiveness of radiomics and deep learning features (DLFs) extracted from both intra-thrombus and peri-thrombus regions on baseline CT in predicting prognosis following MT. Materials and methods: This retrospective, multicenter study included 471 AIS patients who underwent MT. Patients from Center A ( Results: A total of 191 patients (40.6%) achieved a favorable outcome (90-day mRS ≤ 2). In the test cohort, the late-fusion combined model achieved the highest discriminative performance (AUC = 0.836, 95% CI: 0.769-0.891). It significantly outperformed both the peri-thrombus model (AUC = 0.758, Conclusion: The late-fusion combined model, integrating CT-derived radiomic and DLFs from both intra-thrombus and peri-thrombus regions, may provide an effective approach for predicting 90-day functional outcome after mechanical thrombectomy.
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