ArticleFrontiers in neuroscience2025
Development and validation of a CT-based multi-omics nomogram for predicting hospital discharge outcomes following mechanical thrombectomy.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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8 authors.
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Abstract
Objective: This study aimed to develop a multi-omics nomogram that combines clinical parameters, radiomics, and deep transfer learning (DTL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict functional outcomes at discharge. Methods: This study enrolled 246 patients with HIM who underwent MT. Patients were randomly assigned to a training cohort ( Results: The nomogram achieved AUCs of 0.995 (95% CI: 0.989-1.000) in training, 0.959 (95% CI: 0.910-1.000) in validation, and 0.894 (95% CI: 0.807-0.981) in test cohorts. Our nomogram significantly outperformed clinical, radiomics, and DTL models, as well as physician assessments (senior physicians: 0.693, Conclusion: This multi-omics nomogram, integrating HIM-derived, clinical, radiomic, and DTL features, accurately predicts post-MT discharge outcomes, enabling early identification of high-risk patients and optimizing management to improve prognosis.
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