ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1643014
This article is part of the Research TopicAdvances in brain diseases: leveraging multimodal data and artificial intelligence for diagnosis, prognosis, and treatmentView all articles
Development and Validation of a CT-Based Multi-Omics Nomogram for Predicting Hospital Discharge Outcomes Following Mechanical Thrombectomy
Provisionally accepted
- the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
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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.This study enrolled 246 patients with HIM who underwent MT. Patients were randomly assigned to a training cohort (n = 197, 80%) and a validation cohort (n = 49, 20%), with an additional internal prospective test cohort (n = 57). A total of 1,834 radiomics features and 25,088 DTL features were extracted from HIM images. Feature selection was conducted using analysis of variance (ANOVA), Pearson's correlation, principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) regression. A support vector machine (SVM)-based nomogram integrating clinical, radiomics, and DTL features was developed to predict functional outcomes at discharge. Its performance was evaluated based on accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve and area under the curve (AUC) analysis, decision curve analysis (DCA), and the DeLong test.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, p = 0.001; junior physicians: 0.600, p < 0.001). 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.
Keywords: prognosis, nomogram, Thrombectomy, Acute ischemic stroke, Multi-detector CT
Received: 07 Jun 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Liu, Hong, Chen, Liu, Wang, Su, Hu and Fu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Sheng Hu, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
Jingjing Fu, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
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