A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke - a two-center study

Aug 5, 2026

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A CT-based machine learning framework was developed to estimate the likelihood of malignant cerebral edema (MCE) in patients with acute ischemic stroke using unenhanced computed tomography and clinical-radiomics. The study analyzed 179 consecutive cases from two hospitals, splitting data into training and validation cohorts. Radiomics features were extracted with 3DSlicer and selected via consistency testing, Student’s t test, and LASSO. Models integrating clinical, radiomics, and combined features were evaluated by AUC, supporting early prediction to guide treatment and prognostic assessment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Methods

Study design and dataset

Radiomics feature extraction and selection

Model construction

Results

Conclusion

TYPE Original Research PUBLISHED 03 October 2024 DOI 10.3389/fnins.2024.1443486 OPEN ACCESS EDITED BY Xiangzhi Bai, Beihang University, China REVIEWED BY Guangyu Dan, University of Illinois Chicago, United States Susan Klapproth, University Medical Center Hamburg-Eppendorf, Germany *CORRESPONDENCE Kang Li [lkrmyydoctor@126.com](lkrmyydoctor@126.com); RECEIVED 04 June 2024 ACCEPTED 20 September 2024 PUBLISHED 03 October 2024 CITATION Zhang L, Xie G, Zhang Y, Li J, Tang W, Yang L and Li K (2024) A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study. Front. Neurosci. 18:1443486 . doi: 10.3389/fnins.2024.1443486 COPYRIGHT © 2024 Zhang, Xie, Zhang, Li, Tang, Yang and Li. 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) and the copyright owner(s) 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. A CT-based machine learning model for using clinical-radiomicsto predict malignant cerebral edema after stroke: a two-center study Lingfeng Zhang 1, Gang Xie 2, Yue Zhang3,4, Junlin Li 1,3, Wuli Tang3,4, Ling Yang3,4 and Kang Li3* 1 North Sichuan Medical College, Nanchong, China, 2 Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, China, 3 Department of Radiology, Chongqing General Hospital, Chongqing, China, 4Chongqing Medical University, Chongqing, China Purpose: This research aimed to create a machine learning model for clinicalradiomics that utilizes unenhanced computed tomography images to assess the likelihood of malignant cerebral edema (MCE) in individuals suffering from acute ischemic stroke (AIS) . Methods: The research included 179 consecutive patients with AIS from two different hospitals. These patients were randomly assigned to training (n =143) and validation (n =36) sets with an 8:2 ratio. Using 3DSlicer software, the radiomics features of regions impacted by infarction were derived from unenhanced CT scans. The radiomics features linked to MCE were pinpointed through a consistency test, Student’s t test and the least absolute shrinkage and selection operator (LASSO) method for selecting features. Clinical parameters associated with MCE were also identified. Subsequently, machine learning models were constructed based on clinical, radiomics, and clinical-radiomics. Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs) . Results: Logistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. In the training and validation cohorts, the AUCs of clinical model were 0.836 and 0.773, respectively, for differentiating MCE patients; the AUCs of radiomics model were 0.849 and 0. 818, respectively; the AUCs of clinical and radiomics model were 0.912 and 0.916, respectively. Conclusion: This model can assist in predicting MCE after acute ischemic stroke and can provide guidance for clinical treatment and prognostic assessment. KEYWORDS ischemic, malignant cerebral edema, radiomics feature, machine learning, prediction 1 Introduction AIS is widely acknowledged as the primary cause of mortality and impairment among adults (Meschia and Brott, 2018). Cerebral edema is a frequent complication after AIS and maybe linked to poor outcomes. According to the guidelines of the “Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) program” (Thorén et al., 2017), Frontiers in Neuroscience 01 [frontiersin.org](frontiersin.org) cerebral edema can be classified into three levels: CED-1 denotes

FAQs

What was the purpose of the proposed model?

To create a machine learning model that uses clinical-radiomics from unenhanced CT to predict the likelihood of malignant cerebral edema after acute ischemic stroke.

How were patients assigned for model development and evaluation?

179 consecutive acute ischemic stroke patients from two hospitals were randomly divided into a training set (n=143) and a validation set (n=36) in an 8:2 ratio.

Which model and feature set performed best?

Logistic regression was identified as the most effective algorithm for forecasting MCE. The combined clinical and radiomics model achieved AUCs of 0.912 (training) and 0.916 (validation).

Why is early prediction of malignant cerebral edema clinically important?

Early identification supports timely intervention by stroke clinicians and helps determine treatment and prognostic assessment to reduce deterioration risk.

Aug 5, 2026

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