ArticleBMC medical imaging2026
Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundThe early prediction of malignant cerebral edema (MCE) following endovascular therapy for acute ischemic stroke is of paramount importance for facilitating timely interventions. The present study aimed to create a comprehensive map of lesion topography associated with MCE risk and to build a machine learning model based on these topography-informed radiomics to predict the MCE in stroke patients after endovascular therapy.
methodsUsing voxel-based lesion analyses, we comprehensively quantified the spatial features of infarct location lesions. These topological features were integrated with radiomics to create a hybrid spatial radiomics model. Four machine learning algorithms bases on topography features, radiomics, and Topo-Rad features were developed to predict MCE in acute stroke patients, respectively. The performance of models was evaluated using the receiver operating characteristic curves, decision curve analysis and Net Reclassification Improvement. The SHapley Additive exPlanations (SHAP) method was employed to interpret and visualize the output of the optimal model.
resultsThe topography maps for acute stroke patients showed the right temporal lobe and right caudate nucleus were significantly associated with MCE (P < 0.05). For four ML algorithms, the SVM model based on topo-Rad achieved the highest predictive performance (AUC in training/validation set: 0.872/0.842), while no statistically significant difference was observed compared to the model based on topography (0.857/0.812). The SHAP plots demonstrated that the most significant contributors to model performance were related to temporal_pars_of_MCA_R, occipital_pars_of_PCA_R, parietal_pars_of_MCA_R, temporal_pars_of_MCA_L, and parietal_pars_of_MCA_L.
conclusionsThe infarct topography plays a dominant role in predicting MCE following endovascular therapy, with radiomic features providing limited additional predictive value.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.