ArticleTranslational stroke research2020
Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment.
Article in Translational stroke research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 40 citations in OpenAlex.
- Radiomics analysis for predicting malignant cerebral edema in patients undergoing endovascular treatment for acute ischemic strokeDiagnostic and interventional radiology (Ankara, Turkey) · 2023Trial
- Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke.Korean journal of radiology · 2022Trial
- Mechanisms and biomarkers of poststroke cognitive impairment.Neural regeneration research · 2026Article
- Development and validation of interpretable multimodal clinical-radiomics models for predicting epileptogenic foci and surgical outcomes in tuberous sclerosis complex: A multicenter study.PLOS digital health · 2026Article
- Radiomics models identifying dementia in patients with normal age-related hippocampal atrophy: a study based on machine learning.BMC geriatrics · 2025Article
- Development and validation of a machine learning-based risk prediction model for post-stroke cognitive impairment.Scientific reports · 2025Article
- Cerebellar MRI-based radiomics models for identifying mild cognitive impairment: a retrospective multicenter study in Southeast China.Frontiers in aging neuroscience · 2025Article
- An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy.Quantitative imaging in medicine and surgery · 2024Article
- Radiomic deformation features to predict associations between affected brain regions and verbal fluency scores in acute stroke patients: preliminary finding.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.BMC neurology · 2024Article
- Article
- Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage.European radiology · 2023Article
- Prediction of hemorrhagic transformation via pre-treatment CT radiomics in acute ischemic stroke patients receiving endovascular therapy.The British journal of radiology · 2023Article
- MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke.Frontiers in neurology · 2023Article
- Diagnosis of Subcortical Ischemic Vascular Cognitive Impairment With No Dementia Using Radiomics of Cerebral Cortex and Subcortical Nuclei in High-Resolution T1-Weighted MR Imaging.Frontiers in oncology · 2022Article
- Three-dimensional printing and 3D slicer powerful tools in understanding and treating neurosurgical diseases.Frontiers in surgery · 2022Review
- Effect of Early Cognitive Training Combined with Aerobic Exercise on Quality of Life and Cognitive Function Recovery of Patients with Poststroke Cognitive Impairment.Journal of healthcare engineering · 2022Article
- Detection of Microstructural Medial Prefrontal Cortex Changes Using Magnetic Resonance Imaging Texture Analysis in a Post-Traumatic Stress Disorder Rat Model.Frontiers in psychiatry · 2022Article
- Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion.BMC medical imaging · 2021Article
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Authors and funding
10 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.
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