ArticleFrontiers in neurology2024
The value of CT-based radiomics in predicting hemorrhagic transformation in acute ischemic stroke patients without recanalization therapy.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- Early Predictive Accuracy of Machine Learning for Hemorrhagic Transformation in Acute Ischemic Stroke: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.Journal of neurology · 2024Pooled it
- Research Advances in Timing of Anticoagulant Therapy Following Cardioembolic Stroke.Clinical Medicine Insights. Cardiology · 2026Review
- A predictive clinical-radiomics nomogram for early diagnosis of mesenteric arterial embolism based on non-contrast CT and biomarkers.Abdominal radiology (New York) · 2025Article
- Optimizing acute ischemic stroke outcome prediction by integrating radiomics features of DSC-PWI and perfusion parameter maps.Frontiers in neurology · 2025Article
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The aim of this study is to investigate the clinical value of radiomics based on non-enhanced head CT in the prediction of hemorrhage transformation in acute ischemic stroke (AIS). Materials and methods: A total of 140 patients diagnosed with AIS from January 2015 to August 2022 were enrolled. Radiomic features from infarcted areas on non-enhanced CT images were extracted using ITK-SNAP. The max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select features. The radiomics signature was then constructed by multiple logistic regressions. The clinicoradiomics nomogram was constructed by combining radiomics signature and clinical characteristics. All predictive models were constructed in the training group, and these were verified in the validation group. All models were evaluated with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: Of the 140 patients, 59 experienced hemorrhagic transformation, while 81 remained stable. The radiomics signature was constructed by 10 radiomics features. The clinicoradiomics nomogram was constructed by combining radiomics signature and atrial fibrillation. The area under the ROC curve (AUCs) of the clinical model, radiomics signature, and clinicoradiomics nomogram for predicting hemorrhagic transformation in the training group were 0.64, 0.86, and 0.86, respectively. The AUCs of the clinical model, radiomics signature, and clinicoradiomics nomogram for predicting hemorrhagic transformation in the validation group were 0.63, 0.90, and 0.90, respectively. The DCA curves showed that the radiomics signature performed well as well as the clinicoradiomics nomogram. The DCA curve showed that the clinical application value of the radiomics signature is similar to that of the clinicoradiomics nomogram. Conclusion: The radiomics signature, constructed without incorporating clinical characteristics, can independently and effectively predict hemorrhagic transformation in AIS patients.
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