ArticleFrontiers in neuroscience2020
A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can artificial intelligence accurately predict the risk of hematoma expansion in intracerebral hemorrhage? A systematic review and Meta-analysis of 7,665 patients.Neurosurgical review · 2025Pooled it
- Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2024Pooled it
- Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema.Clinical neuroradiology · 2023Trial
- Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.European journal of neurology · 2021Trial
- Clinically Applicable Machine Learning Approach to Predict Intracerebral Hematoma Expansion.Journal of the American Heart Association · 2026Article
- Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.International journal of legal medicine · 2025Article
- Review
- Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.Scientific reports · 2024Article
- Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.European radiology · 2024Article
- Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival.Diagnostics (Basel, Switzerland) · 2024Article
- A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study.Frontiers in neuroscience · 2024Article
- Review
- Article
- A clinical-radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study.Insights into imaging · 2023Article
- Building nonenhanced CT based radiomics model in discriminating arteriovenous malformation related hematomas from hypertensive intracerebral hematomas.Frontiers in neuroscience · 2023Article
- Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers.Frontiers in neuroscience · 2023Article
- The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters.Diagnostics (Basel, Switzerland) · 2022Article
- Non-Contrast CT-Based Radiomics Score for Predicting Hematoma Enlargement in Spontaneous Intracerebral Hemorrhage.Clinical neuroradiology · 2022Article
- An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.Medical physics · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe aimed to construct and validate a nomogram model based on the combination of radiomic features and satellite sign number for predicting intracerebral hematoma expansion.
methodsA total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-Kruskal-Wallis test and least absolute shrinkage and selection operator regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating the Radscore with a satellite sign number. The discrimination performance of the proposed model was evaluated by receiver operating characteristic (ROC) analysis, and the predictive accuracy was assessed
resultsFour optimal features were ultimately selected and contributed to the Radscore construction. A positive correlation was observed between the satellite sign number and Radscore (Pearson's
conclusionA nomogram model of integrated radiomic signature and satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.
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