Trial reportBrain and behavior2021
Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning.
Trial report in Brain and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 5 of them syntheses that pooled it.
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The trial behind it
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Who cites it
46 citing papers in PubMed, 5 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- Artificial intelligence applications in intracerebral hemorrhage care: implications for clinical and nursing practice - a narrative literature review.Frontiers in rehabilitation sciences · 2025Pooled it
- Guidelines for Neuroprognostication in Critically Ill Adults with Intracerebral Hemorrhage.Neurocritical care · 2024Guideline
- Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis.International journal of clinical practice · 2022Pooled it
- Prognostic prediction of hypertensive intracerebral hemorrhage using CT radiomics and machine learning.Brain and behavior · 2021Trial
- Development and validation of a machine learning-based predictive model for prognosis in cerebral hemorrhage patients after hyperbaric oxygen therapy.Medical gas research · 2027Article
- Review
- Artificial Intelligence in Intracerebral Hemorrhage: Current Applications and Future Perspectives.Journal of clinical medicine · 2026Review
- Article
- Article
- Predicting long-term outcomes in intracerebral hemorrhage: a comparative study of machine learning models highlights the prognostic value of hematoma clearance.Frontiers in neurology · 2026Article
- A Machine Learning Method for Predicting Acute Kidney Injury in Patients with Intracranial Hemorrhage.Cell biochemistry and biophysics · 2025Article
- CT-Based deep learning platform combined with clinical parameters for predicting different discharge outcome in spontaneous intracerebral hemorrhage.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Efficacy of Concurrent Utilization of Mannitol and Nimodipine in Treating Hypertensive Intracerebral Hemorrhage and Its Effects on Neurological Function.Psychiatry and clinical psychopharmacology · 2025Article
- Radiomics based on multiple machine learning methods for diagnosing early bone metastases not visible on CT images.Skeletal radiology · 2025Article
- Combinations of Clinical Factors, CT Signs, and Radiomics for Differentiating High-Density Areas after Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.AJNR. American journal of neuroradiology · 2025Article
- Analysis of Prognostic Factors for Drilling Drainage Surgery in Patients with Hypertensive Intracerebral Hemorrhage and Development of a Predictive Nomogram.Risk management and healthcare policy · 2025Article
- Article
- Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.Journal of the American Heart Association · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesSpontaneous intracerebral hemorrhage remains a major cause of death and disability throughout the world. We tried to establish accurate long-term outcome prediction models for hypertensive intracerebral hemorrhage (HICH) using CT radiomics and machine learning.
methodsIn a retrospective study of 270 patients with HICH between June 2013 and June 2018, CT images and patients' 6-month outcome based on the modified Rankin Scale were collected. Hematomas on CT images were selected as volumes of interests (VOIs), and 1,029 radiomics features of the VOIs were extracted. Based on correlations with patients' outcome, radiomics features underwent dimensionality reduction analyses. Then, the support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and XGBoost algorithms were applied with the screened features to establish prognostic prediction models of HICH. Accuracies of all models were compared.
resultsEighteen radiomics features were screened as prognosis-associated radiomics signature of HICH based on the variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression models. Patients were randomly allocated into training (n = 215) and validation (n = 55) sets. Accuracies of all 6 machine learning algorithms in the validation set exceeded 80%. The sensitivity, specificity, and accuracy in the validation set were 93.3%, 92.5%, and 92.7% for the RF model and 92.3%, 88.1%, and 89.1% for the XGBoost model, respectively, which were the best two among all models.
conclusionsTaking advantage of radiomics and machine learning, we established accurate prognostic prediction models of HICH. The RF model and XGBoost model returned the best accuracies.
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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.