ArticleEuropean radiology2020
Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05424614 (Capital's Funds for Health Improvement and Research), which is not on this map. Cited by 57 papers, 5 of them syntheses that pooled it.
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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.
Capital's Funds for Health Improvement and Research
Who cites it
57 citing papers in PubMed, 5 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Machine Learning-Based Prediction of Poor Outcomes in Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Brain and behavior · 2026Pooled it
- The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2024Pooled it
- Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis.Frontiers in oncology · 2022Pooled it
- Island Sign Predicts Hematoma Expansion and Poor Outcome After Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis.Frontiers in neurology · 2020Pooled it
- Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging.Journal of orthopaedic surgery and research · 2024Trial
- Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema.Clinical neuroradiology · 2023Trial
- [Automatic identification algorithm of meniscus tear based on radiomics of knee MRI].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2022Trial
- Identification of high-risk carotid plaque with MRI-based radiomics and machine learning.European radiology · 2021Trial
- Fusion attention-based nasopharyngeal carcinoma segmentation model in predicting the clinical outcome of cervical lymph node residue after IMRT.BMC medical imaging · 2026Article
- Comparing manual and automated methods for extracting perihematomal features in predicting hemorrhage expansion in deep intracerebral hemorrhage.Scientific reports · 2026Article
- Deep learning-based imaging model to predict early hematoma enlargement and hospital mortality in spontaneous intracerebral hemorrhage.World journal of radiology · 2026Article
- Artificial Intelligence in Hematologic Malignancies: Opportunities, Challenges, and Clinical Integration.Cureus · 2026Review
- Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation.Frontiers in human neuroscience · 2026Review
- Comparing Handcrafted Radiomics Versus Latent Deep Learning Features of Admission Head CT for Hemorrhagic Stroke Outcome Prediction.Biotech (Basel (Switzerland)) · 2025Article
- Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.International journal of legal medicine · 2025Article
- Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrounding oedema.Frontiers in neurology · 2025Article
- Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT).Diagnostics (Basel, Switzerland) · 2024Article
- Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approachDiagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- Feasibility of a clinical-radiomics combined model to predict the occurrence of stroke-associated pneumonia.BMC neurology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
objectivesTo develop a radiomics model for predicting hematoma expansion in patients with intracerebral hemorrhage (ICH) and to compare its predictive performance with a conventional radiological feature-based model.
methodsWe retrospectively analyzed 251 consecutive patients with acute ICH. Two radiologists independently assessed baseline noncontrast computed tomography (NCCT) images. For each radiologist, a radiological model was constructed from radiological variables; a radiomics score model was constructed from high-dimensional quantitative features extracted from NCCT images; and a combined model was constructed using both radiological variables and radiomics score. Development of models was constructed in a primary cohort (n = 177). We then validated the results in an independent validation cohort (n = 74). The primary outcome was hematoma expansion. We compared the three models for predicting hematoma expansion. Predictive performance was assessed with the receiver operating characteristic (ROC) curve analysis.
resultsIn the primary cohort, combined model and radiomics model showed greater AUCs than radiological model for both readers (all p < .05). In the validation cohort, combined model and radiomics model showed greater AUCs, sensitivities, and accuracies than radiological model for reader 2 (all p < .05). Combined model showed greater AUC than radiomics model for reader 1 only in the primary cohort (p = .03). Performance of three models was comparable between reader 1 and reader 2 in both cohorts (all p > .05).
conclusionsNCCT-based radiomics model showed high predictive performance and outperformed radiological model in the prediction of early hematoma expansion in ICH patients. KEY POINTS: • Radiomics model showed better performance for prediction of hematoma expansion in patients with intracerebral hemorrhage than radiological feature-based model. • Hematomas which expanded in follow-up NCCT tended to be larger in baseline volume, more irregular in shape, more heterogeneous in composition, and coarser in texture. • A radiomics model provides a convenient and objective tool for prediction of hematoma expansion that helps to define subsets of patients who would benefit from anti-expansion therapy.
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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.