ArticleDiagnostics (Basel, Switzerland)2022
Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Prediction models for functional outcomes in prolonged disorders of consciousness: a systematic review and meta-analysis.Journal of neuroengineering and rehabilitation · 2025Pooled 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
- Can machine learning be a reliable tool for predicting hematoma progression following traumatic brain injury? A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Review
- An interpretable multimodal model for early prediction of delayed hematoma progression in frontal lobe contusion: a machine learning approach.Frontiers in neuroscience · 2026Article
- Low serum calcium promotes traumatic intracerebral hematoma expansion by the response of immune cell: A multicenter retrospective cohort study.Scientific reports · 2025Article
- PROMISE: Prognostic Radiomic Outcome Measurement in Acute Subdural Hematoma Evacuation Post-Craniotomy.Brain sciences · 2025Article
- An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.BMC medical imaging · 2024Article
- Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.The neuroradiology journal · 2024Article
- Glibenclamide for Brain Contusions: Contextualizing a Promising Clinical Trial Design that Leverages an Imaging-Based TBI Endotype.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Explainable machine learning reveals multifactorial drivers of early intracranial hematoma progression in traumatic brain injury: development of a SHAP-guided SVM nomogram.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (
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Registered trials
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