ArticleFrontiers in neuroscience2022
A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 10 citations in OpenAlex.
- Radiomics-Based AI Model to Assist Clinicians in Intracranial Hemorrhage Diagnosis: External Validation Study.JMIR formative research · 2025Article
- Machine learning based classification of spontaneous intracranial hemorrhages using radiomics features.Neuroradiology · 2025Article
- Using tumor habitat-derived radiomic analysis during pretreatmentCancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors.Frontiers in oncology · 2023Article
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7 authors at 2 institutions in 1 country.
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Abstract
Aim: To develop and validate a radiomics nomogram on non-contrast-enhanced computed tomography (NECT) for classifying hematoma entities in patients with acute spontaneous intracerebral hemorrhage (ICH). Materials and Methods: One hundred and thirty-five patients with acute intraparenchymal hematomas and baseline NECT scans were retrospectively analyzed, i.e., 52 patients with vascular malformation-related hemorrhage (VMH) and 83 patients with primary intracerebral hemorrhage (PICH). The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed. After extracting the radiomics features of hematomas from baseline NECT, the least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct the radiomics signature. Multivariate logistic regression analysis was used to determine the independent clinical-radiological risk factors, and a clinical model was constructed. A predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors. Nomogram performance was assessed in the training cohort and tested in the validation cohort. The capability of models was compared by calibration, discrimination, and clinical benefit. Results: Six features were selected to establish radiomics signature Conclusion: Non-contrast-enhanced computed tomography-based radiomics nomogram can predict the individualized risk of VMH in patients with acute ICH.
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