ArticleJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism2022
Prediction of the trans-stenotic pressure gradient with arteriography-derived hemodynamic features in patients with idiopathic intracranial hypertension.
Article in Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 14 citations in OpenAlex.
- Predictive models for trans-stenotic pressure gradient in the transverse sinus based on multimodal correlates.Quantitative imaging in medicine and surgery · 2026Article
- Idiopathic intracranial hypertension presenting with obsessive thoughts: a rare neuropsychiatric manifestation "case report".BMC neurology · 2026Article
- Post-procedural hemodynamics by quantitative DSA predict restenosis in chronic internal carotid artery occlusion.Neuroradiology · 2026Article
- Trans-stenotic pressure gradient in symptomatic transverse sinus stenosis: clinical, laboratory, and imaging correlates.Quantitative imaging in medicine and surgery · 2026Article
- Multimodal evaluation and etiology-oriented management of chronic intracranial venous hypertension.Frontiers in neurology · 2026Article
- Higher Time to Peak after Stent Implantation in Symptomatic High-Grade Intracranial Atherosclerotic Stenosis is Related to In-Stent Restenosis.Translational stroke research · 2025Article
- Machine learning models for predicting transstenotic pressure gradient based on computed tomography angiography features.Quantitative imaging in medicine and surgery · 2025Article
- Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization.Frontiers in neurology · 2024Article
- Enhanced T-cell activation and chemokine-associated function in CD14-positive cells from venous sinus blood in sub-acute cerebral venous sinus thrombosis.Frontiers in cell and developmental biology · 2024Article
- Prediction of Venous Trans-Stenotic Pressure Gradient Using Shape Features Derived From Magnetic Resonance Venography in Idiopathic Intracranial Hypertension Patients.Korean journal of radiology · 2024Article
- High-resolution magnetic resonance imaging-based radiomic features aid in selecting endovascular candidates among patients with cerebral venous sinus thrombosis.Thrombosis journal · 2023Article
- Group-based trajectory modeling of intracranial pressure in patients with acute brain injury: Results from multi-center ICUs, 2008-2019.CNS neuroscience & therapeutics · 2022Article
- Cerebral venous disorders: clinical presentation, diagnostic strategy, and contemporary management.Frontiers in neurologyReview
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Authors and funding
13 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The pathogenesis of idiopathic intracranial hypertension (IIH) is attributed to segmental stenosis of the venous sinus. The current treatment paradigm requires a trans-stenotic pressure gradient of ≥8 mmHg or ≥6 mmHg threshold. This study aimed to develop a machine learning screening method to identify patients with IIH using hemodynamic features. A total of 204 venous manometry instances (n = 142, training and validation; n = 62, test) from 135 patients were included. Radiomic features extracted from five arteriography perfusion parameter maps were selected using least absolute shrinkage and selection operator and then entered into support vector machine (SVM) classifiers. The Thr8-23-SVM classifier was created with 23 radiomic features to predict if the pressure gradient was ≥8 mmHg. On an independent test dataset, prediction sensitivity, specificity, accuracy, and AUC were 0.972, 0.846, 0.919, and 0.980, respectively (95% confidence interval: 0.980-1.000). For the 6 mmHg threshold, thr6-28-SVM incorporated 28 features, and its sensitivity, specificity, accuracy, and AUC were 0.923, 0.956, 0.935, and 0.969, respectively (95% confidence interval: 0.927-1.000). The trans-stenotic pressure gradient result was associated with perfusion pattern changes, and SVM classifiers trained with arteriography perfusion map-derived radiomic features could predict the 8 mmHg and 6 mmHg dichotomized trans-stenotic pressure gradients with favorable accuracy.
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