Korean Journal of Radiology

  • 제25권1호
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  • Pages.74-85
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  • 2024
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Prediction of Venous Trans-Stenotic Pressure Gradient Using Shape Features Derived From Magnetic Resonance Venography in Idiopathic Intracranial Hypertension Patients

  • Chao Ma (School of Clinical Medicine, Tsinghua University) ;
  • Haoyu Zhu (Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University) ;
  • Shikai Liang (School of Clinical Medicine, Tsinghua University) ;
  • Yuzhou Chang (Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University) ;
  • Dapeng Mo (Department of Neurology, Beijing Tiantan Hospital, Capital Medical University) ;
  • Chuhan Jiang (Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University) ;
  • Yupeng Zhang (Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University)
  • 투고 : 2023.09.26
  • 심사 : 2023.11.03
  • 발행 : 2024.01.01
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초록

Objective: Idiopathic intracranial hypertension (IIH) is a condition of unknown etiology associated with venous sinus stenosis. This study aimed to develop a magnetic resonance venography (MRV)-based radiomics model for predicting a high trans-stenotic pressure gradient (TPG) in IIH patients diagnosed with venous sinus stenosis. Materials and Methods: This retrospective study included 105 IIH patients (median age [interquartile range], 35 years [27-42 years]; female:male, 82:23) who underwent MRV and catheter venography complemented by venous manometry. Contrast enhanced-MRV was conducted under 1.5 Tesla system, and the images were reconstructed using a standard algorithm. Shape features were derived from MRV images via the PyRadiomics package and selected by utilizing the least absolute shrinkage and selection operator (LASSO) method. A radiomics score for predicting high TPG (≥ 8 mmHg) in IIH patients was formulated using multivariable logistic regression; its discrimination performance was assessed using the area under the receiver operating characteristic curve (AUROC). A nomogram was constructed by incorporating the radiomics scores and clinical features. Results: Data from 105 patients were randomly divided into two distinct datasets for model training (n = 73; 50 and 23 with and without high TPG, respectively) and testing (n = 32; 22 and 10 with and without high TPG, respectively). Three informative shape features were identified in the training datasets: least axis length, sphericity, and maximum three-dimensional diameter. The radiomics score for predicting high TPG in IIH patients demonstrated an AUROC of 0.906 (95% confidence interval, 0.836-0.976) in the training dataset and 0.877 (95% confidence interval, 0.755-0.999) in the test dataset. The nomogram showed good calibration. Conclusion: Our study presents the feasibility of a novel model for predicting high TPG in IIH patients using radiomics analysis of noninvasive MRV-based shape features. This information may aid clinicians in identifying patients who may benefit from stenting.

키워드

과제정보

This work was supported by the National Natural Science Foundation of China (NSFC) Youth Project (grant number 82301457).

참고문헌

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