文献类型：期刊文献

英文题名：Prediction of the trans-stenotic pressure gradient with arteriography-derived hemodynamic features in patients with idiopathic intracranial hypertension

作者：Zhang, Yupeng[1,2];Ma, Chao[2,3];Li, Changxuan[4];Li, Xiaoqing[1];Liu, Raynald[1];Liu, Minke[5];Zhu, Haoyu[2];Liang, Fei[2];Wang, Yilong[6];Dong, Kehui[6];Jiang, Chuhan[1,2];Miao, Zhongrong[1];Mo, Dapeng[1]

第一作者：Zhang, Yupeng

通信作者：Mo, DP[1]

机构：[1]Capital Med Univ, Beijing Tiantan Hosp, Intervent Neuroradiol Ctr, Beijing 100070, Peoples R China;[2]Capital Med Univ, Beijing Neurosurg Inst, Beijing, Peoples R China;[3]Tsinghua Univ, Beijing Tsinghua Changgung Hosp, Sch Clin Med, Dept Neurosurg, Beijing, Peoples R China;[4]Hainan Med Univ, Dept Neurol, Affiliated Hosp 1, Sanya, Hainan, Peoples R China;[5]Gansu Univ Tradit Chinese Med, Dept Neurointervent Surg, Affiliated Hosp, Lanzhou, Gansu, Peoples R China;[6]Capital Med Univ, Beijing Tiantan Hosp, Dept Neurol, Beijing, Peoples R China

第一机构：Capital Med Univ, Beijing Tiantan Hosp, Intervent Neuroradiol Ctr, Beijing 100070, Peoples R China

通信机构：[1]corresponding author), Capital Med Univ, Beijing Tiantan Hosp, Intervent Neuroradiol Ctr, Beijing 100070, Peoples R China.

年份：2022

卷号：42

期号：8

起止页码：1524

外文期刊名：JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM

收录：;Scopus(收录号：2-s2.0-85126060267);WOS:【SCI-EXPANDED(收录号:WOS:000769489200001)】；

基金：The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Beijing Municipal Administration of Hospitals Incubating Program (No. PX2017009) and Beijing Natural Science Foundation project (Grant No. 7212007), and Project Supported by Hainan Province Clinical Medical Center.

语种：英文

外文关键词：Idiopathic intracranial hypertension; manometry; radiomics; support vector machine; time-density curve

摘要：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.