Literature DB >> 31493994

Early prediction of clinical outcomes in patients with aneurysmal subarachnoid hemorrhage using computed tomography texture analysis.

Tokunori Kanazawa1, Satoshi Takahashi2, Yasuhiro Minami3, Masahiro Jinzaki3, Masahiro Toda2, Kazunari Yoshida2.   

Abstract

Radiological evaluation of subarachnoid hemorrhage (SAH) is often subject to interobserver variability. The aim of this study was to retrospectively detect computed tomography (CT) texture parameters in the early postictal state to predict cerebral vasospasm, delayed cerebral ischemia (DCI), and functional outcome in aneurysmal SAH using quantitative CT texture analysis (CTTA) via a commercially available software program and routine CT images. 40 patients with aneurysmal SAH surgically treated at the Keio University Hospital during a four-year period were analyzed. CT texture analyses were performed using a commercially available software program (Synapse Vincent). The following texture parameters of blood clots in the subarachnoid space and cerebral edema were assessed: mean CT value, entropy, skewness, and kurtosis. The mean CT value of blood clots in the subarachnoid space was significantly associated with cerebral vasospasm, DCI, and functional outcome. The mean CT value ≥ 49.64 Hounsfield units (HU) predicted cerebral vasospasm with a sensitivity and specificity of 85.7% and 61.5%, respectively (area under the curve [AUC] = 0.758). The mean CT value ≥ 49.95 HU predicted DCI with a sensitivity and specificity of 100% and 60.6%, respectively (AUC = 0.810). The mean CT value ≥ 53.00 HU predicted poor functional outcome with a sensitivity and specificity of 56.3% and 91.7%, respectively (AUC = 0.747). CTTA using a commercially available software program demonstrated that the mean CT value of clots in the subarachnoid space in the early postictal state could predict vasospasm, DCI, and clinical outcome with a high sensitivity and specificity.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Cerebral edema; Cerebral vasospasm; Delayed cerebral ischemia; Mean CT value; Prognosis; Subarachnoid hemorrhage; Texture analysis

Mesh:

Year:  2019        PMID: 31493994     DOI: 10.1016/j.jocn.2019.08.098

Source DB:  PubMed          Journal:  J Clin Neurosci        ISSN: 0967-5868            Impact factor:   1.961


  4 in total

1.  Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study.

Authors:  Ping Hu; Yuntao Li; Yangfan Liu; Geng Guo; Xu Gao; Zhongzhou Su; Long Wang; Gang Deng; Shuang Yang; Yangzhi Qi; Yang Xu; Liguo Ye; Qian Sun; Xiaohu Nie; Yanqi Sun; Mingchang Li; Hongbo Zhang; Qianxue Chen
Journal:  Front Aging Neurosci       Date:  2022-06-17       Impact factor: 5.702

2.  A Comparison of LASSO Regression and Tree-Based Models for Delayed Cerebral Ischemia in Elderly Patients With Subarachnoid Hemorrhage.

Authors:  Ping Hu; Yangfan Liu; Yuntao Li; Geng Guo; Zhongzhou Su; Xu Gao; Junhui Chen; Yangzhi Qi; Yang Xu; Tengfeng Yan; Liguo Ye; Qian Sun; Gang Deng; Hongbo Zhang; Qianxue Chen
Journal:  Front Neurol       Date:  2022-03-10       Impact factor: 4.003

3.  Development and external validation of a dynamic nomogram for delayed cerebral ischaemia after aneurysmal subarachnoid hemorrhage: a study protocol for a multicentre retrospective cohort study.

Authors:  Ping Hu; Yuntao Li; Hongbo Zhang; Zhongzhou Su; Shancai Xu; Xuesong Li; Xu Gao; Yangfan Liu; Gang Deng; Yang Xu; Liguo Ye; Qianxue Chen
Journal:  BMJ Open       Date:  2021-12-23       Impact factor: 2.692

4.  Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies.

Authors:  Jewel Sengupta; Robertas Alzbutas
Journal:  Biomed Res Int       Date:  2022-01-27       Impact factor: 3.411

  4 in total

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