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Zhang SQ, Zhang YL, Yuan L, Ma YB, Huang JM, Wen YQ, Zhu MH, Yang WS. A comprehensive prediction model predicts perihematomal edema growth in the acute stage after intracerebral hemorrhage. Clin Neurol Neurosurg 2024; 245:108495. [PMID: 39126898 DOI: 10.1016/j.clineuro.2024.108495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/14/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Perihematomal edema (PHE) is regarded as a potential intervention indicator of secondary injury following intracerebral hemorrhage (ICH). But it still lacks a comprehensive prediction model for early PHE formation. METHODS The included ICH patients have received an initial Computed Tomography scan within 6 hours of symptom onset. Hematoma volume and PHE volume were computed using semiautomated computer-assisted software. The volume of the hematoma, edema around the hematoma, and surface area of the hematoma were calculated. The platelet-to-lymphocyte ratio (PLR) was calculated by dividing the platelet count by the lymphocyte cell count. All analyses were 2-tailed, and the significance level was determined by P <0.05. RESULTS A total of 226 patients were included in the final analysis. The optimal cut-off values for PHE volume increase to predict poor outcomes were determined as 5.5 mL. For clinical applicability, we identified a value of 5.5 mL as the optimal threshold for early PHE growth. In the multivariate logistic regression analyses, we finally found that baseline hematoma surface area (p < 0.001), expansion-prone hematoma (p < 0.001), and PLR (p = 0.033) could independently predict PHE growth. The comprehensive prediction model demonstrated good performance in predicting PHE growth, with an area under the curve of 0.841, sensitivity of 0.807, and specificity of 0.732. CONCLUSION In this study, we found that baseline hematoma surface area, expansion-prone hematoma, and PLR were independently associated with PHE growth. Additionally, a risk nomogram model was established to predict the PHE growth in patients with ICH.
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Affiliation(s)
- Shu-Qiang Zhang
- Department of Radiology, Chongqing University FuLing Hospital, Chongqing 408000, China; Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yan-Ling Zhang
- Department of Radiology, Chongqing University FuLing Hospital, Chongqing 408000, China
| | - Liang Yuan
- Department of Radiology, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
| | - Yong-Bo Ma
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jun-Meng Huang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Yi-Qian Wen
- Department of Radiology, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
| | - Ming-Hong Zhu
- Department of Radiology, Chongqing University FuLing Hospital, Chongqing 408000, China.
| | - Wen-Song Yang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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Yalcin C, Abramova V, Terceño M, Oliver A, Silva Y, Lladó X. Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework. Comput Med Imaging Graph 2024; 117:102430. [PMID: 39260113 DOI: 10.1016/j.compmedimag.2024.102430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 08/03/2024] [Accepted: 08/30/2024] [Indexed: 09/13/2024]
Abstract
Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. It is observed within 24 h of onset and associated with patient worsening. Clinically it is relevant to detect the patients that will develop HE from their initial computed tomography (CT) scans which could improve patient management and treatment decisions. However, this is a significant challenge due to the predictive nature of the task and its low prevalence, which hinders the availability of large datasets with the required longitudinal information. In this work, we present an end-to-end deep learning framework capable of predicting which cases will exhibit HE using only the initial basal image. We introduce a deep learning framework based on the 2D EfficientNet B0 model to predict the occurrence of HE using initial non-contrasted CT scans and their corresponding lesion annotation as priors. We used an in-house acquired dataset of 122 ICH patients, including 35 HE cases, containing longitudinal CT scans with manual lesion annotations in both basal and follow-up (obtained within 24 h after the basal scan). Experiments were conducted using a 5-fold cross-validation strategy. We addressed the limited data problem by incorporating synthetic images into the training process. To the best of our knowledge, our approach is novel in the field of HE prediction, being the first to use image synthesis to enhance results. We studied different scenarios such as training only with the original scans, using standard image augmentation techniques, and using synthetic image generation. The best performance was achieved by adding five generated versions of each image, along with standard data augmentation, during the training process. This significantly improved (p=0.0003) the performance obtained with our baseline model using directly the original CT scans from an Accuracy of 0.56 to 0.84, F1-Score of 0.53 to 0.82, Sensitivity of 0.51 to 0.77, and Specificity of 0.60 to 0.91, respectively. The proposed approach shows promising results in predicting HE, especially with the inclusion of synthetically generated images. The obtained results highlight the significance of this research direction, which has the potential to improve the clinical management of patients with hemorrhagic stroke. The code is available at: https://github.com/NIC-VICOROB/HE-prediction-SynthCT.
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Affiliation(s)
- Cansu Yalcin
- Computer Vision and Robotics Group, University of Girona, Girona, Spain.
| | - Valeriia Abramova
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
| | - Mikel Terceño
- Department of Neurology, Hospital Universitari Dr Josep Trueta - Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Arnau Oliver
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
| | - Yolanda Silva
- Department of Neurology, Hospital Universitari Dr Josep Trueta - Institut d'Investigació Biomèdica de Girona, Girona, Spain
| | - Xavier Lladó
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
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Wang P, Zhang J, Liu Y, Wu J, Yu H, Yu C, Jiang R. Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion. Sci Rep 2024; 14:22467. [PMID: 39341957 PMCID: PMC11439036 DOI: 10.1038/s41598-024-73415-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 09/17/2024] [Indexed: 10/01/2024] Open
Abstract
The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.
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Affiliation(s)
- Peng Wang
- Department of Radiology, Chinese People's Liberation the General Hospital of Western Theater Command, Chengdu, 610083, China.
| | - Junfeng Zhang
- Department of Radiology, Chinese People's Liberation the General Hospital of Western Theater Command, Chengdu, 610083, China
| | - Yi Liu
- Department of Radiology, Chinese People's Liberation the General Hospital of Western Theater Command, Chengdu, 610083, China
| | - Jialing Wu
- Department of Radiology, Chinese People's Liberation the General Hospital of Western Theater Command, Chengdu, 610083, China
| | - Hongmei Yu
- Department of Radiology, Chinese People's Liberation the General Hospital of Western Theater Command, Chengdu, 610083, China
| | - Chengzhou Yu
- Department of Radiology, Chinese People's Liberation Army Marine Corps Hospital, Chaozhou, 521000, China
| | - Rui Jiang
- Department of Radiology, Chinese People's Liberation the General Hospital of Western Theater Command, Chengdu, 610083, China.
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4
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Wu F, Wang P, Yang H, Wu J, Liu Y, Yang Y, Zuo Z, Wu T, Li J. Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network. Postgrad Med J 2024; 100:592-602. [PMID: 38507237 DOI: 10.1093/postmj/qgae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 12/28/2024] [Indexed: 03/22/2024]
Abstract
PURPOSE To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance. METHODS All 254 patients with primary cerebral hemorrhage from January 2017 to December 2022 in the General Hospital of the Western Theater Command were included. According to the criteria of hematoma enlargement exceeding 33% or the volume exceeding 6 ml, the patients were divided into the HE group and the hematoma non-enlargement (NHE) group. Multiple models and the 10-fold cross-validation method were used to screen the most valuable features and model the probability of predicting HE. The area under the curve (AUC) was used to analyze the prediction efficiency of each model for HE. RESULTS They were randomly divided into a training set of 204 cases in an 8:2 ratio and 50 cases of the test set. The clinical imaging deep feature joint model (22 features) predicted the area under the curve of HE as follows: clinical Navie Bayes model AUC 0.779, traditional radiology logistic regression (LR) model AUC 0.818, deep learning LR model AUC 0.873, and clinical NCCT deep learning multilayer perceptron model AUC 0.921. CONCLUSION The combined clinical imaging deep learning model has a high predictive effect for early HE in sICH patients, which is helpful for clinical individualized assessment of the risk of early HE in sICH patients.
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Affiliation(s)
- Fa Wu
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Peng Wang
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Huimin Yang
- Department of Ultrasound, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Jie Wu
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Yi Liu
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Yulin Yang
- Department of Ultrasound, Chengdu 5th People's Hospital, No. 33, Mashi Street, Liucheng Town, Wenjiang District, Chengdu, Sichuan 611100, PR China
| | - Zhiwei Zuo
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Tingting Wu
- Neurosurgery Department, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
| | - Jianghao Li
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province 610083, PR China
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Yang WS, Liu JY, Shen YQ, Xie XF, Zhang SQ, Liu FY, Yu JL, Ma YB, Xiao ZS, Duan HW, Li Q, Chen SX, Xie P. Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison. J Stroke Cerebrovasc Dis 2024; 33:107731. [PMID: 38657831 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.
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Affiliation(s)
- Wen-Song Yang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Jia-Yang Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Yi-Qing Shen
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Xiong-Fei Xie
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Shu-Qiang Zhang
- Department of Radiology, Chongqing University Fuling Hospital, Chongqing 408000, China.
| | - Fang-Yu Liu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Jia-Lun Yu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Yong-Bo Ma
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Zhong-Song Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Hao-Wei Duan
- College of computer and information science, Southwest University, Chongqing 400715, China.
| | - Qi Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Shan-Xiong Chen
- College of computer and information science, Southwest University, Chongqing 400715, China.
| | - Peng Xie
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
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Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A, Petersen NH, de Havenon A, Falcone GJ, Sheth KN, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digit Med 2024; 7:26. [PMID: 38321131 PMCID: PMC10847454 DOI: 10.1038/s41746-024-01007-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Abstract
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.
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Grants
- K23 NS110980 NINDS NIH HHS
- U24 NS107136 NINDS NIH HHS
- UL1 TR001863 NCATS NIH HHS
- K76 AG059992 NIA NIH HHS
- P30 AG021342 NIA NIH HHS
- R03 NS112859 NINDS NIH HHS
- U24 NS107215 NINDS NIH HHS
- U01 NS106513 NINDS NIH HHS
- 2020097 Doris Duke Charitable Foundation
- K23 NS118056 NINDS NIH HHS
- R01 NR018335 NINR NIH HHS
- Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- Doris Duke Charitable Foundation (DDCF)
- Doris Duke Charitable Foundation (2020097), American Society of Neuroradiology, and National Institutes of Health (K23NS118056).
- National Institutes of Health (K76AG059992, R03NS112859, and P30AG021342), the American Heart Association (18IDDG34280056), the Yale Pepper Scholar Award, and the Neurocritical Care Society Research Fellowship
- National Institutes of Health (U24NS107136, U24NS107215, R01NR018335, and U01NS106513) and the American Heart Association (18TPA34170180 and 17CSA33550004) and a Hyperfine Research Inc research grant.
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Affiliation(s)
- Anh T Tran
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Gaby Abou Karam
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elisa R Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Adnan I Qureshi
- Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Pina C Sanelli
- Department of Radiology, Northwell Health, Manhasset, NY, USA
| | - David J Werring
- Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, UK
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nils H Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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Song L, Qiu X, Zhang C, Zhou H, Guo W, Ye Y, Wang R, Xiong H, Zhang J, Tang D, Zou L, Wang L, Yu Y, Guo T. Combining Non-Contrast CT Signs With Onset-to-Imaging Time to Predict the Evolution of Intracerebral Hemorrhage. Korean J Radiol 2024; 25:166-178. [PMID: 38238018 PMCID: PMC10831293 DOI: 10.3348/kjr.2023.0591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 11/05/2023] [Accepted: 11/19/2023] [Indexed: 01/31/2024] Open
Abstract
OBJECTIVE This study aimed to determine the predictive performance of non-contrast CT (NCCT) signs for hemorrhagic growth after intracerebral hemorrhage (ICH) when stratified by onset-to-imaging time (OIT). MATERIALS AND METHODS 1488 supratentorial ICH within 6 h of onset were consecutively recruited from six centers between January 2018 and August 2022. NCCT signs were classified according to density (hypodensities, swirl sign, black hole sign, blend sign, fluid level, and heterogeneous density) and shape (island sign, satellite sign, and irregular shape) features. Multivariable logistic regression was used to evaluate the association between NCCT signs and three types of hemorrhagic growth: hematoma expansion (HE), intraventricular hemorrhage growth (IVHG), and revised HE (RHE). The performance of the NCCT signs was evaluated using the positive predictive value (PPV) stratified by OIT. RESULTS Multivariable analysis showed that hypodensities were an independent predictor of HE (adjusted odds ratio [95% confidence interval] of 7.99 [4.87-13.40]), IVHG (3.64 [2.15-6.24]), and RHE (7.90 [4.93-12.90]). Similarly, OIT (for a 1-h increase) was an independent inverse predictor of HE (0.59 [0.52-0.66]), IVHG (0.72 [0.64-0.81]), and RHE (0.61 [0.54-0.67]). Blend and island signs were independently associated with HE and RHE (10.60 [7.36-15.30] and 10.10 [7.10-14.60], respectively, for the blend sign and 2.75 [1.64-4.67] and 2.62 [1.60-4.30], respectively, for the island sign). Hypodensities demonstrated low PPVs of 0.41 (110/269) or lower for IVHG when stratified by OIT. When OIT was ≤ 2 h, the PPVs of hypodensities, blend sign, and island sign for RHE were 0.80 (215/269), 0.90 (142/157), and 0.83 (103/124), respectively. CONCLUSION Hypodensities, blend sign, and island sign were the best NCCT predictors of RHE when OIT was ≤ 2 h. NCCT signs may assist in earlier recognition of the risk of hemorrhagic growth and guide early intervention to prevent neurological deterioration resulting from hemorrhagic growth.
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Affiliation(s)
- Lei Song
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China
| | - Xiaoming Qiu
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Hefei, China
| | - Hang Zhou
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Wenmin Guo
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yu Ye
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China
| | - Rujia Wang
- Department of Radiology, Tangshan Gongren Hospital, Tangshan, China
| | - Hui Xiong
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China
| | - Ji Zhang
- Department of Clinical Laboratory, Xiangyang Central Haspital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Dongfang Tang
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Liwei Zou
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Longsheng Wang
- Department of Radiology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Tingting Guo
- Department of Nuclear Medicine, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China.
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Wang P, Shen Y, Manaenko A, Liu F, Yang W, Xiao Z, Li P, Ran Y, Dang R, He Y, Wu Q, Xie P, Li Q. TMT-based quantitative proteomics reveals the protective mechanism of tenuigenin after experimental intracerebral hemorrhage in mice. JOURNAL OF ETHNOPHARMACOLOGY 2024; 319:117213. [PMID: 37739103 DOI: 10.1016/j.jep.2023.117213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/20/2023] [Indexed: 09/24/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Tenuigenin (TNG) is an extract obtained from Polygalae Radix. It possesses anti-inflammatory, antioxidant, and neuroprotective properties. However, the potential mechanism of TNG in intracerebral hemorrhage (ICH) has not been well studied. AIM OF THE STUDY In the present study, we aimed to identify the prospective mechanism of TNG in treating ICH. MATERIALS AND METHODS A total of 120 mice were divided into five groups: Sham group, ICH + vehicle group, ICH + TNG(8 mg/kg), ICH + TNG(16 mg/kg), and ICH + TNG(32 mg/kg). The modified Garcia test and beam walking test were carried out at 24 h and 72 h after ICH. Brain water content, haematoma volume and hemoglobin content examinations were performed at 72 h after ICH. TMT-based quantitative proteomics combined with bioinformatics analysis methods was used to distinguish differentially expressed proteins (DEPs) to explore potential pharmacological mechanisms. Western blotting was performed to validate representative proteins. RESULTS Our results showed that the optimal dose of TNG was 16 mg/kg, which could markedly improve neurological functions, and reduce cerebral oedema, haematoma volume and hemoglobin levels 72 h after ICH. A total of 404 DEPs (353 up-and 51 downregulated) were identified in the ICH + vehicle vs. sham group, while 342 DEPs (306 up-and 36 downregulated) and 76 DEPs (28 up-and 48 downregulated) were quantified in the TNG vs. sham group and TNG vs. ICH + vehicle group, respectively. In addition, a total of 26 DEPs were selected according to strict criteria. Complement and coagulation cascades were the most significantly enriched pathways, and two proteins (MBL-C and Car1) were further validated as hub molecules. CONCLUSIONS Our results suggested that the therapeutic effects of TNG on ICH were closely associated with the complement system, and that MBL-C and Car1 might be potential targets of TNG for the treatment of ICH.
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Affiliation(s)
- Peng Wang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - YiQing Shen
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Anatol Manaenko
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - FangYu Liu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - WenSong Yang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - ZhongSong Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - PeiZheng Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - YuXin Ran
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - RuoZhi Dang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yong He
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - QingYuan Wu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; Department of Neurology, Chongqing University Three Gorges Hospital, Chongqing, China
| | - Peng Xie
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Qi Li
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China; NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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9
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Nawabi J, Schlunk F, Dell'Orco A, Elsayed S, Mazzacane F, Desser D, Vu L, Vogt E, Cao H, Böhmer MFH, Akkurt BH, Sporns PB, Pasi M, Jensen-Kondering U, Broocks G, Penzkofer T, Fiehler J, Padovani A, Hanning U, Morotti A. Non-contrast computed tomography features predict intraventricular hemorrhage growth. Eur Radiol 2023; 33:7807-7817. [PMID: 37212845 PMCID: PMC10598100 DOI: 10.1007/s00330-023-09707-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/09/2023] [Accepted: 03/18/2023] [Indexed: 05/23/2023]
Abstract
OBJECTIVES Non-contrast computed tomography (NCCT) markers are robust predictors of parenchymal hematoma expansion in intracerebral hemorrhage (ICH). We investigated whether NCCT features can also identify ICH patients at risk of intraventricular hemorrhage (IVH) growth. METHODS Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. NCCT markers were rated by two investigators for heterogeneous density, hypodensity, black hole sign, swirl sign, blend sign, fluid level, island sign, satellite sign, and irregular shape. ICH and IVH volumes were semi-manually segmented. IVH growth was defined as IVH expansion > 1 mL (eIVH) or any delayed IVH (dIVH) on follow-up imaging. Predictors of eIVH and dIVH were explored with multivariable logistic regression. Hypothesized moderators and mediators were independently assessed in PROCESS macro models. RESULTS A total of 731 patients were included, of whom 185 (25.31%) suffered from IVH growth, 130 (17.78%) had eIVH, and 55 (7.52%) had dIVH. Irregular shape was significantly associated with IVH growth (OR 1.68; 95%CI [1.16-2.44]; p = 0.006). In the subgroup analysis stratified by the IVH growth type, hypodensities were significantly associated with eIVH (OR 2.06; 95%CI [1.48-2.64]; p = 0.015), whereas irregular shape (OR 2.72; 95%CI [1.91-3.53]; p = 0.016) in dIVH. The association between NCCT markers and IVH growth was not mediated by parenchymal hematoma expansion. CONCLUSIONS NCCT features identified ICH patients at a high risk of IVH growth. Our findings suggest the possibility to stratify the risk of IVH growth with baseline NCCT and might inform ongoing and future studies. CLINICAL RELEVANCE STATEMENT Non-contrast CT features identified ICH patients at a high risk of intraventricular hemorrhage growth with subtype-specific differences. Our findings may assist in the risk stratification of intraventricular hemorrhage growth with baseline CT and might inform ongoing and future clinical studies. KEY POINTS • NCCT features identified ICH patients at a high risk of IVH growth with subtype-specific differences. • The effect of NCCT features was not moderated by time and location or indirectly mediated by hematoma expansion. • Our findings may assist in the risk stratification of IVH growth with baseline NCCT and might inform ongoing and future studies.
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Affiliation(s)
- Jawed Nawabi
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany.
| | - Frieder Schlunk
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
- Department of Neuroradiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Andrea Dell'Orco
- Department of Neuroradiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Sarah Elsayed
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Federico Mazzacane
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- U.C. Malattie Cerebrovascolari E Stroke Unit, IRCCS Fondazione Mondino, Pavia, Italy
| | - Dmitriy Desser
- Department of Neuroradiology (CCM), Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Ly Vu
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Estelle Vogt
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Haoyin Cao
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Mitte, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Maik F H Böhmer
- Department of Radiology, University Hospital Muenster, Muenster, Germany
| | - Burak Han Akkurt
- Department of Radiology, University Hospital Muenster, Muenster, Germany
| | - Peter B Sporns
- Department of Neuroradiology, Clinic for Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Marco Pasi
- Department of Neurology, University Hospital of Tours, Tours, France
| | - Ulf Jensen-Kondering
- Department of Neuroradiology, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Tobias Penzkofer
- Berlin Institute of Health (BIH), BIH Biomedical Innovation Academy, Berlin, Germany
- Department of Radiology, Charité - Universitätsmedizin Berlin, Campus Virchow Klinikum, Humboldt-Universität Zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, Neurology Clinic, University of Brescia, Brescia, Italy
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg Eppendorf, Hamburg, Germany
| | - Andrea Morotti
- Neurology Unit, Department of Neurological Sciences and Vision, ASST-Spedali Civili, Brescia, Italy
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Ducroux C, Nehme A, Rioux B, Panzini MA, Fahed R, Gioia LC, Létourneau-Guillon L. NCCT Markers of Intracerebral Hemorrhage Expansion Using Revised Criteria: An External Validation of Their Predictive Accuracy. AJNR Am J Neuroradiol 2023; 44:658-664. [PMID: 37169542 PMCID: PMC10249705 DOI: 10.3174/ajnr.a7871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/06/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND PURPOSE Several NCCT expansion markers have been proposed to improve the prediction of hematoma expansion. We retrospectively evaluated the predictive accuracy of 9 expansion markers. MATERIALS AND METHODS Patients admitted for intracerebral hemorrhage within 24 hours of last seen well were retrospectively included from April 2016 to April 2020. The primary outcome was revised hematoma expansion, defined as any of a ≥6-mL or ≥33% increase in intracerebral hemorrhage volume, a ≥ 1-mL increase in intraventricular hemorrhage volume, or de novo intraventricular hemorrhage. We assessed the predictive accuracy of expansion markers and determined their association with revised hematoma expansion. RESULTS We included 124 patients, of whom 51 (41%) developed revised hematoma expansion. The sensitivity of each marker for the prediction of revised hematoma expansion ranged from 4% to 78%; the specificity, 37%-97%; the positive likelihood ratio, 0.41-7.16; and the negative likelihood ratio, 0.49-1.06. By means of univariable logistic regressions, 5 markers were significantly associated with revised hematoma expansion: black hole (OR = 8.66; 95% CI, 2.15-58.14; P = .007), hypodensity (OR = 3.18; 95% CI, 1.49-6.93; P = .003), blend (OR = 2.90; 95% CI, 1.08-8.38; P = .04), satellite (OR = 2.84; 95% CI, 1.29-6.61; P = .01), and Barras shape (OR = 2.41, 95% CI; 1.17-5.10; P = .02). In multivariable models, only the black hole marker remained independently associated with revised hematoma expansion (adjusted OR = 5.62; 95% CI, 1.23-40.23; P = .03). CONCLUSIONS No single NCCT expansion marker had both high sensitivity and specificity for the prediction of revised hematoma expansion. Improved image-based analysis is needed to tackle limitations associated with current NCCT-based expansion markers.
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Affiliation(s)
- C Ducroux
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
- Neurovascular Health Program (C.D., L.C.G.)
- Department of Medicine (C.D., R.F.), Division of Neurology, The Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Ontario, Canada
| | - A Nehme
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
| | - B Rioux
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
- Centre for Clinical Brain Sciences (B.R.), University of Edinburgh, Edinburgh, UK
| | - M-A Panzini
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
| | - R Fahed
- Department of Medicine (C.D., R.F.), Division of Neurology, The Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Ontario, Canada
| | - L C Gioia
- From the Département des Neurosciences (C.D., A.N., B.R., M.-A.P., L.C.G.), Faculté de Médecine
- Département de Médicine (Neurologie) (C.D., A.N., B.R., M.-A.P., L.C.G.)
- Neurovascular Health Program (C.D., L.C.G.)
| | - L Létourneau-Guillon
- Département de Radiologie (L.L.-G.), Radio-oncologie et Médecine Nucléaire, Faculté de Médicine, Université de Montréal, Montréal, Quebec, Canada
- Département de Radiologie (L.L.-G.), Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
- Imaging and Engineering Axis (L.L.-G.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
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11
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Song L, Zhou H, Guo T, Qiu X, Tang D, Zou L, Ye Y, Fu Y, Wang R, Wang L, Mao H, Yu Y. Predicting Hemorrhage Progression in Deep Intracerebral Hemorrhage: A Multicenter Retrospective Cohort Study. World Neurosurg 2023; 170:e387-e401. [PMID: 36371042 DOI: 10.1016/j.wneu.2022.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/05/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Hemorrhage progression in deep intracerebral hemorrhage (ICH) involves not only the growth of parenchymal hematoma but also an increase in intraventricular hemorrhage (IVH). The search for methods that predict both the increased risk of parenchymal hematoma and IVH growth is warranted. METHODS We conducted a retrospective cohort study at multiple centers. Participants with deep ICH were enrolled from January 2018 to December 2021. Prediction models based on logistic regression analysis included clinical as well as routine radiographic and radiomics variables, separately or in combination. The performance of each model was evaluated using discrimination measures (e.g., area under the curve [AUC]). Evaluation of clinical utility was performed using decision curve analysis (DCA). RESULTS Overall, 647 individuals across 4 stroke centers were included. A total of 429 (66%) patients from 3 centers were assigned to the primary cohort and 218 (34%) from another center were placed in the validation cohort. Multivariate analysis showed that the Glasgow Coma Scale score, baseline ICH volume, IVH, blend sign, and radiomics score were associated with hemorrhage progression in the primary cohort. The clinical-radiomics model (AUC = 0.852 and 0.835) improved the prediction performance of hemorrhage progression compared to the Noncontrast computed tomography signs model (AUC = 0.666 and 0.618) in both the primary and validation cohorts, with similar results in the decision curve analysis curves. CONCLUSIONS The clinical-radiomics model outperformed the routine Noncontrast computed tomography signs model in predicting the progression of deep ICH. The clinical benefit of screening patients using this model may assist in risk stratification.
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Affiliation(s)
- Lei Song
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hang Zhou
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Tingting Guo
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiaoming Qiu
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Dongfang Tang
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Liwei Zou
- Department of Radiology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Yu Ye
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Yufei Fu
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Rujia Wang
- Department of Radiology, Tangshan Gongren Hospital, Tangshan, China
| | - Longsheng Wang
- Department of Radiology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Huaqing Mao
- School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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12
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Wang J, Wang D, Bian L, Wang A, Zhang X, Jiang R, Wang W, Ju Y, Lu J, Zhao X. Subarachnoid extension and unfavorable outcomes in patients with supratentorial intracerebral hemorrhage. BMC Neurol 2023; 23:46. [PMID: 36709260 PMCID: PMC9883933 DOI: 10.1186/s12883-023-03087-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 01/24/2023] [Indexed: 01/30/2023] Open
Abstract
OBJECTIVE Our study aimed to investigate the association between the subarachnoid extension of intracranial hemorrhage (SAHE) and clinical outcomes in patients with supratentorial intracerebral hemorrhage (ICH). METHODS We analyzed the data from a prospective, multi-center, and registry-based database. Two experienced investigators independently assessed ICH imaging data. We compared baseline characteristics and follow-up outcomes. Multivariable logistic regression analysis was used to evaluate the association between SAHE and poor clinical outcomes. We also performed Kaplan-Meier curves and Cox proportional hazards regression analyses to analyze whether SAHE was relevant to a higher mortality rate. RESULTS A total of 931 patients were included in this study (SAHE vs. no SAHE, 121 [13.0%] vs. 810 [87.0%]). Patients with SAHE had more severe neurological deficits, higher scores of the mRS, and more remarkable mortality rates at follow-up (all p values < 0.05). In multivariable-adjusted models, SAHE was independently associated with a higher risk of poor outcomes (adjusted OR [95%CI]: 2.030 [1.142-3.608] at 3 months; 2.348 [1.337-4.123] at 1 year). In addition, SAHE remained an independent association with an increased death rate at 1 year (adjusted HR [95%CI], 1.314[1.057-1.635]). In the subgroup analysis, the correlation between SAHE and prognosis exists in patients with lobar or deep ICH. CONCLUSIONS SAHE is independently associated with poor outcomes in patients with supratentorial ICH. It may provide a promising target for developing new predictive tools targeting ICH.
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Affiliation(s)
- Jinjin Wang
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Dandan Wang
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Liheng Bian
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Anxin Wang
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xiaoli Zhang
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Ruixuan Jiang
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Wenjuan Wang
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yi Ju
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jingjing Lu
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xingquan Zhao
- grid.411617.40000 0004 0642 1244Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District Beijing, 100070 China ,grid.411617.40000 0004 0642 1244China National Clinical Research Center for Neurological Diseases, Beijing, China ,grid.506261.60000 0001 0706 7839Research Unit of Artificial Intelligence in Cerebrovascular Disease, Chinese Academy of Medical Sciences, Beijing, China ,grid.24696.3f0000 0004 0369 153XBeijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
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Off-Hour Admission Is Associated with Poor Outcome in Patients with Intracerebral Hemorrhage. J Clin Med 2022; 12:jcm12010066. [PMID: 36614867 PMCID: PMC9821144 DOI: 10.3390/jcm12010066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/15/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
The mortality of stroke increases on weekends and during off-hour periods. We investigated the effect of off-hour admission on the outcomes of intracerebral hemorrhage (ICH) patients. We retrospectively analyzed a prospective cohort of ICH patients, admitted between January 2017 and December 2019 at the First Affiliated Hospital of Chongqing Medical University. Acute ICH within 72 h after onset with a baseline computed tomography and 3-month follow-up were included in our study. Multivariable logistic regression analysis was performed for calculating the odds ratios (OR) and 95% confidence interval (CI) for the outcome measurements. Of the 656 participants, 318 (48.5%) were admitted during on-hours, whereas 338 (51.5%) were admitted during off-hours. Patients with a poor outcome had a larger median baseline hematoma volume, of 27 mL (interquartile range 11.1-53.2 mL), and a lower median time from onset to imaging, of 2.8 h (interquartile range 1.4-9.6 h). Off-hour admission was significantly associated with a poor functional outcome at 3 months, after adjusting for cofounders (adjusted OR 2.17, 95% CI 1.35-3.47; p = 0.001). We found that patients admitted during off-hours had a higher risk of poor functional outcomes at 3 months than those admitted during working hours.
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Diagnostic Accuracy and Reliability of Noncontrast Computed Tomography Markers for Acute Hematoma Expansion among Radiologists. Tomography 2022; 8:2893-2901. [PMID: 36548534 PMCID: PMC9785236 DOI: 10.3390/tomography8060242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 12/04/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience. METHODS Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen's kappa (κ) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater κ statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters. RESULTS Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen's kappa from 0.70 (95% CI 0.65-0.81) to 0.96 (95% CI 0.94-0.98). The interrater Cohen's kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52-0.65) to 94 (95% CI 92-0.97). The intrarater gold-standard Cohen's kappa was almost perfect and ranged from 0.79 (95% CI 0.78-0.90) to 0.98 (95% CI 0.78-0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57-0.66) to 0.93 (95%CI 0.89-0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p-values < 0.05. CONCLUSION The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction.
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Yang W, Zhang S, Shen Y, Wei X, Zhao L, Xie X, Deng L, Li X, Lv X, Lv F, Dowlatshahi D, Li Q, Xie P. Noncontrast Computed Tomography Markers as Predictors of Revised Hematoma Expansion in Acute Intracerebral Hemorrhage. J Am Heart Assoc 2021; 10:e018248. [PMID: 33506695 PMCID: PMC7955436 DOI: 10.1161/jaha.120.018248] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 12/16/2020] [Indexed: 01/12/2023]
Abstract
Background Noncontrast computed tomography (NCCT) markers are the emerging predictors of hematoma expansion in intracerebral hemorrhage. However, the relationship between NCCT markers and the dynamic change of hematoma in parenchymal tissues and the ventricular system remains unclear. Methods and Results We included 314 consecutive patients with intracerebral hemorrhage admitted to our hospital from July 2011 to May 2017. The intracerebral hemorrhage volumes and intraventricular hemorrhage (IVH) volumes were measured using a semiautomated, computer-assisted technique. Revised hematoma expansion (RHE) was defined by incorporating the original definition of hematoma expansion into IVH growth. Receiver operating characteristic curve analysis was used to compare the performance of the NCCT markers in predicting the IVH growth and RHE. Of 314 patients in our study, 61 (19.4%) had IVH growth and 93 (23.9%) had RHE. After adjustment for potential confounding variables, blend sign, black hole sign, island sign, and expansion-prone hematoma could independently predict IVH growth and RHE in the multivariate logistic regression analysis. Expansion-prone hematoma had a higher predictive performance of RHE than any single marker. The diagnostic accuracy of RHE in predicting poor prognosis was significantly higher than that of hematoma expansion. Conclusions The NCCT markers are independently associated with IVH growth and RHE. Furthermore, the expansion-prone hematoma has a higher predictive accuracy for prediction of RHE and poor outcome than any single NCCT marker. These findings may assist in risk stratification of NCCT signs for predicting active bleeding.
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Affiliation(s)
- Wen‐Song Yang
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional DiseasesThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Shu‐Qiang Zhang
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional DiseasesThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yi‐Qing Shen
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional DiseasesThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Xiao Wei
- Department of Traditional Chinese MedicineChongqing Medical and Pharmaceutical CollegeChongqingChina
| | - Li‐Bo Zhao
- Department of NeurologyYongchuan Hospital of Chongqing Medical UniversityChongqingChina
- Chongqing Key Laboratory of Cerebrovascular Disease ResearchYongchuan Hospital of Chongqing Medical UniversityChongqingChina
| | - Xiong‐Fei Xie
- Department of RadiologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Lan Deng
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Xin‐Hui Li
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional DiseasesThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Xin‐Ni Lv
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Fa‐Jin Lv
- Department of RadiologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Dar Dowlatshahi
- Department of Medicine (Neurology)Ottawa Hospital Research InstituteUniversity of OttawaOntarioCanada
| | - Qi Li
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional DiseasesThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- Chongqing Key Laboratory of Cerebrovascular Disease ResearchYongchuan Hospital of Chongqing Medical UniversityChongqingChina
| | - Peng Xie
- Department of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional DiseasesThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
- Chongqing Key Laboratory of Cerebrovascular Disease ResearchYongchuan Hospital of Chongqing Medical UniversityChongqingChina
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