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Sun T, Yu HY, Zhan CH, Guo HL, Luo MY. Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke. BMC Med Imaging 2024; 24:178. [PMID: 39030494 PMCID: PMC11264869 DOI: 10.1186/s12880-024-01365-7] [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: 04/29/2024] [Accepted: 07/15/2024] [Indexed: 07/21/2024] Open
Abstract
OBJECTIVE To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment. METHODS A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively. CONCLUSION The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.
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Affiliation(s)
- Tao Sun
- First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Hai-Yun Yu
- First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Chun-Hua Zhan
- Department of Medical Ultrasonics, The Third Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China
| | - Han-Long Guo
- First Clinical Medical College, Gannan Medical University, Ganzhou, Jiangxi, China
| | - Mu-Yun Luo
- Department of Neurosurgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China.
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Yang TH, Su YY, Tsai CL, Lin KH, Lin WY, Sung SF. Magnetic resonance imaging-based deep learning imaging biomarker for predicting functional outcomes after acute ischemic stroke. Eur J Radiol 2024; 174:111405. [PMID: 38447430 DOI: 10.1016/j.ejrad.2024.111405] [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: 12/22/2023] [Revised: 02/05/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores. METHOD This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance. RESULTS The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001). CONCLUSIONS Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Ying-Ying Su
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Chia-Ling Tsai
- Computer Science Department, Queens College, City University of New York, Flushing, NY, USA
| | - Kai-Hsuan Lin
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Wei-Yang Lin
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan; Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi, Taiwan.
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan; Department of Beauty & Health Care, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan.
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Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol 2024; 15:1367854. [PMID: 38606275 PMCID: PMC11007047 DOI: 10.3389/fneur.2024.1367854] [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: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.
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Affiliation(s)
- Zijian Zhao
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yuanyuan Zhang
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Jiuhui Su
- Department of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, China
| | - Lianbo Yang
- Department of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, China
| | - Luhang Pang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yingshan Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
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Fan K, Cao W, Chang H, Tian F. Predicting prognosis in patients with stroke treated with intravenous alteplase through blood pressure changes: A machine learning-based approach. J Clin Hypertens (Greenwich) 2023; 25:1009-1018. [PMID: 37843065 PMCID: PMC10631101 DOI: 10.1111/jch.14732] [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: 05/09/2023] [Revised: 08/21/2023] [Accepted: 09/15/2023] [Indexed: 10/17/2023]
Abstract
The use of machine learning (ML) in predicting disease prognosis has increased, and researchers have adopted different methods for variable selection to optimize early screening for AIS to determine its prognosis as soon as possible. We aimed to improve the understanding of the predictors of poor functional outcome at three months after discharge in AIS patients treated with intravenous thrombolysis and to construct a highly effective prognostic model to improve prediction accuracy. And four ML methods (random forest, support vector machine, naive Bayesian, and logistic regression) were used to screen and recombine the features for construction of an ML prognostic model. A total of 352 patients that had experienced AIS and had been treated with intravenous thrombolysis were recruited. The variables included in the model were NIHSS on admission, age, white blood cell count, percentage of neutrophils and triglyceride after thrombolysis, tirofiban, early neurological deterioration, early neurological improvement, and BP at each time point or period. The model's area under the curve for predicting 30-day modified Rankin scale was 0.790 with random forest, 0.542 with support vector machine, 0.411 with naive Bayesian, and 0.661 with logistic regression. The random forest model was shown to accurately evaluate the prognosis of AIS patients treated with intravenous thrombolysis, and therefore they may be helpful for accurate and personalized secondary prevention. The model offers improved prediction accuracy that may reduce rates of misdiagnosis and missed diagnosis in patients with AIS.
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Affiliation(s)
- Kaiting Fan
- Department of NeurologyXuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric DiseaseBeijingChina
| | - Wenya Cao
- Department of NeurologyXuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric DiseaseBeijingChina
| | - Hong Chang
- Department of NeurologyXuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric DiseaseBeijingChina
| | - Fei Tian
- Department of NeurologyXuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric DiseaseBeijingChina
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Yang Y, Tang L, Deng Y, Li X, Luo A, Zhang Z, He L, Zhu C, Zhou M. The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis. Front Neurosci 2023; 17:1256592. [PMID: 37746141 PMCID: PMC10512718 DOI: 10.3389/fnins.2023.1256592] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 08/02/2023] [Indexed: 09/26/2023] Open
Abstract
Objectives This study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke. Methods We searched PubMed, Embase, and Web of Science databases to identify studies using AI for acute stroke prognosis prediction from the database inception to February 2023. Selected studies were designed cohorts and had complete data. We used the Quality Assessment of Diagnostic Accuracy Studies tool to assess the qualities and bias of included studies and used a random-effects model to summarize and analyze the data. We used the area under curve (AUC) as an indicator of the predictive accuracy of AI models. Results We retrieved a total of 1,241 publications and finally included seven studies. There was a low risk of bias and no significant heterogeneity in the final seven studies. The total pooled AUC under the fixed-effects model was 0.872 with a 95% CI of (0.862-0.881). The DL subgroup showed its AUC of 0.888 (95%CI 0.872-0.904). The LR subgroup showed its AUC 0.852 (95%CI 0.835-0.869). The RF subgroup showed its AUC 0.863 (95%CI 0.845-0.882). The SVM subgroup showed its AUC 0.905 (95%CI 0.857-0.952). The Xgboost subgroup showed its AUC 0.905 (95%CI 0.805-1.000). Conclusion The accuracy of AI models in predicting the outcomes of ischemic stroke is good from our study. It could be an assisting tool for physicians in judging the outcomes of stroke patients. With the update of AI algorithms and the use of big data, further AI predictive models will perform better.
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Affiliation(s)
- Yujia Yang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Tang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yiting Deng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xuzi Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Anling Luo
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhao Zhang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li He
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Cairong Zhu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Muke Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Luo Y, Sun X, Kong X, Tong X, Xi F, Mao Y, Miao Z, Ma J. A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients. Eur J Radiol 2023; 161:110731. [PMID: 36804312 DOI: 10.1016/j.ejrad.2023.110731] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023]
Abstract
OBJECTIVE To develop an effective machine learning model to preoperatively predict the occurrence of futile recanalization (FR) of acute basilar artery occlusion (ABAO) patients with endovascular treatment (EVT). MATERIALS AND METHODS Data from 132 ABAO patients (109 male [82.6 %]; mean age ± standard deviation, 59.1 ± 12.5 years) were randomly divided into the training (n = 106) and test cohort (n = 26) with a ratio of 8:2. FR is defined as a poor outcome [modified Rankin Scale (mRS) 4-6] despite a successful recanalization [modified Thrombolysis in Cerebral Infarction (mTICI) ≥ 2b]. A total of 1130 radiomics features were extracted from diffusion-weighted imaging (DWI) images. The least absolute shrinkage and selection operator (LASSO) regression method was applicated to select features. Support vector machine (SVM) was applicated to construct radiomics and clinical models. Finally, a radiomics-clinical model that combined clinical with radiomics features was developed. The models were evaluated by receiver operating characteristic (ROC) curve and decision curve. RESULTS The area under the receiver operating characteristic (ROC) curve (AUC) of the radiomics-clinical model was 0.897 (95 % confidence interval, 0.837-0.958) in the training cohort and 0.935 (0.833-1.000) in the test cohort. The AUC of the radiomics model was 0.887 (0.824-0.951) in the training cohort and 0.840 (0.680-1.000) in the test cohort. The AUC of the clinical model was 0.746 (0.652-0.840) in the training cohort and 0.766 (0.569-0.964) in the test cohort. The AUC of the radiomics-clinical model was significantly larger than the clinical model (p = 0.016). A radiomics-clinical nomogram was developed. The decision curve analysis indicated its clinical usefulness. CONCLUSION The DWI-based radiomics-clinical machine learning model achieved satisfactory performance in predicting the FR of ABAO patients preoperatively.
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Affiliation(s)
- Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Xuan Sun
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China.
| | - Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Xu Tong
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Fengjun Xi
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Yu Mao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China
| | - Zhongrong Miao
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China.
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100071, China.
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