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Beaudoin AM, Ho JK, Lam A, Thijs V. Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment. Can Assoc Radiol J 2024; 75:549-557. [PMID: 38420881 DOI: 10.1177/08465371241234545] [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] [Indexed: 03/02/2024] Open
Abstract
Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.
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Affiliation(s)
- Ann-Marie Beaudoin
- Université de Sherbrooke, Sherbrooke, QC, Canada
- The Florey, Heidelberg, VIC, Australia
| | - Jan Kee Ho
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | | | - Vincent Thijs
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia
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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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Guo K, Zhu B, Li R, Xi J, Wang Q, Chen K, Shao Y, Liu J, Cao W, Liu Z, Di Z, Gu N. Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke. Front Neurol 2024; 15:1379031. [PMID: 38933326 PMCID: PMC11202100 DOI: 10.3389/fneur.2024.1379031] [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: 01/30/2024] [Accepted: 04/29/2024] [Indexed: 06/28/2024] Open
Abstract
Background Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis. Objective To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS. Methods This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model's predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes. Results Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility. Conclusion The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.
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Affiliation(s)
- Kun Guo
- Xi'an Central Hospital, Xi’an, China
| | - Bo Zhu
- Xi'an Central Hospital, Xi’an, China
| | - Rong Li
- Xi'an Central Hospital, Xi’an, China
| | - Jing Xi
- Xi'an Central Hospital, Xi’an, China
| | - Qi Wang
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - KongBo Chen
- Tongchuan Mining Bureau Central Hospital, Tongchuan, China
| | - Yuan Shao
- Tongchuan Mining Bureau Central Hospital, Tongchuan, China
| | - Jiaqi Liu
- Tongchuan Mining Bureau Central Hospital, Tongchuan, China
| | - Weili Cao
- Xi'an Central Hospital, Xi’an, China
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Yang Y, Guo Y. Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network. Front Neurol 2024; 15:1394879. [PMID: 38765270 PMCID: PMC11099238 DOI: 10.3389/fneur.2024.1394879] [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: 03/02/2024] [Accepted: 04/12/2024] [Indexed: 05/21/2024] Open
Abstract
Objectives This study proposed an outcome prediction method to improve the accuracy and efficacy of ischemic stroke outcome prediction based on the diversity of whole brain features, without using basic information about patients and image features in lesions. Design In this study, we directly extracted dynamic radiomics features (DRFs) from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) and further extracted static radiomics features (SRFs) and static encoding features (SEFs) from the minimum intensity projection (MinIP) map, which was generated from the time dimension of DSC-PWI images. After selecting whole brain features Ffuse from the combinations of DRFs, SRFs, and SEFs by the Lasso algorithm, various machine and deep learning models were used to evaluate the role of Ffuse in predicting stroke outcomes. Results The experimental results show that the feature Ffuse generated from DRFs, SRFs, and SEFs (Resnet 18) outperformed other single and combination features and achieved the best mean score of 0.971 both on machine learning models and deep learning models and the 95% CI were (0.703, 0.877) and (0.92, 0.983), respectively. Besides, the deep learning models generally performed better than the machine learning models. Conclusion The method used in our study can achieve an accurate assessment of stroke outcomes without segmentation of ischemic lesions, which is of great significance for rapid, efficient, and accurate clinical stroke treatment.
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Affiliation(s)
- Yingjian Yang
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China
- Shenzhen Lanmage Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Yingwei Guo
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China
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Gupta R, Bilgin C, Jabal MS, Kandemirli S, Ghozy S, Kobeissi H, Kallmes DF. Quality Assessment of Radiomics Studies on Functional Outcomes After Acute Ischemic Stroke-A Systematic Review. World Neurosurg 2024; 183:164-171. [PMID: 38056625 DOI: 10.1016/j.wneu.2023.11.154] [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: 10/17/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVE Radiomics is a machine-learning method that extracts features from medical images. The objective of the present systematic review was to assess the quality of existing studies that use radiomics methods to predict functional outcomes in patients after acute ischemic stroke. METHODS Studies using radiomics-extracted features to predict functional outcomes among patients with acute ischemic stroke using the modified Rankin Scale were included. PubMed, Scopus, Web of Science, and Embase were screened using the terms "radiomics" and "texture" in combination with "stroke." Quality scores were calculated based on Radiomics Quality Score, the IBSI (Image Biomarkers Standardization Initiative), and the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). RESULTS Fourteen studies were included. The median total Radiomics Quality Score was 14.5 (13-16) out of 36. Domains 1, 5, and 6 on protocol quality and stability of imaging and segmentation, level of evidence, and use of open science and data, respectively, were poor. Median IBSI score was 2.5 (1-5) out of 6. Few studies included bias-field correction algorithms, isovoxel resampling, skull stripping, or gray-level discretization. Of 14 studies, none received +6 points, 1 received +5 points, 5 received +4 points, 1 study received +3 points, 5 received +2 points, 2 received +1 points, and none received 0 points. As per the QUADAS-2, 6/14 (42.9%) studies had a risk of bias concern and 0/14 (0%) had applicability concern. CONCLUSIONS The quality of the included studies was low to moderate. With increasing use of radiomics, future studies should attempt to adhere to and report established radiomics quality guidelines.
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Affiliation(s)
- Rishabh Gupta
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA; University of Minnesota Medical School, Minneapolis, Minnesota, USA.
| | - Cem Bilgin
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamed S Jabal
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Sedat Kandemirli
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Sherief Ghozy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hassan Kobeissi
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA; Central Michigan University College of Medicine, Mount Pleasant, Michigan, USA
| | - David F Kallmes
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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Wang J, Fu K, Wang Z, Wang N, Wang X, Xu T, Li H, Han X, Wu Y. MRI-based clinical-radiomics nomogram to predict early neurological deterioration in isolated acute pontine infarction: a two-center study in Northeast China. BMC Neurol 2024; 24:39. [PMID: 38263044 PMCID: PMC10804506 DOI: 10.1186/s12883-024-03533-2] [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: 09/27/2023] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE To predict the appearance of early neurological deterioration (END) among patients with isolated acute pontine infarction (API) based on magnetic resonance imaging (MRI)-derived radiomics of the infarct site. METHODS 544 patients with isolated API were recruited from two centers and divided into the training set (n = 344) and the verification set (n = 200). In total, 1702 radiomics characteristics were extracted from each patient. A support vector machine algorithm was used to construct a radiomics signature (rad-score). Subsequently, univariate and multivariate logistic regression (LR) analysis was adopted to filter clinical indicators and establish clinical models. Then, based on the LR algorithm, the rad-score and clinical indicators were integrated to construct the clinical-radiomics model, which was compared with other models. RESULTS A clinical-radiomics model was established, including the 5 indicators rad-score, age, initial systolic blood pressure, initial National Institute of Health Stroke Scale, and triglyceride. A nomogram was then made based on the model. The nomogram had good predictive accuracy, with an area under the curve (AUC) of 0.966 (95% confidence interval [CI] 0.947-0.985) and 0.920 (95% [CI] 0.873-0.967) in the training and verification sets, respectively. According to the decision curve analysis, the clinical-radiomics model showed better clinical value than the other models. In addition, the calibration curves also showed that the model has excellent consistency. CONCLUSION The clinical-radiomics model combined MRI-derived radiomics and clinical metrics and may serve as a scoring tool for early prediction of END among patients with isolated API.
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Affiliation(s)
- Jia Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Kuang Fu
- Department of MR Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Zhenqi Wang
- Department of Neurology, The Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, Heilongjiang, China
| | - Ning Wang
- Department of MR Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Xiaokun Wang
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Tianquan Xu
- Department of MR Diagnosis, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang, China
| | - Haoran Li
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Xv Han
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China
| | - Yun Wu
- Department of Neurology, The Second Affiliated Hospital of Harbin Medical University, No.148. Baojian Road, NanGangDistrict, Heilongjiang, Heilongjiang prov, China.
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7
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Guo Y, Yang Y, Wang M, Luo Y, Guo J, Cao F, Lu J, Zeng X, Miao X, Zaman A, Kang Y. The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111847. [PMID: 36430982 PMCID: PMC9694195 DOI: 10.3390/life12111847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/05/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022]
Abstract
Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions.
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Affiliation(s)
- Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Mingming Wang
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
| | - Yu Luo
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
- Correspondence: (Y.L.); (J.G.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
| | - Jia Guo
- Department of Psychiatry, Columbia University, New York, NY 10027, USA
- Correspondence: (Y.L.); (J.G.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
| | - Fengqiu Cao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Jiaxi Lu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Xueqiang Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Xiaoqiang Miao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Asim Zaman
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
- Correspondence: (Y.L.); (J.G.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
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Guo Y, Yang Y, Cao F, Wang M, Luo Y, Guo J, Liu Y, Zeng X, Miu X, Zaman A, Lu J, Kang Y. A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke. J Clin Med 2022; 11:jcm11185364. [PMID: 36143010 PMCID: PMC9504165 DOI: 10.3390/jcm11185364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 12/18/2022] Open
Abstract
Background: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction. Methods: The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation. Results: In experimental group_A, the best AUCs (0.873 for stroke detection, 0.795 for NIHSS assessment, and 0.818 for outcome prediction) were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF (experimental group_B), the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF (experimental group_D), achieving the highest AUC scores in all three evaluation items (0.925 for stroke detection, 0.853 for NIHSS assessment, and 0.828 for outcome prediction). By the method in this study, comparing the best AUC of Ft-test in experimental group_A and the best_AUC in experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.
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Affiliation(s)
- Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Fengqiu Cao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Mingming Wang
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
| | - Yu Luo
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
- Correspondence: (Y.L.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
| | - Jia Guo
- Department of Psychiatry, Columbia University, New York, NY 10027, USA
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Xueqiang Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Xiaoqiang Miu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Asim Zaman
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Jiaxi Lu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
- Correspondence: (Y.L.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
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