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Zhou Z, Chen W, Yu R, Chen Y, Li X, Zhou H, Fan Q, Wang J, Wu X, Zhou Y, Zhou X, Guo D. HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage. Eur J Radiol 2024; 176:111533. [PMID: 38833770 DOI: 10.1016/j.ejrad.2024.111533] [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: 01/16/2024] [Revised: 04/25/2024] [Accepted: 05/24/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE To develop and validate an end-to-end model for automatically predicting hematoma expansion (HE) after spontaneous intracerebral hemorrhage (sICH) using a novel deep learning framework. METHODS This multicenter retrospective study collected cranial noncontrast computed tomography (NCCT) images of 490 patients with sICH at admission for model training (n = 236), internal testing (n = 60), and external testing (n = 194). A HE-Mind model was designed to predict HE, which consists of a densely connected U-net for segmentation process, a multi-instance learning strategy for resolving label ambiguity and a Siamese network for classification process. Two radiomics models based on support vector machine or logistic regression and two deep learning models based on residual network or Swin transformer were developed for performance comparison. Reader experiments including physician diagnosis mode and artificial intelligence mode were conducted for efficiency comparison. RESULTS The HE-Mind model showed better performance compared to the comparative models in predicting HE, with areas under the curve of 0.849 and 0.809 in the internal and external test sets respectively. With the assistance of the HE-Mind model, the predictive accuracy and work efficiency of the emergency physician, junior radiologist, and senior radiologist were significantly improved, with accuracies of 0.768, 0.789, and 0.809 respectively, and reporting times of 7.26 s, 5.08 s, and 3.99 s respectively. CONCLUSIONS The HE-Mind model could rapidly and automatically process the NCCT data and predict HE after sICH within three seconds, indicating its potential to assist physicians in the clinical diagnosis workflow of HE.
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Affiliation(s)
- Zhiming Zhou
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidao Chen
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, China
| | - Ruize Yu
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, China
| | - Yuanyuan Chen
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuejiao Li
- Department of Emergency, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongli Zhou
- Department of Radiology, Nanchong Central Hospital, Nanchong 637000, Sichuan, China
| | - Qianrui Fan
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, China
| | - Jing Wang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yu Zhou
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xi Zhou
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dajing Guo
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Kumar A, Witsch J, Frontera J, Qureshi AI, Oermann E, Yaghi S, Melmed KR. Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial. J Neurol Sci 2024; 461:123048. [PMID: 38749281 DOI: 10.1016/j.jns.2024.123048] [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: 12/19/2023] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/13/2024]
Abstract
INTRODUCTION Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features. METHODS Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models. RESULTS Among 1000 patients included in the ATACH-2 trial, 924 had the complete parameters which were included in the analytical cohort. The median [interquartile range (IQR)] initial hematoma volume was 9.93.mm3 [5.03-18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631-0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641-0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models. CONCLUSION We developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.
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Affiliation(s)
- Arooshi Kumar
- Rush University Medical Center, Department of Neurology, Chicago, IL 60612, United States of America.
| | - Jens Witsch
- Hospital of the University of Pennsylvania, Department of Neurology, Philadelphia, PA 19104, United States of America
| | - Jennifer Frontera
- NYU Langone Medical Center, Department of Neurology, New York, NY 10016, United States of America
| | - Adnan I Qureshi
- Zeenat Qureshi Stroke Institutes and Department of Neurology, University of Missouri, Columbia, MO 65201, United States of America
| | - Eric Oermann
- NYU Langone Medical Center, Department of Neurology, New York, NY 10016, United States of America
| | - Shadi Yaghi
- Warren Alpert Medical School of Brown University, Department of Neurology, Providence, RI 02903, United States of America
| | - Kara R Melmed
- NYU Langone Medical Center, Department of Neurology, New York, NY 10016, United States of America; NYU Langone Medical Center, Department of Neurosurgery, New York, NY 10016, United States of America
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Liu Y, Zhao F, Niu E, Chen L. Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis. Neuroradiology 2024:10.1007/s00234-024-03399-8. [PMID: 38862772 DOI: 10.1007/s00234-024-03399-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/06/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage. METHODS Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies. RESULTS A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts. CONCLUSIONS Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.
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Affiliation(s)
- Yihua Liu
- Department of General medical subjects, Ezhou Central Hospital, Ezhou Hubei, 436000, China
| | - Fengfeng Zhao
- School of Clinical Medicine, Weifang Medical University, Weifang, 261000, China
| | - Enjing Niu
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China
| | - Liang Chen
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China.
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Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
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Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Feng C, Ding Z, Lao Q, Zhen T, Ruan M, Han J, He L, Shen Q. Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography. Eur Radiol 2024; 34:2908-2920. [PMID: 37938384 DOI: 10.1007/s00330-023-10410-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/20/2023] [Accepted: 09/21/2023] [Indexed: 11/09/2023]
Abstract
OBJECTIVES Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion. METHODS A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis. RESULTS The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore. CONCLUSION The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma. CLINICAL RELEVANCE STATEMENT Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion. KEY POINTS • We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.
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Affiliation(s)
- Changfeng Feng
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
- Department of Radiology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Zhongxiang Ding
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
| | - Qun Lao
- Department of Radiology, Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Tao Zhen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
| | - Mei Ruan
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China
| | - Jing Han
- Department of Radiology, Zhejiang Kangjing Hospital, Hangzhou, Zhejiang, China
| | - Linyang He
- Hangzhou Jianpei Technology Company Ltd, Xiaoshan District, Hangzhou, Zhejiang, China
| | - Qijun Shen
- Department of Radiology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No. 261, Huansha Road, Hangzhou, Zhejiang, China.
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Lee H, Lee J, Jang J, Hwang I, Choi KS, Park JH, Chung JW, Choi SH. Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT. Neuroradiology 2024; 66:577-587. [PMID: 38337016 PMCID: PMC10937749 DOI: 10.1007/s00234-024-03298-y] [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: 11/06/2023] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
PURPOSE To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.
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Affiliation(s)
- Hyochul Lee
- Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Junhyeok Lee
- Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Joon Jang
- Department of Biomedical Sciences, Seoul National University, Seoul, 03080, Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Artificial Intelligence Collaborative Network, Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
- Artificial Intelligence Collaborative Network, Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
| | - Jung Hyun Park
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, 07061, South Korea
| | - Jin Wook Chung
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Artificial Intelligence Collaborative Network, Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Seung Hong Choi
- Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
- Artificial Intelligence Collaborative Network, Department of Radiology, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, 08826, Republic of Korea
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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:qgae037. [PMID: 38507237 DOI: 10.1093/postmj/qgae037] [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: 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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Liao CH, Liu YJ. Brain is also time: good short-term outcome predictions of artificial intelligence in spontaneous intracerebral hemorrhage pave the way for the long-term assessment. Eur Radiol 2024:10.1007/s00330-024-10665-z. [PMID: 38396249 DOI: 10.1007/s00330-024-10665-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024]
Affiliation(s)
- Chun-Han Liao
- Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
- Division of Medical Imaging, Yuanlin Christian Hospital, Changhua, Taiwan, Republic of China
- Department of Medical Imaging, Changhua Christian Hospital, Changhua, Taiwan, Republic of China
| | - Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, Republic of 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
- 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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Zhao X, Zhou B, Luo Y, Chen L, Zhu L, Chang S, Fang X, Yao Z. CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage. Eur Radiol 2023:10.1007/s00330-023-10505-6. [PMID: 38127074 DOI: 10.1007/s00330-023-10505-6] [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: 05/09/2023] [Revised: 10/18/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images. METHODS A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS). RESULTS A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively). CONCLUSIONS Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage. CLINICAL RELEVANCE STATEMENT The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage. KEY POINTS • Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.
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Affiliation(s)
- Xianjing Zhao
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Bijing Zhou
- Department of Radiology, Huashan Hospital, Fudan University, Jing'an District, 12 Middle Urumqi Road, Shanghai, 200040, China
| | - Yong Luo
- Department of Radiology, Luzhou People's Hospital, Luzhou, China
| | - Lei Chen
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lequn Zhu
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shixin Chang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangming Fang
- Department of Medical Imaging, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214023, Jiangsu, China.
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Jing'an District, 12 Middle Urumqi Road, Shanghai, 200040, China.
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11
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Gilotra K, Swarna S, Mani R, Basem J, Dashti R. Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease. Front Hum Neurosci 2023; 17:1254417. [PMID: 37746051 PMCID: PMC10516608 DOI: 10.3389/fnhum.2023.1254417] [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: 07/07/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. Methods We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. Results The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. Conclusion The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.
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Affiliation(s)
| | | | | | | | - Reza Dashti
- Dashti Lab, Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, United States
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A machine learning approach for predicting perihematomal edema expansion in patients with intracerebral hemorrhage. Eur Radiol 2022; 33:4052-4062. [PMID: 36472694 DOI: 10.1007/s00330-022-09311-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/06/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Preventing the expansion of perihematomal edema (PHE) represents a novel strategy for the improvement of neurological outcomes in intracerebral hemorrhage (ICH) patients. Our goal was to predict early and delayed PHE expansion using a machine learning approach. METHODS We enrolled 550 patients with spontaneous ICH to study early PHE expansion, and 389 patients to study delayed expansion. Two imaging researchers rated the shape and density of hematoma in non-contrast computed tomography (NCCT). We trained a radiological machine learning (ML) model, a radiomics ML model, and a combined ML model, using data from radiomics, traditional imaging, and clinical indicators. We then validated these models on an independent dataset by using a nested 4-fold cross-validation approach. We compared models with respect to their predictive performance, which was assessed using the receiver operating characteristic curve. RESULTS For both early and delayed PHE expansion, the combined ML model was most predictive (early/delayed AUC values were 0.840/0.705), followed by the radiomics ML model (0.799/0.663), the radiological ML model (0.779/0.631), and the imaging readers (reader 1: 0.668/0.565, reader 2: 0.700/0.617). CONCLUSION We validated a machine learning approach with high interpretability for the prediction of early and delayed PHE expansion. This new technique may assist clinical practice for the management of neurocritical patients with ICH. KEY POINTS • This is the first study to use artificial intelligence technology for the prediction of perihematomal edema expansion. • A combined machine learning model, trained on data from radiomics, clinical indicators, and imaging features associated with hematoma expansion, outperformed all other methods.
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