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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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Chen Y, Rivier CA, Mora SA, Torres Lopez V, Payabvash S, Sheth KN, Harloff A, Falcone GJ, Rosand J, Mayerhofer E, Anderson CD. Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan. Eur Stroke J 2024:23969873241260154. [PMID: 38880882 DOI: 10.1177/23969873241260154] [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] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.
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Affiliation(s)
- Yutong Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Cyprien A Rivier
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Samantha A Mora
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Victor Torres Lopez
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Sam Payabvash
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Andreas Harloff
- Department of Neurology, University of Freiburg, Freiburg, Germany
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Yale Center for Brain and Mind Health, New Haven, CT, USA
| | - Jonathan Rosand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Ernst Mayerhofer
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Christopher D Anderson
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
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Vitt JR, Mainali S. Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients. Semin Neurol 2024; 44:342-356. [PMID: 38569520 DOI: 10.1055/s-0044-1785504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.
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Affiliation(s)
- Jeffrey R Vitt
- Department of Neurological Surgery, UC Davis Medical Center, Sacramento, California
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, Virginia
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Cao S, Hu Y. Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index. Nutr Metab (Lond) 2024; 21:25. [PMID: 38745171 PMCID: PMC11092237 DOI: 10.1186/s12986-024-00802-2] [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: 01/25/2024] [Accepted: 05/01/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. METHODS Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. RESULTS An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798-0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. CONCLUSION The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.
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Affiliation(s)
- Shunshun Cao
- Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yangyang Hu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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Hu P, Yan T, Xiao B, Shu H, Sheng Y, Wu Y, Shu L, Lv S, Ye M, Gong Y, Wu M, Zhu X. Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. Int J Surg 2024; 110:01279778-990000000-01187. [PMID: 38489547 PMCID: PMC11175741 DOI: 10.1097/js9.0000000000001266] [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: 12/19/2023] [Accepted: 02/21/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P=0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P=0.001). CONCLUSION This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.
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Affiliation(s)
- Ping Hu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Tengfeng Yan
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Bing Xiao
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Hongxin Shu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Yilei Sheng
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Yanze Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Lei Shu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Shigang Lv
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Minhua Ye
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Yanyan Gong
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Miaojing Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Xingen Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
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Geng Z, Yang C, Zhao Z, Yan Y, Guo T, Liu C, Wu A, Wu X, Wei L, Tian Y, Hu P, Wang K. Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage. J Transl Med 2024; 22:236. [PMID: 38439097 PMCID: PMC10910789 DOI: 10.1186/s12967-024-04896-3] [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: 11/15/2023] [Accepted: 01/14/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH. METHODS Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model's performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform. RESULTS A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705-0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model's variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients. CONCLUSION We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.
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Affiliation(s)
- Zhi Geng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Chaoyi Yang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Ziye Zhao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Yibing Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Tao Guo
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Chaofan Liu
- Center for Biomedical Imaging, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Aimei Wu
- Department of Neurology, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Ling Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China.
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, 230000, China.
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China.
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China.
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Li J, Liang C, Dang J, Zhang Y, Chen H, Yan X, Liu Q. Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features. Front Surg 2024; 11:1344263. [PMID: 38389861 PMCID: PMC10882084 DOI: 10.3389/fsurg.2024.1344263] [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: 11/25/2023] [Accepted: 01/19/2024] [Indexed: 02/24/2024] Open
Abstract
Hypertensive Intracerebral Hemorrhage (HICH) is one of the most common types of cerebral hemorrhage with a high mortality and disability rate. Currently, preoperative non-contrast computed tomography (NCCT) scanning-guided stereotactic hematoma removal has achieved good results in treating HICH, but some patients still have poor prognoses. This study collected relevant clinical and radiomic data by retrospectively collecting and analyzing 432 patients who underwent stereotactic hematoma removal for HICH from January 2017 to December 2020 at the Liuzhou Workers Hospital. The prognosis of patients after 90 days was judged by the modified Rankin Scale (mRS) scale and divided into the good prognosis group (mRS ≤ 3) and the poor prognosis group (mRS > 3). The 268 patients were randomly divided into training and test sets in the ratio of 8:2, with 214 patients in the training set and 54 patients in the test set. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomics features. They were combining clinical features and radiomic features to build a joint prediction model of the nomogram. The AUCs of the clinical model for predicting different prognoses of patients undergoing stereotactic HICH were 0.957 and 0.922 in the training and test sets, respectively, while the AUCs of the radiomics model were 0.932 and 0.770, respectively, and the AUCs of the combined prediction model for building a nomogram were 0.987 and 0.932, respectively. Compared with a single clinical or radiological model, the nomogram constructed by fusing clinical variables and radiomic features could better identify the prognosis of HICH patients undergoing stereotactic hematoma removal after 90 days.
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Affiliation(s)
- Jinwei Li
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Cong Liang
- Department of Pharmacy, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Junsun Dang
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Yang Zhang
- Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Hongmou Chen
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Xianlei Yan
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Quan Liu
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
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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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Miyazaki Y, Kawakami M, Kondo K, Tsujikawa M, Honaga K, Suzuki K, Tsuji T. Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models. PLoS One 2023; 18:e0286269. [PMID: 37235575 DOI: 10.1371/journal.pone.0286269] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
OBJECTIVES Stepwise linear regression (SLR) is the most common approach to predicting activities of daily living at discharge with the Functional Independence Measure (FIM) in stroke patients, but noisy nonlinear clinical data decrease the predictive accuracies of SLR. Machine learning is gaining attention in the medical field for such nonlinear data. Previous studies reported that machine learning models, regression tree (RT), ensemble learning (EL), artificial neural networks (ANNs), support vector regression (SVR), and Gaussian process regression (GPR), are robust to such data and increase predictive accuracies. This study aimed to compare the predictive accuracies of SLR and these machine learning models for FIM scores in stroke patients. METHODS Subacute stroke patients (N = 1,046) who underwent inpatient rehabilitation participated in this study. Only patients' background characteristics and FIM scores at admission were used to build each predictive model of SLR, RT, EL, ANN, SVR, and GPR with 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) values were compared between the actual and predicted discharge FIM scores and FIM gain. RESULTS Machine learning models (R2 of RT = 0.75, EL = 0.78, ANN = 0.81, SVR = 0.80, GPR = 0.81) outperformed SLR (0.70) to predict discharge FIM motor scores. The predictive accuracies of machine learning methods for FIM total gain (R2 of RT = 0.48, EL = 0.51, ANN = 0.50, SVR = 0.51, GPR = 0.54) were also better than of SLR (0.22). CONCLUSIONS This study suggested that the machine learning models outperformed SLR for predicting FIM prognosis. The machine learning models used only patients' background characteristics and FIM scores at admission and more accurately predicted FIM gain than previous studies. ANN, SVR, and GPR outperformed RT and EL. GPR could have the best predictive accuracy for FIM prognosis.
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Affiliation(s)
- Yuta Miyazaki
- Department of Physical Rehabilitation, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Michiyuki Kawakami
- Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Kunitsugu Kondo
- Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Tsujikawa
- Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Kaoru Honaga
- Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
- Department of Rehabilitation Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kanjiro Suzuki
- Department of Rehabilitation Medicine, Waseda Clinic, Miyazaki, Japan
| | - Tetsuya Tsuji
- Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
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Wu TC, Liu YL, Chen JH, Ho CH, Zhang Y, Su MY. Prediction of poor outcome in stroke patients using radiomics analysis of intraparenchymal and intraventricular hemorrhage and clinical factors. Neurol Sci 2023; 44:1289-1300. [PMID: 36445541 DOI: 10.1007/s10072-022-06528-4] [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: 05/11/2022] [Accepted: 11/23/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE To build three prognostic models using radiomics analysis of the hemorrhagic lesions, clinical variables, and their combination, to predict the outcome of stroke patients with spontaneous intracerebral hemorrhage (sICH). MATERIALS AND METHODS Eighty-three sICH patients were included. Among them, 40 patients (48.2%) had poor prognosis with modified Rankin scale (mRS) of 5 and 6 at discharge, and the prognostic model was built to differentiate mRS ≤ 4 vs. 5 + 6. The region of interest (ROI) of intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) were separately segmented. Features were extracted using PyRadiomics, and the support vector machine was applied to select features and build radiomics models based on IPH and IPH + IVH. The clinical models were built using multivariate logistic regression, and then the radiomics scores were combined with clinical variables to build the combined model. RESULTS When using IPH, the AUC for radiomics, clinical, and combined model was 0.78, 0.82, and 0.87, respectively. When using IPH + IVH, the AUC was increased to 0.80, 0.84, and 0.90, respectively. The combined model had a significantly improved AUC compared to the radiomics by DeLong test. A clinical prognostic model based on the ICH score of 0-1 only achieved AUC of 0.71. CONCLUSIONS The combined model using the radiomics score derived from IPH + IVH and the clinical factors could achieve a high accuracy in prediction of sICH patients with poor outcome, which may be used to assist in making the decision about the optimal care.
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Affiliation(s)
- Te-Chang Wu
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan.
- Department of Medical Sciences Industry, Chang Jung Christian University, Tainan, Taiwan.
| | - Yan-Lin Liu
- Center for Functional Onco-Imaging of Radiological Sciences, School of Medicine, University of California, Irvine, CA, USA
| | - Jeon-Hor Chen
- Center for Functional Onco-Imaging of Radiological Sciences, School of Medicine, University of California, Irvine, CA, USA
- Department of Radiology, E-DA Hospital, E-DA Cancer Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Chung-Han Ho
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
- Department of Information Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan
| | - Yang Zhang
- Center for Functional Onco-Imaging of Radiological Sciences, School of Medicine, University of California, Irvine, CA, USA
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Min-Ying Su
- Center for Functional Onco-Imaging of Radiological Sciences, School of Medicine, University of California, Irvine, CA, USA
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Rusche T, Wasserthal J, Breit HC, Fischer U, Guzman R, Fiehler J, Psychogios MN, Sporns PB. Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage. J Clin Med 2023; 12:jcm12072631. [PMID: 37048712 PMCID: PMC10094957 DOI: 10.3390/jcm12072631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.
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Affiliation(s)
- Thilo Rusche
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Correspondence:
| | - Jakob Wasserthal
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Hanns-Christian Breit
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Urs Fischer
- Department of Neurology, University Hospital Basel, 4031 Basel, Switzerland
| | - Raphael Guzman
- Department of Neurosurgery, University Hospital Basel, 4031 Basel, Switzerland
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
| | - Peter B. Sporns
- Department of Neuroradiology, Clinic of Radiology & Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, 55131 Hamburg, Germany
- Department of Radiology and Neuroradiology, Stadtspital Zürich, 8063 Zürich, Switzerland
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Outcome Prediction of Spontaneous Supratentorial Intracerebral Hemorrhage after Surgical Treatment Based on Non-Contrast Computed Tomography: A Multicenter Study. J Clin Med 2023; 12:jcm12041580. [PMID: 36836120 PMCID: PMC9961203 DOI: 10.3390/jcm12041580] [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: 12/03/2022] [Revised: 01/19/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
This study aims to explore the value of a machine learning (ML) model based on radiomics features and clinical features in predicting the outcome of spontaneous supratentorial intracerebral hemorrhage (sICH) 90 days after surgery. A total of 348 patients with sICH underwent craniotomy evacuation of hematoma from three medical centers. One hundred and eight radiomics features were extracted from sICH lesions on baseline CT. Radiomics features were screened using 12 feature selection algorithms. Clinical features included age, gender, admission Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), midline shift (MLS), and deep ICH. Nine ML models were constructed based on clinical feature, and clinical features + radiomics features, respectively. Grid search was performed on different combinations of feature selection and ML model for parameter tuning. The averaged receiver operating characteristics (ROC) area under curve (AUC) was calculated and the model with the largest AUC was selected. It was then tested using multicenter data. The combination of lasso regression feature selection and logistic regression model based on clinical features + radiomics features had the best performance (AUC: 0.87). The best model predicted an AUC of 0.85 (95%CI, 0.75-0.94) on the internal test set and 0.81 (95%CI, 0.64-0.99) and 0.83 (95%CI, 0.68-0.97) on the two external test sets, respectively. Twenty-two radiomics features were selected by lasso regression. The second-order feature gray level non-uniformity normalized was the most important radiomics feature. Age is the feature with the greatest contribution to prediction. The combination of clinical features and radiomics features using logistic regression models can improve the outcome prediction of patients with sICH 90 days after surgery.
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Kurita Y, Meguro S, Tsuyama N, Kosugi I, Enomoto Y, Kawasaki H, Uemura T, Kimura M, Iwashita T. Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images. PLoS One 2023; 18:e0285996. [PMID: 37200281 DOI: 10.1371/journal.pone.0285996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 05/07/2023] [Indexed: 05/20/2023] Open
Abstract
Deep learning technology has been used in the medical field to produce devices for clinical practice. Deep learning methods in cytology offer the potential to enhance cancer screening while also providing quantitative, objective, and highly reproducible testing. However, constructing high-accuracy deep learning models necessitates a significant amount of manually labeled data, which takes time. To address this issue, we used the Noisy Student Training technique to create a binary classification deep learning model for cervical cytology screening, which reduces the quantity of labeled data necessary. We used 140 whole-slide images from liquid-based cytology specimens, 50 of which were low-grade squamous intraepithelial lesions, 50 were high-grade squamous intraepithelial lesions, and 40 were negative samples. We extracted 56,996 images from the slides and then used them to train and test the model. We trained the EfficientNet using 2,600 manually labeled images to generate additional pseudo labels for the unlabeled data and then self-trained it within a student-teacher framework. Based on the presence or absence of abnormal cells, the created model was used to classify the images as normal or abnormal. The Grad-CAM approach was used to visualize the image components that contributed to the classification. The model achieved an area under the curve of 0.908, accuracy of 0.873, and F1-score of 0.833 with our test data. We also explored the optimal confidence threshold score and optimal augmentation approaches for low-magnification images. Our model efficiently classified normal and abnormal images at low magnification with high reliability, making it a promising screening tool for cervical cytology.
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Affiliation(s)
- Yuki Kurita
- Department of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Shiori Meguro
- Department of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Naoko Tsuyama
- Division of Pathology, Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Isao Kosugi
- Department of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Yasunori Enomoto
- Department of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hideya Kawasaki
- Institute for NanoSuit Research, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Takashi Uemura
- Department of Pathology, JA Shizuoka Kohseiren Enshu Hospital, Hamamatsu, Shizuoka, Japan
| | - Michio Kimura
- Department of Medical Informatics, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Toshihide Iwashita
- Department of Regenerative and Infectious Pathology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
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Hu L, Yu J, Deng J, Zhou H, Yang F, Lu X. Development of nomogram to predict in-hospital death for patients with intracerebral hemorrhage: A retrospective cohort study. Front Neurol 2022; 13:968623. [PMID: 36504658 PMCID: PMC9729245 DOI: 10.3389/fneur.2022.968623] [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: 06/16/2022] [Accepted: 10/31/2022] [Indexed: 11/25/2022] Open
Abstract
Aim This study aimed to investigate the association between systemic immune-inflammation (SII) and the risk of in-hospital death for patients with intracerebral hemorrhage (ICH) in the intensive care units (ICU) and to further develop a prediction model related to SII in predicting the risk of in-hospital death for patients with ICH. Methods In this retrospective cohort study, we included 1,176 patients with ICH from the Medical Information Mart for Intensive Care III (MIMIC-III) database. All patients were randomly assigned to the training group for the construction of the nomogram and the testing group for the validation of the nomogram based on a ratio of 8:2. Predictors were screened by the least absolute shrinkage and selection operator (LASSO) regression analysis. A multivariate Cox regression analysis was used to investigate the association between SII and in-hospital death for patients with ICH in the ICU and develop a model for predicting the in-hospital death risk for ICU patients with ICH. The receiver operator characteristic curve was used to assess the predicting performance of the constructed nomogram. Results In the training group, 232 patients with ICH died while 708 survived. LASSO regression showed some predictors, including white blood cell count, glucose, blood urea nitrogen, SII, the Glasgow Coma Scale, age, heart rate, mean artery pressure, red blood cell, bicarbonate, red blood cell distribution width, liver cirrhosis, respiratory failure, renal failure, malignant cancer, vasopressor, and mechanical ventilation. A prediction model integrating these predictors was established. The area under the curve (AUC) of the nomogram was 0.810 in the training group and 0.822 in the testing group, indicating that this nomogram might have a good performance. Conclusion Systemic immune-inflammation was associated with an increased in-hospital death risk for patients with ICH in the ICU. A nomogram for in-hospital death risk for patients with ICH in the ICU was developed and validated.
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Affiliation(s)
- Linwang Hu
- Department of Neurosurgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Jie Yu
- Department of Neurosurgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Jian Deng
- Department of Neurosurgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Hong Zhou
- Department of Neurosurgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Feng Yang
- Department of Pharmacy, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Xiaohang Lu
- Department of Critical Care Medicine, People's Hospital of Ningxia Hui Autonomous Region, Ningxia, China,*Correspondence: Xiaohang Lu
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Huang X, Wang D, Zhang Q, Ma Y, Zhao H, Li S, Deng J, Ren J, Yang J, Zhao Z, Xu M, Zhou Q, Zhou J. Radiomics for prediction of intracerebral hemorrhage outcomes: A retrospective multicenter study. Neuroimage Clin 2022; 36:103242. [PMID: 36279754 PMCID: PMC9668657 DOI: 10.1016/j.nicl.2022.103242] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Accurate risk stratification of patients with intracerebral hemorrhage (ICH) could help refine adjuvant therapy selection and better understand the clinical course. We aimed to evaluate the value of radiomics features from hematomal and perihematomal edema areas for prognosis prediction and to develop a model combining clinical and radiomic features for accurate outcome prediction of patients with ICH. METHODS This multicenter study enrolled patients with ICH from January 2016 to November 2021. Their outcomes at 3 months were recorded based on the modified Rankin Scale (good, 0-3; poor, 4-6). Independent clinical and radiomic risk factors for poor outcome were identified through multivariate logistic regression analysis, and predictive models were developed. Model performance and clinical utility were evaluated in both internal and external cohorts. RESULTS Among the 1098 ICH patients evaluated (mean age, 60 ± 13 years), 703 (64 %) had poor outcomes. Age, hemorrhage volume and location, and Glasgow Coma Scale (GCS) were independently associated with outcomes. The area under the receiver operating characteristic curve (AUC) of the clinical model was 0.881 in the external validation cohort. Addition of the Rad-score (combined hematoma and perihematomal edema area) improved predictive accuracy and model performance (AUC, 0.893), net reclassification improvement, 0.140 (P < 0.001), and integrated discrimination improvement, 0.050 (P < 0.001). CONCLUSIONS The radiomics features of hematomal and perihematomal edema area have additional value in prognostic prediction; moreover, addition of radiomic features significantly improves model accuracy.
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Affiliation(s)
- Xiaoyu Huang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Dan Wang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China
| | - Qiaoying Zhang
- Department of Radiology, Xi'an Central Hospital, Xi An 710000, China
| | - Yaqiong Ma
- Second Clinical School, Lanzhou University, Lanzhou 730030, China; Department of Radiology, Gansu Provincial Hospital, Lanzhou 730030, China
| | - Hui Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | | | - Jingjing Yang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Zhiyong Zhao
- Department of Radiology, Gansu Provincial Hospital, Lanzhou 730030, China
| | - Min Xu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China; Department of Neurosurgery, Lanzhou University Second Hospital Lanzhou 730030, China.
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Shih YJ, Liu YL, Chen JH, Ho CH, Yang CC, Chen TY, Wu TC, Ko CC, Zhou JT, Zhang Y, Su MY. Prediction of Intraparenchymal Hemorrhage Progression and Neurologic Outcome in Traumatic Brain Injury Patients Using Radiomics Score and Clinical Parameters. Diagnostics (Basel) 2022; 12:diagnostics12071677. [PMID: 35885581 PMCID: PMC9320220 DOI: 10.3390/diagnostics12071677] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/04/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Radiomics analysis of spontaneous intracerebral hemorrhages on computed tomography (CT) images has been proven effective in predicting hematoma expansion and poor neurologic outcome. In contrast, there is limited evidence on its predictive abilities for traumatic intraparenchymal hemorrhage (IPH). (2) Methods: A retrospective analysis of 107 traumatic IPH patients was conducted. Among them, 45 patients (42.1%) showed hemorrhagic progression of contusion (HPC) and 51 patients (47.7%) had poor neurological outcome. The IPH on the initial CT was manually segmented for radiomics analysis. After feature extraction, selection and repeatability evaluation, several machine learning algorithms were used to derive radiomics scores (R-scores) for the prediction of HPC and poor neurologic outcome. (3) Results: The AUCs for R-scores alone to predict HPC and poor neurologic outcome were 0.76 and 0.81, respectively. Clinical parameters were used to build comparison models. For HPC prediction, variables including age, multiple IPH, subdural hemorrhage, Injury Severity Score (ISS), international normalized ratio (INR) and IPH volume taken together yielded an AUC of 0.74, which was significantly (p = 0.022) increased to 0.83 after incorporation of the R-score in a combined model. For poor neurologic outcome prediction, clinical variables of age, Glasgow Coma Scale, ISS, INR and IPH volume showed high predictability with an AUC of 0.92, and further incorporation of the R-score did not improve the AUC. (4) Conclusion: The results suggest that radiomics analysis of IPH lesions on initial CT images has the potential to predict HPC and poor neurologic outcome in traumatic IPH patients. The clinical and R-score combined model further improves the performance of HPC prediction.
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Affiliation(s)
- Yun-Ju Shih
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
- Department of Radiology, E-Da Hospital/I-Shou University, Kaohsiung 824, Taiwan
- Correspondence:
| | - Chung-Han Ho
- Department of Medical Research, Chi Mei Medical Center, Tainan 710, Taiwan;
- Department of Information Management, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan
| | - Cheng-Chun Yang
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan 711, Taiwan
| | - Te-Chang Wu
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
- Department of Medical Sciences Industry, Chang Jung Christian University, Tainan 711, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan 710, Taiwan; (Y.-J.S.); (C.-C.Y.); (T.-Y.C.); (T.-C.W.); (C.-C.K.)
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan 717, Taiwan
| | - Jonathan T. Zhou
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA 92868, USA; (Y.-L.L.); (J.T.Z.); (Y.Z.); (M.-Y.S.)
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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17
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Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): A review. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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18
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Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy-Cramer J, Iglesias Gonzalez JE, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Ann Neurol 2022; 92:574-587. [PMID: 35689531 DOI: 10.1002/ana.26435] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/04/2022] [Indexed: 11/08/2022]
Abstract
Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022.
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Affiliation(s)
- Isha R Chavva
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Anna L Crawford
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Mercy H Mazurek
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Matthew M Yuen
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Sam Payabvash
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Gordon Sze
- Department of Radiology, Yale School of Medicine, New Haven, CT
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Charles C Matouk
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Richa Sharma
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Steven J Schiff
- Departments of Neurosurgery, Engineering Science and Mechanics and Physics, Penn State University, University Park, PA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - Juan E Iglesias Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
| | - W Taylor Kimberly
- Department of Neurology, Division of Neurocritical Care, Massachusetts General Hospital, Boston, MA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT
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19
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Huang X, Wang D, Zhang Q, Ma Y, Li S, Zhao H, Deng J, Yang J, Ren J, Xu M, Xi H, Li F, Zhang H, Xie Y, Yuan L, Hai Y, Yue M, Zhou Q, Zhou J. Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage. Front Aging Neurosci 2022; 14:904085. [PMID: 35615596 PMCID: PMC9125153 DOI: 10.3389/fnagi.2022.904085] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/15/2022] [Indexed: 11/23/2022] Open
Abstract
We aimed to develop and validate an objective and easy-to-use model for identifying patients with spontaneous intracerebral hemorrhage (ICH) who have a poor 90-day prognosis. This three-center retrospective study included a large cohort of 1,122 patients with ICH who presented within 6 h of symptom onset [training cohort, n = 835; internal validation cohort, n = 201; external validation cohort (center 2 and 3), n = 86]. We collected the patients’ baseline clinical, radiological, and laboratory data as well as the 90-day functional outcomes. Independent risk factors for prognosis were identified through univariate analysis and multivariate logistic regression analysis. A nomogram was developed to visualize the model results while a calibration curve was used to verify whether the predictive performance was satisfactorily consistent with the ideal curve. Finally, we used decision curves to assess the clinical utility of the model. At 90 days, 714 (63.6%) patients had a poor prognosis. Factors associated with prognosis included age, midline shift, intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), hypodensities, ICH volume, perihematomal edema (PHE) volume, temperature, systolic blood pressure, Glasgow Coma Scale (GCS) score, white blood cell (WBC), neutrophil, and neutrophil-lymphocyte ratio (NLR) (p < 0.05). Moreover, age, ICH volume, and GCS were identified as independent risk factors for prognosis. For identifying patients with poor prognosis, the model showed an area under the receiver operating characteristic curve of 0.874, 0.822, and 0.868 in the training cohort, internal validation, and external validation cohorts, respectively. The calibration curve revealed that the nomogram showed satisfactory calibration in the training and validation cohorts. Decision curve analysis showed the clinical utility of the nomogram. Taken together, the nomogram developed in this study could facilitate the individualized outcome prediction in patients with ICH.
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Affiliation(s)
- Xiaoyu Huang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Dan Wang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Qiaoying Zhang
- Department of Radiology, Xi’an Central Hospital, Xi’an, China
| | - Yaqiong Ma
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Hui Zhao
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Jingjing Yang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | | | - Min Xu
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Fukai Li
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Hongyu Zhang
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Yijing Xie
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Yucheng Hai
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Mengying Yue
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
- *Correspondence: Junlin Zhou,
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20
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Deng B, Zhu W, Sun X, Xie Y, Dan W, Zhan Y, Xia Y, Liang X, Li J, Shi Q, Jiang L. Development and Validation of an Automatic System for Intracerebral Hemorrhage Medical Text Recognition and Treatment Plan Output. Front Aging Neurosci 2022; 14:798132. [PMID: 35462698 PMCID: PMC9028758 DOI: 10.3389/fnagi.2022.798132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/25/2022] [Indexed: 11/30/2022] Open
Abstract
The main purpose of the study was to explore a reliable way to automatically handle emergency cases, such as intracerebral hemorrhage (ICH). Therefore, an artificial intelligence (AI) system, named, H-system, was designed to automatically recognize medical text data of ICH patients and output the treatment plan. Furthermore, the efficiency and reliability of the H-system were tested and analyzed. The H-system, which is mainly based on a pretrained language model Bidirectional Encoder Representations from Transformers (BERT) and an expert module for logical judgment of extracted entities, was designed and founded by the neurosurgeon and AI experts together. All emergency medical text data were from the neurosurgery emergency electronic medical record database (N-eEMRD) of the First Affiliated Hospital of Chongqing Medical University, Chongqing Emergency Medical Center, and Chongqing First People’s Hospital, and the treatment plans of these ICH cases were divided into two types. A total of 1,000 simulated ICH cases were randomly selected as training and validation sets. After training and validating on simulated cases, real cases from three medical centers were provided to test the efficiency of the H-system. Doctors with 1 and 5 years of working experience in neurosurgery (Doctor-1Y and Doctor-5Y) were included to compare with H-system. Furthermore, the data of the H-system, for instance, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristics curve (AUC), were calculated and compared with Doctor-1Y and Doctor-5Y. In the testing set, the time H-system spent on ICH cases was significantly shorter than that of doctors with Doctor-1Y and Doctor-5Y. In the testing set, the accuracy of the H-system’s treatment plan was 88.55 (88.16–88.94)%, the specificity was 85.71 (84.99–86.43)%, and the sensitivity was 91.83 (91.01–92.65)%. The AUC value of the H-system in the testing set was 0.887 (0.884–0.891). Furthermore, the time H-system spent on ICH cases was significantly shorter than that of doctors with Doctor-1Y and Doctor-5Y. The accuracy and AUC of the H-system were significantly higher than that of Doctor-1Y. In addition, the accuracy of the H-system was more closed to that of Doctor-5Y. The H-system designed in the study can automatically recognize and analyze medical text data of patients with ICH and rapidly output accurate treatment plans with high efficiency. It may provide a reliable and novel way to automatically and rapidly handle emergency cases, such as ICH.
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Affiliation(s)
- Bo Deng
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenwen Zhu
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
| | - Xiaochuan Sun
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yanfeng Xie
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wei Dan
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yan Zhan
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yulong Xia
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xinyi Liang
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Li
- School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
- Jie Li,
| | - Quanhong Shi
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Quanhong Shi,
| | - Li Jiang
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Li Jiang,
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21
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Bowry R, Parker SA, Bratina P, Singh N, Yamal JM, Rajan SS, Jacob AP, Phan K, Czap A, Grotta JC. Hemorrhage Enlargement Is More Frequent in the First 2 Hours: A Prehospital Mobile Stroke Unit Study. Stroke 2022; 53:2352-2360. [DOI: 10.1161/strokeaha.121.037591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Hematoma enlargement (HE) after intracerebral hemorrhage (ICH) is a therapeutic target for improving outcomes. Hemostatic therapies to prevent HE may be more effective the earlier they are attempted. An understanding of HE in first 1 to 2 hours specifically in the prehospital setting would help guide future treatment interventions in this time frame and setting.
Methods:
Patients with spontaneous ICH within 4 hours of symptom onset were prospectively evaluated between May 2014 and April 2020 as a prespecified substudy within a multicenter trial of prehospital mobile stroke unit versus standard management. Baseline computed tomography scans obtained <1, 1 to 2, and 2 to 4 hours postsymptom onset on the mobile stroke unit in the prehospital setting were compared with computed tomography scans repeated 1 hour later and at 24 hours in the hospital. HE was defined as >6 mL if baseline ICH volume was
<
20 mL and 33% increase if baseline volume >20 mL. The association between time from symptom onset to baseline computed tomography (hours) and HE was investigated using Wilcoxon rank-sum test when time was treated as a continuous variable and using Fisher exact test when time was categorized. Kruskal-Wallis and Wilcoxon rank-sum tests evaluated differences in baseline volumes and HE. Univariable and multivariable logistic regression analyses were conducted to identify factors associated with HE and variable selection was performed using cross-validated L1-regularized (Lasso regression). This study adhered to STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology) for cohort studies.
Results:
One hundred thirty-nine patients were included. There was no difference between baseline ICH volumes obtained <1 hour (n=43) versus 1 to 2 hour (n=51) versus >2 hours (n=45) from symptom onset (median [interquartile range], 13 mL [6–24] versus 14 mL [6–30] versus 12 mL [4–19];
P
=0.65). However, within the same 3 time epochs, initial hematoma growth (volume/time from onset) was greater with earlier baseline scanning (median [interquartile range], 17 mL/hour [9–35] versus 9 mL/hour [5–23]) versus 4 mL/hour [2–7];
P
<0.001). Forty-nine patients had repeat scans 1 hour after baseline imaging (median, 2.3 hours [interquartile range. 1.9–3.1] after symptom onset). Eight patients (16%) had HE during that 1-hour interval; all of these occurred in patients with baseline imaging within 2 hours of onset (5/18=28% with baseline imaging within 1 hour, 3/18=17% within 1–2 hour, 0/13=0% >2 hours;
P
=0.02). HE did not occur between the scans repeated at 1 hour and 24 hours. No association between baseline variables and HE was detected in multivariable analyses.
Conclusions:
HE in the next hour occurs in 28% of ICH patients with baseline imaging within the first hour after symptom onset, and in 17% of those with baseline imaging between 1 and 2 hours. These patients would be a target for ultraearly hemostatic intervention.
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Affiliation(s)
- Ritvij Bowry
- Department of Neurology, McGovern Medical School, University of Texas Health Sciences Center, Houston (R.B., S.A.P., P.B., A.C.)
| | - Stephanie A. Parker
- Department of Neurology, McGovern Medical School, University of Texas Health Sciences Center, Houston (R.B., S.A.P., P.B., A.C.)
| | - Patti Bratina
- Department of Neurology, McGovern Medical School, University of Texas Health Sciences Center, Houston (R.B., S.A.P., P.B., A.C.)
| | - Noopur Singh
- Department of Biostatics and Data Science (N.S., J.M.Y., A.P.J.)
| | | | - Suja S. Rajan
- Department of Management, Policy and Community Health (S.S.R.)
| | - Asha P. Jacob
- Department of Biostatics and Data Science (N.S., J.M.Y., A.P.J.)
| | - Kenny Phan
- University of Texas School of Public Health, Houston. Clinical Innovation and Research Institute, Memorial Hermann Hospital, Houston, TX (K.P., J.C.G.)
| | - Alexandra Czap
- Department of Neurology, McGovern Medical School, University of Texas Health Sciences Center, Houston (R.B., S.A.P., P.B., A.C.)
| | - James C. Grotta
- University of Texas School of Public Health, Houston. Clinical Innovation and Research Institute, Memorial Hermann Hospital, Houston, TX (K.P., J.C.G.)
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22
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Development and Validation of Machine Learning Prediction Model for Post-Rehabilitation Functional Outcome After Intracerebral Hemorrhage. INTERDISCIPLINARY NEUROSURGERY 2022. [DOI: 10.1016/j.inat.2022.101560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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23
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Shan Y, Li Y, Wu X, Liu J, Zhang G, Xue Y, Gao G. Evaluation of Intracranial Hypertension in Patients With Hypertensive Intracerebral Hemorrhage Using Texture Analysis. Front Neurol 2022; 13:832234. [PMID: 35370879 PMCID: PMC8966839 DOI: 10.3389/fneur.2022.832234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 02/21/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose Texture analysis based on clinical images had been widely used in neurological diseases. This study aimed to achieve depth information of computed tomography (CT) images by texture analysis and to establish a model for noninvasive evaluation of intracranial pressure (ICP) in patients with hypertensive intracerebral hemorrhage (HICH). Methods Forty-seven patients with HICH were selected. Related CT images and ICP value were collected. The morphological features of hematoma volume, midline shift, and ventriculocranial ratio were measured. Forty textural features were extracted from regions of interest. Four models were established to predict intracranial hypertension with morphological features, textural features of anterior horn, textural features of temporal lobe, and textural features of posterior horn. Results Model of posterior horn had the highest ability to predict intracranial hypertension (AUC = 0.90, F1 score = 0.72), followed by model of anterior horn (AUC = 0.70, F1 score = 0.53) and model of temporal lobe (AUC = 0.70, F1 score = 0.58), and model of morphological features displayed the worst performance (AUC = 0.42, F1 score = 0.38). Conclusion Texture analysis can realize interpretation of CT images in depth, which has great potential in noninvasive evaluation of intracranial hypertension.
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Affiliation(s)
- Yingchi Shan
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yihua Li
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiang Wu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiaqi Liu
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guoqing Zhang
- Department of Neurosurgery, The People's Hospital of Qiannan, Guizhou, China
| | - Yajun Xue
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Yajun Xue
| | - Guoyi Gao
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Guoyi Gao
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24
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Wang P, Wu F, Wang Y, Du F, Yang X, Li J, Sheng J, Yu H, Jiang R. Computed tomography and clinical parameters predict intracerebral hemorrhage expansion. Medicine (Baltimore) 2022; 101:e28912. [PMID: 35244045 PMCID: PMC8896498 DOI: 10.1097/md.0000000000028912] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 02/01/2022] [Indexed: 01/04/2023] Open
Abstract
This study aimed to evaluate the association of imaging signs, and to establish a predictive model through selecting highly relevant imaging signs in combination with clinical parameters for hematoma expansion.Intracerebral Hemorrhage (ICH) patients who received 2 consecutive noncontrast computed tomography scans were examined and recruited through January 2014 to December 2020. Demographic information and clinical characteristics were collected. Two experienced radiologists reviewed baseline noncontrast computed tomography images to assess the imaging characteristics. Correlation analysis was analyzed with Pearson and Spearman correlation tests. The association between clinical and imaging predictors with hematoma expansion was evaluated in multivariate models. Receiver operating characteristic (ROC) curve analysis was adopted to evaluate predictive performance.A total of 232 ICH patients, with mean age of 59.73 years, and 31% of female were included, among which, 32 patients occurred with hematoma expansion. For sex, ICH density, low density in hematoma, the midline shift, and Glasgow Coma Scale score, liquid level, H-tra, edema Cor, H Volume, time from onset to examination, there were significant differences between the 2 groups. As for imaging signs, only blend sign showed a significant difference, that patients with blend sign had a higher incidence of ICH expansion. The logistic analysis found that radiation attenuation, liquid level, the midline shift, Glasgow Coma Scale score, history of ischemic stroke, and smoking could predict the occurrence of ICH expansion.In summary, the model combined radiological characteristics with clinical indicators showed considerable predictive performance. Further validation is needed to verify the findings and help transfer to clinical practice.
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Affiliation(s)
- Peng Wang
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Fa Wu
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Yang Wang
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Feizhou Du
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Xiaokun Yang
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Jianhao Li
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Jinping Sheng
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Hongmei Yu
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
| | - Rui Jiang
- Department of Radiology, The General Hospital of Western Theater Command, No. 270, Tianhui Road, Rongdu Avenue, Jinniu District, Chengdu, Sichuan Province, PR China
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Advances in computed tomography-based prognostic methods for intracerebral hemorrhage. Neurosurg Rev 2022; 45:2041-2050. [DOI: 10.1007/s10143-022-01760-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/18/2022] [Accepted: 02/18/2022] [Indexed: 10/19/2022]
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Zhao K, Zhao Q, Zhou P, Liu B, Zhang Q, Yang M. Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis. Int J Clin Pract 2022; 2022:9430097. [PMID: 35685590 PMCID: PMC9159188 DOI: 10.1155/2022/9430097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
Aim We intended to provide the clinical evidence that artificial intelligence (AI) could be used to assist doctors in the diagnosis of intracerebral hemorrhage (ICH). Methods Studies published in 2021 were identified after the literature search of PubMed, Embase, and Cochrane. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to perform the quality assessment of studies. Data extraction of diagnosis effect included accuracy (ACC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), area under curve (AUC), and Dice scores (Dices). The pooled effect with its 95% confidence interval (95%CI) was calculated by the random effects model. I-Square (I 2) was used to test heterogeneity. To check the stability of the overall results, sensitivity analysis was conducted by recalculating the pooled effect of the remaining studies after omitting the study with the highest quality or the random effects model was switched to the fixed effects model. Funnel plot was used to evaluate publication bias. To reduce heterogeneity, recalculating the pooled effect of the remaining studies after omitting the study with the lowest quality or perform subgroup analysis. Results Twenty-five diagnostic tests of ICH via AI and doctors with overall high quality were included. Pooled ACC, SEN, SPE, PPV, NPV, AUC, and Dices were 0.88 (0.83∼0.93), 0.85 (0.81∼0.89), 0.90 (0.88∼0.92), 0.80 (0.75∼0.85), 0.93 (0.91∼0.95), 0.84 (0.80∼0.89), and 0.90 (0.85∼0.95), respectively. There was no publication bias. All of results were stable as revealed by sensitivity analysis and were accordant as outcomes via subgroups analysis. Conclusion Under the background of the fourth industrial revolution, AI might be an effective and efficient tool to assist doctors in the clinical diagnosis of ICH.
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Affiliation(s)
- Kai Zhao
- Graduate School, Qinghai University, Xining 810016, Qinghai, China
| | - Qing Zhao
- Human Resource, Women's and Children's Hospital of Qinghai Province, Xining 810007, Qinghai, China
| | - Ping Zhou
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
| | - Bin Liu
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
| | - Qiang Zhang
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
| | - Mingfei Yang
- Department of Neurosurgery, Qinghai Provincial People's Hospital, Xining 810007, Qinghai, China
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Vedmurthy P, Pinto ALR, Lin DDM, Comi AM, Ou Y. Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber. BMJ Open 2022; 12:e053103. [PMID: 35121603 PMCID: PMC8819809 DOI: 10.1136/bmjopen-2021-053103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
INTRODUCTION Secondary analysis of hospital-hosted clinical data can save time and cost compared with prospective clinical trials for neuroimaging biomarker development. We present such a study for Sturge-Weber syndrome (SWS), a rare neurovascular disorder that affects 1 in 20 000-50 000 newborns. Children with SWS are at risk for developing neurocognitive deficit by school age. A critical period for early intervention is before 2 years of age, but early diagnostic and prognostic biomarkers are lacking. We aim to retrospectively mine clinical data for SWS at two national centres to develop presymptomatic biomarkers. METHODS AND ANALYSIS We will retrospectively collect clinical, MRI and neurocognitive outcome data for patients with SWS who underwent brain MRI before 2 years of age at two national SWS care centres. Expert review of clinical records and MRI quality control will be used to refine the cohort. The merged multisite data will be used to develop algorithms for abnormality detection, lesion-symptom mapping to identify neural substrate and machine learning to predict individual outcomes (presence or absence of seizures) by 2 years of age. Presymptomatic treatment in 0-2 years and before seizure onset may delay or prevent the onset of seizures by 2 years of age, and thereby improve neurocognitive outcomes. The proposed work, if successful, will be one of the largest and most comprehensive multisite databases for the presymptomatic phase of this rare disease. ETHICS AND DISSEMINATION This study involves human participants and was approved by Boston Children's Hospital Institutional Review Board: IRB-P00014482 and IRB-P00025916 Johns Hopkins School of Medicine Institutional Review Board: NA_00043846. Participants gave informed consent to participate in the study before taking part. The Institutional Review Boards at Kennedy Krieger Institute and Boston Children's Hospital approval have been obtained at each site to retrospectively study this data. Results will be disseminated by presentations, publication and sharing of algorithms generated.
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Affiliation(s)
- Pooja Vedmurthy
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Anna L R Pinto
- Department of Neurology, Division of Epilepsy, Harvard Medical School, Boston, Massachusetts, USA
| | - Doris D M Lin
- Neuroradiology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Anne M Comi
- Department of Neurology and Developmental Medicine, Hugo Moser Research Institute, Baltimore, Maryland, USA
- Department of Neurology and Pediatrics, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology and Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Radiology, Boston Children's Hospital; Harvard Medical School, Boston, MA, USA
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Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies. BIOMED RESEARCH INTERNATIONAL 2022; 2022:5416726. [PMID: 35111845 PMCID: PMC8802084 DOI: 10.1155/2022/5416726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/01/2021] [Accepted: 12/08/2021] [Indexed: 01/09/2023]
Abstract
Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.
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Mainali S, Darsie ME, Smetana KS. Machine Learning in Action: Stroke Diagnosis and Outcome Prediction. Front Neurol 2021; 12:734345. [PMID: 34938254 PMCID: PMC8685212 DOI: 10.3389/fneur.2021.734345] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/28/2021] [Indexed: 01/01/2023] Open
Abstract
The application of machine learning has rapidly evolved in medicine over the past decade. In stroke, commercially available machine learning algorithms have already been incorporated into clinical application for rapid diagnosis. The creation and advancement of deep learning techniques have greatly improved clinical utilization of machine learning tools and new algorithms continue to emerge with improved accuracy in stroke diagnosis and outcome prediction. Although imaging-based feature recognition and segmentation have significantly facilitated rapid stroke diagnosis and triaging, stroke prognostication is dependent on a multitude of patient specific as well as clinical factors and hence accurate outcome prediction remains challenging. Despite its vital role in stroke diagnosis and prognostication, it is important to recognize that machine learning output is only as good as the input data and the appropriateness of algorithm applied to any specific data set. Additionally, many studies on machine learning tend to be limited by small sample size and hence concerted efforts to collate data could improve evaluation of future machine learning tools in stroke. In the present state, machine learning technology serves as a helpful and efficient tool for rapid clinical decision making while oversight from clinical experts is still required to address specific aspects not accounted for in an automated algorithm. This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagnosis and outcome prediction.
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Affiliation(s)
- Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, United States
| | - Marin E Darsie
- Department of Emergency Medicine, University of Wisconsin Hospitals and Clinics, Madison, WI, United States.,Department of Neurological Surgery, University of Wisconsin Hospitals and Clinics, Madison, WI, United States
| | - Keaton S Smetana
- Department of Pharmacy, The Ohio State University Wexner Medical Center, Columbus, OH, United States
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Neuroimaging of Acute Intracerebral Hemorrhage. J Clin Med 2021; 10:jcm10051086. [PMID: 33807843 PMCID: PMC7962049 DOI: 10.3390/jcm10051086] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/16/2021] [Accepted: 03/02/2021] [Indexed: 01/25/2023] Open
Abstract
Intracerebral hemorrhage (ICH) accounts for 10% to 20% of all strokes worldwide and is associated with high morbidity and mortality. Neuroimaging is clinically important for the rapid diagnosis of ICH and underlying etiologies, but also for identification of ICH expansion, often as-sociated with an increased risk for poor outcome. In this context, rapid assessment of early hema-toma expansion risk is both an opportunity for therapeutic intervention and a potential hazard for hematoma evacuation surgery. In this review, we provide an overview of the current literature surrounding the use of multimodal neuroimaging of ICH for etiological diagnosis, prediction of early hematoma expansion, and prognostication of neurological outcome. Specifically, we discuss standard imaging using computed tomography, the value of different vascular imaging modalities to identify underlying causes and present recent advances in magnetic resonance imaging and computed tomography perfusion.
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