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Liu Y, Zhao F, Niu E, Chen L. Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis. Neuroradiology 2024:10.1007/s00234-024-03399-8. [PMID: 38862772 DOI: 10.1007/s00234-024-03399-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/06/2024] [Indexed: 06/13/2024]
Abstract
PURPOSE Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage. METHODS Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies. RESULTS A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts. CONCLUSIONS Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.
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Affiliation(s)
- Yihua Liu
- Department of General medical subjects, Ezhou Central Hospital, Ezhou Hubei, 436000, China
| | - Fengfeng Zhao
- School of Clinical Medicine, Weifang Medical University, Weifang, 261000, China
| | - Enjing Niu
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China
| | - Liang Chen
- Department of Adult Internal Medicine, Qingdao Women's and Children's Hospital, No. 217 Liaoyang West Street, Shibei District, Qingdao, 266000, Shandong, China.
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2
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Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
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Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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He H, Liu J, Li C, Guo Y, Liang K, Du J, Xue J, Liang Y, Chen P, Liu L, Cui M, Wang J, Liu Y, Tian S, Deng Y. Predicting Hematoma Expansion and Prognosis in Cerebral Contusions: A Radiomics-Clinical Approach. J Neurotrauma 2024; 41:1337-1352. [PMID: 38326935 DOI: 10.1089/neu.2023.0410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.
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Affiliation(s)
- Haoyue He
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- Bioengineering College, Chongqing University, Chongqing, China
| | - Jinxin Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- School of Medicine, Chongqing University, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yi Guo
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Kaixin Liang
- Department of Neurosurgery, Yubei District Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Jun Du
- Department of Neurosurgery, Chongqing Qianjiang Central Hospital, Chongqing University Qianjiang Hospital, Chongqing, China
| | - Jun Xue
- Department of Neurosurgery, Bishan Hospital of Chongqing, Bishan Hospital of Chongqing Medical University, Chongqing, China
| | - Yidan Liang
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Peng Chen
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Liu Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Min Cui
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Jia Wang
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Ye Liu
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
- School of Medicine, Chongqing University, Chongqing, China
| | - Shanshan Tian
- Department of Prehospital Emergency, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yongbing Deng
- Department of Neurosurgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
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Zaman S, Dierksen F, Knapp A, Haider SP, Abou Karam G, Qureshi AI, Falcone GJ, Sheth KN, Payabvash S. Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival. Diagnostics (Basel) 2024; 14:944. [PMID: 38732358 PMCID: PMC11083693 DOI: 10.3390/diagnostics14090944] [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: 03/27/2024] [Revised: 04/23/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
The mortality rate of acute intracerebral hemorrhage (ICH) can reach up to 40%. Although the radiomics of ICH have been linked to hematoma expansion and outcomes, no research to date has explored their correlation with mortality. In this study, we determined the admission non-contrast head CT radiomic correlates of survival in supratentorial ICH, using the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) trial dataset. We extracted 107 original radiomic features from n = 871 admission non-contrast head CT scans. The Cox Proportional Hazards model, Kaplan-Meier Analysis, and logistic regression were used to analyze survival. In our analysis, the "first-order energy" radiomics feature, a metric that quantifies the sum of squared voxel intensities within a region of interest in medical images, emerged as an independent predictor of higher mortality risk (Hazard Ratio of 1.64, p < 0.0001), alongside age, National Institutes of Health Stroke Scale (NIHSS), and baseline International Normalized Ratio (INR). Using a Receiver Operating Characteristic (ROC) analysis, "the first-order energy" was a predictor of mortality at 1-week, 1-month, and 3-month post-ICH (all p < 0.0001), with Area Under the Curves (AUC) of >0.67. Our findings highlight the potential role of admission CT radiomics in predicting ICH survival, specifically, a higher "first-order energy" or very bright hematomas are associated with worse survival outcomes.
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Affiliation(s)
- Saif Zaman
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Fiona Dierksen
- Department of Radiology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Avery Knapp
- Independent Researcher, Guaynabo, PR 00934, USA
| | - Stefan P. Haider
- Department of Radiology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Gaby Abou Karam
- Department of Radiology, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Adnan I. Qureshi
- Department of Neurology, Zeenat Qureshi Stroke Institute, University of Missouri, Columbia, MO 65211, USA
| | - Guido J. Falcone
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06510, USA (K.N.S.)
| | - Kevin N. Sheth
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06510, USA (K.N.S.)
| | - Seyedmehdi Payabvash
- Department of Radiology, Yale University School of Medicine, New Haven, CT 06510, USA
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5
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Li Q, Li F, Liu H, Li Y, Chen H, Yang W, Duan S, Zhang H. CT-based radiomics models predict spontaneous intracerebral hemorrhage expansion and are comparable with CT angiography spot sign. Front Neurol 2024; 15:1332509. [PMID: 38476195 PMCID: PMC10929015 DOI: 10.3389/fneur.2024.1332509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/30/2024] [Indexed: 03/14/2024] Open
Abstract
Background and purpose This study aimed to investigate the efficacy of radiomics, based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) images, in predicting early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (SICH). Additionally, the predictive performance of these models was compared with that of the established CTA spot sign. Materials and methods A retrospective analysis was conducted using CT images from 182 patients with SICH. Data from the patients were divided into a training set (145 cases) and a testing set (37 cases) using random stratified sampling. Two radiomics models were constructed by combining quantitative features extracted from NCCT images (the NCCT model) and CTA images (the CTA model) using a logistic regression (LR) classifier. Additionally, a univariate LR model based on the CTA spot sign (the spot sign model) was established. The predictive performance of the two radiomics models and the spot sign model was compared according to the area under the receiver operating characteristic (ROC) curve (AUC). Results For the training set, the AUCs of the NCCT, CTA, and spot sign models were 0.938, 0.904, and 0.726, respectively. Both the NCCT and CTA models demonstrated superior predictive performance compared to the spot sign model (all P < 0.001), with the performance of the two radiomics models being comparable (P = 0.068). For the testing set, the AUCs of the NCCT, CTA, and spot sign models were 0.925, 0.873, and 0.720, respectively, with only the NCCT model exhibiting significantly greater predictive value than the spot sign model (P = 0.041). Conclusion Radiomics models based on NCCT and CTA images effectively predicted HE in patients with SICH. The predictive performances of the NCCT and CTA models were similar, with the NCCT model outperforming the spot sign model. These findings suggest that this approach has the potential to reduce the need for CTA examinations, thereby reducing radiation exposure and the use of contrast agents in future practice for the purpose of predicting hematoma expansion.
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Affiliation(s)
- Qingrun Li
- Department of Radiology, Traditional Chinese Medicine Hospital of Dianjiang Chongqing, Chongqing, China
| | - Feng Li
- Department of Radiology, Traditional Chinese Medicine Hospital of Dianjiang Chongqing, Chongqing, China
| | - Hao Liu
- Department of Research and Development, Yizhun Medical AI Co. Ltd., Beijing, China
| | - Yan Li
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China
| | - Hongri Chen
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China
| | - Wenrui Yang
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China
| | - Shaofeng Duan
- Precision Health Institution, GE Healthcare, Shanghai, China
| | - Hongying Zhang
- Department of Radiology, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, China
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6
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Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A, Petersen NH, de Havenon A, Falcone GJ, Sheth KN, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digit Med 2024; 7:26. [PMID: 38321131 PMCID: PMC10847454 DOI: 10.1038/s41746-024-01007-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Abstract
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.
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Grants
- U24 NS107136 NINDS NIH HHS
- UL1 TR001863 NCATS NIH HHS
- K76 AG059992 NIA NIH HHS
- P30 AG021342 NIA NIH HHS
- R03 NS112859 NINDS NIH HHS
- U24 NS107215 NINDS NIH HHS
- U01 NS106513 NINDS NIH HHS
- 2020097 Doris Duke Charitable Foundation
- K23 NS118056 NINDS NIH HHS
- R01 NR018335 NINR NIH HHS
- Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- Doris Duke Charitable Foundation (DDCF)
- Doris Duke Charitable Foundation (2020097), American Society of Neuroradiology, and National Institutes of Health (K23NS118056).
- National Institutes of Health (K76AG059992, R03NS112859, and P30AG021342), the American Heart Association (18IDDG34280056), the Yale Pepper Scholar Award, and the Neurocritical Care Society Research Fellowship
- National Institutes of Health (U24NS107136, U24NS107215, R01NR018335, and U01NS106513) and the American Heart Association (18TPA34170180 and 17CSA33550004) and a Hyperfine Research Inc research grant.
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Affiliation(s)
- Anh T Tran
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Gaby Abou Karam
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elisa R Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Adnan I Qureshi
- Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Pina C Sanelli
- Department of Radiology, Northwell Health, Manhasset, NY, USA
| | - David J Werring
- Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, UK
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Nils H Petersen
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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Pei L, Fang T, Xu L, Ni C. A Radiomics Model Based on CT Images Combined with Multiple Machine Learning Models to Predict the Prognosis of Spontaneous Intracerebral Hemorrhage. World Neurosurg 2024; 181:e856-e866. [PMID: 37931880 DOI: 10.1016/j.wneu.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 11/01/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE We aimed to construct 3 predictive models, including a clinical model, a radiomics model, and a combined model, to forecast the discharge prognosis of patients with intracerebral hemorrhage on admission. METHODS A retrospective study was conducted, involving a total of 161 patients with intracerebral hemorrhage (ICH). At a ratio of 7:3, 115 of these patients were assigned to the training cohort, and 46 of these patients were assigned to the validation cohort. To produce the radionics signature and pick the features to use in its construction, the least absolute shrinkage and selection operator (LASSO) regression was applied. Five machine models were applied, and the optimal model was chosen to construct the radionics model. A clinical model was constructed using univariate and stepwise analysis to identify independent risk variables for poor outcomes at discharge. A predictive combined model nomogram was generated by integrating the clinical model and radiomics model. The performance of the nomogram was assessed in the training cohort and validated in the validation cohort. Analyses of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve were performed to assess the performance of the combined model. RESULTS This study encompassed a cohort of 161 individuals diagnosed with intracerebral hemorrhage (ICH), consisting of 110 males and 51 females. Utilizing the modified Rankin Scale (mRS) at discharge, the analysis revealed that 89 patients (55.3%) had a good prognosis, while 72 patients (44.7%) had a poor prognosis. Only 8 out of 1130 radiomics features were selected and used in conjunction with the LR algorithm to develop the radiomics model. Sex, IVH, GCS score, and ICH volume were determined to be independent predictors of poor outcomes at the time of discharge. The AUC values of the combined model, radiomics model, and clinical model were 0.8583, 0.8364, and 0.7579 in the training cohort, and 0.9153, 0.8692, and 0.7114 in the validation cohort, respectively. The combined model nomogram exhibited good calibration and clinical benefit in both the training and validation cohorts. The decision curve analysis (DCA) displays that the combined model obtained the highest net benefit compared to the radiomics model and clinics model in the training cohort. CONCLUSIONS The combined model demonstrates reliability and efficacy in predicting the discharge prognosis of ICH, enabling physicians to perform individualized risk assessments, and make optimal choices about patients with ICH.
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Affiliation(s)
- Lei Pei
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Tao Fang
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Liang Xu
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China
| | - Chenfeng Ni
- Department of Radiology, Quzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, China.
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Chen Q, Fu C, Qiu X, He J, Zhao T, Zhang Q, Hu X, Hu H. Machine-learning-based performance comparison of two-dimensional (2D) and three-dimensional (3D) CT radiomics features for intracerebral haemorrhage expansion. Clin Radiol 2024; 79:e26-e33. [PMID: 37926647 DOI: 10.1016/j.crad.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/07/2023] [Accepted: 10/02/2023] [Indexed: 11/07/2023]
Abstract
AIM To investigate the value of non-contrast CT (NCCT)-based two-dimensional (2D) radiomics features in predicting haematoma expansion (HE) after spontaneous intracerebral haemorrhage (ICH) and compare its predictive ability with the three-dimensional (3D) signature. MATERIALS AND METHODS Three hundred and seven ICH patients who received baseline NCCT within 6 h of ictus from two stroke centres were analysed retrospectively. 2D and 3D radiomics features were extracted in the manner of one-to-one correspondence. The 2D and 3D models were generated by four different machine-learning algorithms (regularised L1 logistic regression, decision tree, support vector machine and AdaBoost), and the receiver operating characteristic (ROC) curve was used to compare their predictive performance. A robustness analysis was performed according to baseline haematoma volume. RESULTS Each feature type of 2D and 3D modalities used for subsequent analyses had excellent consistency (mean ICC >0.9). Among the different machine-learning algorithms, pairwise comparison showed no significant difference in both the training (mean area under the ROC curve [AUC] 0.858 versus 0.802, all p>0.05) and validation datasets (mean AUC 0.725 versus 0.678, all p>0.05), and the 10-fold cross-validation evaluation yielded similar results. The AUCs of the 2D and 3D models were comparable either in the binary or tertile volume analysis (all p>0.5). CONCLUSION NCCT-derived 2D radiomics features exhibited acceptable and similar performance to the 3D features in predicting HE, and this comparability seemed unaffected by initial haematoma volume. The 2D signature may be preferred in future HE-related radiomic works given its compatibility with emergency condition of ICH.
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Affiliation(s)
- Q Chen
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - C Fu
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - X Qiu
- Department of Radiology, Qian Tang District of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - J He
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - T Zhao
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Q Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - X Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - H Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Luo WH, Li SJ, Wang XF. Response of cholangiocarcinoma with epigastric metastasis to lenvatinib plus sintilimab: A case report and review of literature. World J Gastrointest Oncol 2023; 15:2033-2040. [DOI: 10.4251/wjgo.v15.i11.2033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/15/2023] [Accepted: 09/28/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma (CCA) poses a significant clinical challenge due to its low radical resection rate and a propensity for high postoperative recurrence, resulting in a poor dismal. Although the combination of targeted therapy and immunotherapy has demonstrated notable efficacy in several solid tumors recently, however, its application in CCA remains underexplored and poorly documented.
CASE SUMMARY This case report describes a patient diagnosed with stage IV CCA, accompanied by liver and abdominal wall metastases, who underwent palliative surgery. Subsequently, the patient received two cycles of treatment combining lenvatinib with sintilimab, which resulted in a reduction in abdominal wall metastasis, while intrahepatic metastasis displayed progression. This unexpected observation illustrates different responses of intrahepatic and extrahepatic metastases to the same therapy.
CONCLUSION Lenvatinib combined with sintilimab shows promise as a potential treatment strategy for advanced CCA. Genetic testing for related driver and/or passenger mutations, as well as an analysis of tumor immune microenvironment analysis, is crucial for optimizing drug combinations and eventually addressing the issue of non-response in specific metastatic sites.
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Affiliation(s)
- Wen-Hui Luo
- The Second Department of Hepatobiliary Surgery, Yantai Yuhuangding Hospital, Yantai 264000, Shandong Province, China
| | - Shao-Jun Li
- The Second Department of Hepatobiliary Surgery, Yantai Yuhuangding Hospital, Yantai 264000, Shandong Province, China
| | - Xue-Feng Wang
- The Second Department of Hepatobiliary Surgery, Yantai Yuhuangding Hospital, Yantai 264000, Shandong Province, China
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10
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Wang C, Zhang Z, Dou Y, Liu Y, Chen B, Liu Q, Wang S. Development of clinical and magnetic resonance imaging-based radiomics nomograms for the differentiation of nodular fasciitis from soft tissue sarcoma. Acta Radiol 2023; 64:2578-2589. [PMID: 37593946 DOI: 10.1177/02841851231188473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
BACKGROUND Accurate differentiation of nodular fasciitis (NF) from soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis and treatment of patients. PURPOSE To develop and evaluate radiomics nomograms based on clinical factors and magnetic resonance imaging (MRI) for the preoperative differentiation of NF from STS. MATERIAL AND METHODS This retrospective study analyzed the MRI data of 27 patients with pathologically diagnosed NF and 58 patients with STS who were randomly divided into training (n = 62) and validation (n = 23) groups. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI. Radiomics analysis was applied to fat-suppressed T1-weighted (T1W-FS) images, fat-suppressed T2-weighted (T2W-FS) images, and contrast-enhanced T1-weighted (CE-T1W) images. The radiomics nomograms incorporating the radiomics signatures, clinical factors, and semantic features of MRI were developed. ROC curves and AUCs were carried out to compare the performance of the clinical factors, radiomics signatures, and clinical radiomics nomograms. RESULTS Tumor location, size, heterogeneous signal intensity on T2W-FS imaging, heterogeneous signal intensity on CE-T1W imaging, margin definitions on CE-T1W imaging, and septa were independent predictors for differentiating NF from STS (P < 0.05). The performance of the radiomics signatures based on T2W-FS imaging (AUC = 0.961) and CE-T1W imaging (AUC = 0.938) was better than that based on T1W-FS imaging (AUC = 0.833). The radiomics nomograms had AUCs of 0.949, which demonstrated good clinical utility and calibration. CONCLUSION The non-invasive clinical radiomics nomograms exhibited good performance in the differentiation of NF from STS, and they have clinical application in the preoperative diagnosis of diseases.
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Affiliation(s)
- Chunjie Wang
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
| | - Zhengyang Zhang
- Department of Radiology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, PR China
| | - Yanping Dou
- Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China
| | - Yajie Liu
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
| | - Bo Chen
- Department of Nuclear Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, PR China
| | - Qing Liu
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
| | - Shaowu Wang
- Department of Radiology, The Second Hospital of Dalian Medical University, Dalian, PR China
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11
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Haider SP, Qureshi AI, Jain A, Tharmaseelan H, Berson ER, Zeevi T, Werring DJ, Gross M, Mak A, Malhotra A, Sansing LH, Falcone GJ, Sheth KN, Payabvash S. Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers. Front Neurosci 2023; 17:1225342. [PMID: 37655013 PMCID: PMC10467422 DOI: 10.3389/fnins.2023.1225342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/10/2023] [Indexed: 09/02/2023] Open
Abstract
Objective To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH). Methods Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance. Results A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance. Conclusion Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.
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Affiliation(s)
- Stefan P. Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Department of Otorhinolaryngology, University Hospital of Ludwig-Maximilians-Universität München, Munich, Germany
| | - Adnan I. Qureshi
- Zeenat Qureshi Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, United States
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Elisa R. Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - David J. Werring
- Stroke Research Centre, University College London, Queen Square Institute of Neurology, London, United Kingdom
| | - Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Adrian Mak
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Lauren H. Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Guido J. Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Kevin N. Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
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Sotoudeh H, Rezaei A, Godwin R, Prattipati V, Singhal A, Sotoudeh M, Tanwar M. Radiomics Outperforms Clinical and Radiologic Signs in Predicting Spontaneous Basal Ganglia Hematoma Expansion: A Pilot Study. Cureus 2023; 15:e37162. [PMID: 37153238 PMCID: PMC10162352 DOI: 10.7759/cureus.37162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2023] [Indexed: 04/09/2023] Open
Abstract
Prediction of the hematoma expansion (HE) of spontaneous basal ganglia hematoma (SBH) from the first non-contrast CT can result in better management, which has the potential of improving outcomes. This study has been designed to compare the performance of "Radiomics analysis," "radiology signs," and "clinical-laboratory data" for this task. We retrospectively reviewed the electronic medical records for clinical, demographic, and laboratory data in patients with SBH. CT images were reviewed for the presence of radiologic signs, including black-hole, blend, swirl, satellite, and island signs. Radiomic features from the SBH on the first brain CT were extracted, and the most predictive features were selected. Different machine learning models were developed based on clinical, laboratory, and radiology signs and selected Radiomic features to predict hematoma expansion (HE). The dataset used for this analysis included 116 patients with SBH. Among different models and different thresholds to define hematoma expansion (10%, 20%, 25%, 33%, 40%, and 50% volume enlargement thresholds), the Random Forest based on 10 selected Radiomic features achieved the best performance (for 25% hematoma enlargement) with an area under the curve (AUC) of 0.9 on the training dataset and 0.89 on the test dataset. The models based on clinical-laboratory and radiology signs had low performance (AUCs about 0.5-0.6).
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13
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Yu L, Zhao M, Lin Y, Zeng J, He Q, Zheng Y, Ma K, Lin F, Kang D. Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study. Brain Sci 2023; 13:brainsci13040608. [PMID: 37190573 DOI: 10.3390/brainsci13040608] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/19/2023] [Accepted: 03/24/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Hematoma expansion (HE) is a significant predictor of poor outcomes in patients with intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) markers in ICH are promising predictors of HE. We aimed to determine the association of the NCCT markers with HE by using different temporal HE definitions. METHODS We utilized Risa-MIS-ICH trial data (risk stratification and minimally invasive surgery in acute intracerebral hemorrhage). We defined four HE types based on the time to baseline CT (BCT) and the time to follow-up CT (FCT). Hematoma volume was measured by software with a semi-automatic edge detection tool. HE was defined as a follow-up CT hematoma volume increase of >6 mL or a 33% hematoma volume increase relative to the baseline CT. Multivariable regression analyses were used to determine the HE parameters. The prediction potential of indicators for HE was evaluated using receiver-operating characteristic analysis. RESULTS The study enrolled 158 patients in total. The time to baseline CT was independently associated with HE in one type (odds ratio (OR) 0.234, 95% confidence interval (CI) 0.077-0.712, p = 0.011), and the blend sign was independently associated with HE in two types (OR, 6.203-6.985, both p < 0.05). Heterogeneous density was independently associated with HE in all types (OR, 6.465-88.445, all p < 0.05) and was the optimal type for prediction, with an area under the curve of 0.674 (p = 0.004), a sensitivity of 38.9%, and specificity of 96.0%. CONCLUSION In specific subtypes, the time to baseline CT, blend sign, and heterogeneous density were independently associated with HE. The association between NCCT markers and HE is influenced by the temporal definition of HE. Heterogeneous density is a stable and robust predictor of HE in different subtypes of hematoma expansion.
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Affiliation(s)
- Lianghong Yu
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Institute for Brain Disorders and Brain Science, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Provincial Clinical Research Center for Neurological Diseases, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Mingpei Zhao
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Yuanxiang Lin
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Institute for Brain Disorders and Brain Science, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Provincial Clinical Research Center for Neurological Diseases, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Jiateng Zeng
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Qiu He
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Yan Zheng
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Ke Ma
- Clinical Research and Translation Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Fuxin Lin
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Institute for Brain Disorders and Brain Science, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Provincial Clinical Research Center for Neurological Diseases, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Clinical Research and Translation Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Dezhi Kang
- Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Department of Neurosurgery, Binhai Branch of National Regional Medical Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Institute for Brain Disorders and Brain Science, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Fujian Provincial Clinical Research Center for Neurological Diseases, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
- Clinical Research and Translation Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
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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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15
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Sohn B, Won SY. Quality assessment of stroke radiomics studies: Promoting clinical application. Eur J Radiol 2023; 161:110752. [PMID: 36878154 DOI: 10.1016/j.ejrad.2023.110752] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 03/06/2023]
Abstract
PURPOSE To evaluate the quality of radiomics studies on stroke using a radiomics quality score (RQS), Minimum Information for Medial AI reporting (MINIMAR) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to promote clinical application. METHODS PubMed MEDLINE and Embase were searched to identify radiomics studies on stroke. Of 464 articles, 52 relevant original research articles were included. The RQS, MINIMAR and TRIPOD were scored to evaluate the quality of the studies by neuroradiologists. RESULTS Only four studies (7.7 %) performed external validation. The mean RQS was 3.2 of 36 (8.9 %), and the basic adherence rate was 24.9 %. The adherence rate was low for conducting phantom study (1.9 %), stating comparison to 'gold standard' (1.9 %), offering potential clinical utility (13.5 %) and performing cost-effectiveness analysis (1.9 %). None of the studies performed a test-retest, stated biologic correlation, conducted prospective studies, or opened codes and data to the public, resulting in low RQS. The total MINIMAR adherence rate was 47.4 %. The overall adherence rate for TRIPOD was 54.6 %, with low scores for reporting the title (2.0 %), key elements of the study setting (6.1 %), and explaining the sample size (2.0 %). CONCLUSIONS The overall radiomics reporting quality and reporting of published radiomics studies on stoke was suboptimal. More thorough validation and open data are needed to increase clinical applicability of radiomics studies.
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Affiliation(s)
- Beomseok Sohn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - So Yeon Won
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Ren H, Song H, Wang J, Xiong H, Long B, Gong M, Liu J, He Z, Liu L, Jiang X, Li L, Li H, Cui S, Li Y. A clinical-radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study. Insights Imaging 2023; 14:52. [PMID: 36977913 PMCID: PMC10050271 DOI: 10.1186/s13244-023-01399-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 03/08/2023] [Indexed: 03/30/2023] Open
Abstract
OBJECTIVE To build a clinical-radiomics model based on noncontrast computed tomography images to identify the risk of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) following intravenous thrombolysis (IVT). MATERIALS AND METHODS A total of 517 consecutive patients with AIS were screened for inclusion. Datasets from six hospitals were randomly divided into a training cohort and an internal cohort with an 8:2 ratio. The dataset of the seventh hospital was used for an independent external verification. The best dimensionality reduction method to choose features and the best machine learning (ML) algorithm to develop a model were selected. Then, the clinical, radiomics and clinical-radiomics models were developed. Finally, the performance of the models was measured using the area under the receiver operating characteristic curve (AUC). RESULTS Of 517 from seven hospitals, 249 (48%) had HT. The best method for choosing features was recursive feature elimination, and the best ML algorithm to build models was extreme gradient boosting. In distinguishing patients with HT, the AUC of the clinical model was 0.898 (95% CI 0.873-0.921) in the internal validation cohort, and 0.911 (95% CI 0.891-0.928) in the external validation cohort; the AUC of radiomics model was 0.922 (95% CI 0.896-0.941) and 0.883 (95% CI 0.851-0.902), while the AUC of clinical-radiomics model was 0.950 (95% CI 0.925-0.967) and 0.942 (95% CI 0.927-0.958) respectively. CONCLUSION The proposed clinical-radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.
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Affiliation(s)
- Huanhuan Ren
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Haojie Song
- College of Computer and Information Science, Chongqing Normal University, No. 37, Middle University Town Road, Shapingba District, Chongqing, 400016, China
| | - Jingjie Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hua Xiong
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Bangyuan Long
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Meilin Gong
- Department of Radiology, Chongqing General Hospital, Chongqing, China
| | - Jiayang Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Zhanping He
- Department of Radiology, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, China
| | - Li Liu
- Department of Radiology, People's Hospital of Yubei District of Chongqing City, Chongqing, China
| | - Xili Jiang
- Department of Radiology, The Second People's Hospital of Hunan Province/Brain Hospital of Hunan Province, Changsha, China
| | - Lifeng Li
- Department of Radiology, Changsha Central Hospital (The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China), Changsha, China
| | - Hanjian Li
- Department of Radiology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Shaoguo Cui
- College of Computer and Information Science, Chongqing Normal University, No. 37, Middle University Town Road, Shapingba District, Chongqing, 400016, China.
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Wang J, Zhou L, Chen Y, Zhou H, Tan Y, Zhong W, Zhou Z. Prediction of short-term prognosis of patients with hypertensive intracerebral hemorrhage by radiomic-clinical nomogram. Front Neurol 2023; 14:1053846. [PMID: 36816560 PMCID: PMC9935706 DOI: 10.3389/fneur.2023.1053846] [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: 10/14/2022] [Accepted: 01/10/2023] [Indexed: 02/05/2023] Open
Abstract
Hypertensive intracerebral hemorrhage (HICH) is the most common type of spontaneous intracerebral hemorrhage in China which is associated with high mortality and disability. We sought to develop and validate a noncontrast computed tomography (NCCT)-based nomogram model to achieve short-term prognostic prediction for patients with HICH. We retrospectively studied 292 patients with HICH from two medical centers, and they were divided into training (n = 151), validation (n = 66), and testing cohorts (n = 75). Based on radiomics, univariate and multivariate, and logistic regression analyses, four models (black hole sign, clinical, radiomics score, and combined models) were established to predict the prognosis of patients with HICH 30 days after the onset. The results suggested that the combined model had the best predictive performance with the area under the receiver operating characteristic curve (AUC) of 0.821, 0.816, and 0.815 in the training, validation, and testing cohorts, respectively. In addition, a radiomics-clinical (R-C) nomogram was visualized. A calibration curve analysis showed that the R-C nomogram had satisfactory calibration in the three cohorts. A decision curve analysis demonstrated that the R-C nomogram was clinically valuable. Our results suggest that the R-C nomogram can accurately and reliably predict the short-term prognosis of patients with HICH and provide a useful evaluation for making individualized treatment plans.
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Affiliation(s)
- Jing Wang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lu Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China,Department of Radiology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuanyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongli Zhou
- Department of Radiology, Nanchong Central Hospital, Nanchong, Sichuan, China
| | - Yuanxin Tan
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weijia Zhong
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China,*Correspondence: Weijia Zhong ✉ ; ✉
| | - Zhiming Zhou
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China,Zhiming Zhou ✉
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18
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Song L, Zhou H, Guo T, Qiu X, Tang D, Zou L, Ye Y, Fu Y, Wang R, Wang L, Mao H, Yu Y. Predicting Hemorrhage Progression in Deep Intracerebral Hemorrhage: A Multicenter Retrospective Cohort Study. World Neurosurg 2023; 170:e387-e401. [PMID: 36371042 DOI: 10.1016/j.wneu.2022.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 11/05/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Hemorrhage progression in deep intracerebral hemorrhage (ICH) involves not only the growth of parenchymal hematoma but also an increase in intraventricular hemorrhage (IVH). The search for methods that predict both the increased risk of parenchymal hematoma and IVH growth is warranted. METHODS We conducted a retrospective cohort study at multiple centers. Participants with deep ICH were enrolled from January 2018 to December 2021. Prediction models based on logistic regression analysis included clinical as well as routine radiographic and radiomics variables, separately or in combination. The performance of each model was evaluated using discrimination measures (e.g., area under the curve [AUC]). Evaluation of clinical utility was performed using decision curve analysis (DCA). RESULTS Overall, 647 individuals across 4 stroke centers were included. A total of 429 (66%) patients from 3 centers were assigned to the primary cohort and 218 (34%) from another center were placed in the validation cohort. Multivariate analysis showed that the Glasgow Coma Scale score, baseline ICH volume, IVH, blend sign, and radiomics score were associated with hemorrhage progression in the primary cohort. The clinical-radiomics model (AUC = 0.852 and 0.835) improved the prediction performance of hemorrhage progression compared to the Noncontrast computed tomography signs model (AUC = 0.666 and 0.618) in both the primary and validation cohorts, with similar results in the decision curve analysis curves. CONCLUSIONS The clinical-radiomics model outperformed the routine Noncontrast computed tomography signs model in predicting the progression of deep ICH. The clinical benefit of screening patients using this model may assist in risk stratification.
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Affiliation(s)
- Lei Song
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hang Zhou
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Tingting Guo
- Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xiaoming Qiu
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Dongfang Tang
- Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Liwei Zou
- Department of Radiology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Yu Ye
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Yufei Fu
- Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi, China
| | - Rujia Wang
- Department of Radiology, Tangshan Gongren Hospital, Tangshan, China
| | - Longsheng Wang
- Department of Radiology, The Second Hospital of Anhui Medical University, Hefei, China
| | - Huaqing Mao
- School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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Jiang YW, Xu XJ, Wang R, Chen CM. Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis. Front Oncol 2023; 12:973104. [PMID: 36703784 PMCID: PMC9872032 DOI: 10.3389/fonc.2022.973104] [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: 06/20/2022] [Accepted: 12/20/2022] [Indexed: 01/11/2023] Open
Abstract
Background This meta-analysis aimed to assess the efficacy of radiomics using non-enhanced computed tomography (NCCT) for predicting hematoma expansion in patients with spontaneous intracerebral hemorrhage. Methods Throughout the inception of the project to April 11, 2022, a comprehensive search was conducted on PubMed, Embase, and Cochrane Central Register of Controlled Trials. The methodological quality of studies in this analysis was assessed by the radiomics quality scoring system (RQS). A meta-analysis of radiomic studies based on NCCT for predicting hematoma expansion in patients with intracerebral hemorrhage was performed. The efficacy of the radiomics approach and non-contrast CT markers was compared using network meta-analysis (NMA). Results Ten articles comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using radiomics. Based on the included studies, the mean RQS was 14.4. The AUC value (95% confidence interval) of the radiomics model was 0.80 (0.76-0.83). Five articles comprising 846 patients were included in the NMA. The results synthesized according to Bayesian NMA revealed that the predictive ability of the radiomics model outperformed most of the NCCT biomarkers. Conclusions The NCCT-based radiomics approach has the potential to predict hematoma expansion. Compared to NCCT biomarkers, we recommend a radiomics approach. Standardization of the radiomics approach is required for further clinical implementation. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=324034, identifier [CRD42022324034].
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A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral Hemorrhage. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9751988. [DOI: 10.1155/2022/9751988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
Purpose. To develop and validate a clinical-radiomics nomogram based on clinical risk factors and CT radiomics feature to predict hypertensive intracerebral hemorrhage (HICH) prognosis. Methods. A total of 195 patients with HICH treated in our hospital from January 2018 to January 2022 were retrospectively enrolled and randomly divided into two cohorts for training (n = 138) and validation (n = 57) according to the ratio of 7 : 3. All CT radiomics features were extracted from intrahematomal, perihematomal, and combined intra- and perihematomal regions by using free open-source software called 3D slicer. The least absolute shrinkage and selection operator method was used to select the optimal radiomics features, and the radiomics score (Rad-score) was calculated. The relationship between Rad-score, clinical risk factors, and the HICH prognosis was analyzed by univariate and multivariate logistic regression analyses, and the clinical-radiomics nomogram was built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the clinical-radiomics nomogram in predicting the prognosis of HICH. Results. A total of 1702 radiomics features were extracted from the CT images of each patient for analysis. By univariate and stepwise multivariate logistic regression analyses, age, sex, RBC, serum glucose, D-dimer level, hematoma volume, and midline shift were clinical risk factors for the prognosis of HICH. Rad-score and clinical risk factors developed the clinical-radiomics nomogram. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.95, 95% confidence interval (CI), 0.92 to 0.98) and the validation cohort (AUC = 0.90, 95% CI, 0.82 to 0.98). The calibration curve indicated that the clinical-radiomics nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice. Conclusions. The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.
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Wu TC, Liu YL, Chen JH, Zhang Y, Chen TY, Ko CC, Su MY. The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage. Diagnostics (Basel) 2022; 12:diagnostics12112755. [PMID: 36428815 PMCID: PMC9689620 DOI: 10.3390/diagnostics12112755] [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: 09/19/2022] [Revised: 10/29/2022] [Accepted: 11/08/2022] [Indexed: 11/12/2022] Open
Abstract
Background: Among patients undergoing head computed tomography (CT) scans within 3 h of spontaneous intracerebral hemorrhage (sICH), 28% to 38% have hematoma expansion (HE) on follow-up CT. This study aimed to predict HE using radiomics analysis and investigate the impact of intraventricular hemorrhage (IVH) compared with the conventional approach based on intraparenchymal hemorrhage (IPH) alone. Methods: This retrospective study enrolled 127 patients with baseline and follow-up non-contrast CT (NCCT) within 4~72 h of sICH. IPH and IVH were outlined separately for performing radiomics analysis. HE was defined as an absolute hematoma growth > 6 mL or percentage growth > 33% of either IPH (HEP) or a combination of IPH and IVH (HEP+V) at follow-up. Radiomic features were extracted using PyRadiomics, and then the support vector machine (SVM) was used to build the classification model. For each case, a radiomics score was generated to indicate the probability of HE. Results: There were 57 (44.9%) HEP and 70 (55.1%) non-HEP based on IPH alone, and 58 (45.7%) HEP+V and 69 (54.3%) non-HEP+V based on IPH + IVH. The majority (>94%) of HE patients had poor early outcomes (death or modified Rankin Scale > 3 at discharge). The radiomics model built using baseline IPH to predict HEP (RMP) showed 76.4% accuracy and 0.73 area under the ROC curve (AUC). The other model using IPH + IVH to predict HEP+V (RMP+V) had higher accuracy (81.9%) with AUC = 0.80, and this model could predict poor outcomes. The sensitivity/specificity of RMP and RMP+V for HE prediction were 71.9%/80.0% and 79.3%/84.1%, respectively. Conclusion: The proposed radiomics approach with additional IVH information can improve the accuracy in prediction of HE, which is associated with poor clinical outcomes. A reliable radiomics model may provide a robust tool to help manage ICH patients and to enroll high-risk ICH cases into anti-expansion or neuroprotection drug trials.
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Affiliation(s)
- Te-Chang Wu
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan 71004, Taiwan
- Department of Medical Sciences Industry, Chang Jung Christian University, Tainan 71101, Taiwan
- Correspondence: (T.-C.W.); (J.-H.C.); Tel.: +886-62812811 (ext. 53752) (T.-C.W.)
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA 92521, USA
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA 92521, USA
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung 84001, Taiwan
- Correspondence: (T.-C.W.); (J.-H.C.); Tel.: +886-62812811 (ext. 53752) (T.-C.W.)
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA 92521, USA
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Tai-Yuan Chen
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan 71004, Taiwan
- Graduate Institute of Medical Sciences, Chang Jung Christian University, Tainan 71101, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan 71004, Taiwan
- Center of General Education, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA 92521, USA
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Xu W, Guo H, Li H, Dai Q, Song K, Li F, Zhou J, Yao J, Wang Z, Liu X. A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage. Front Neurol 2022; 13:974183. [DOI: 10.3389/fneur.2022.974183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background and purposeHematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with spontaneous ICH and to develop a nomogram to assess the risk of early HE.Materials and methodsWe retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 h after onset and follow-up CT within 24 h after initial NCCT, between January 2015 and December 2021. Using the LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n = 235) and independently verified in the test cohort (n = 153). The Akaike information criterion (AIC) and the likelihood ratio test (LRT) were used for comparing the goodness of fit of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance.ResultsThe combined model (AIC = 202.599, χ2 = 80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC = 232.263, χ2 = 46.940), radiological model (AIC = 227.932, χ2 = 51.270), clinical-radiological model (AIC = 212.711, χ2 = 55.490) or radiomics model (AIC = 217.647, χ2 = 57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC = 0.900, sensitivity = 83.87%; AUC = 0.850, sensitivity = 80.10%, respectively).ConclusionThe NCCT-based model combining clinical characteristics, radiological signs, and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited satisfactory performance in stratifying HE risks.
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Zou J, Chen H, Liu C, Cai Z, Yang J, Zhang Y, Li S, Lin H, Tan M. Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage. Front Neurosci 2022; 16:942100. [PMID: 36033629 PMCID: PMC9400715 DOI: 10.3389/fnins.2022.942100] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 07/15/2022] [Indexed: 12/28/2022] Open
Abstract
Background Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients. Methods ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems. Conclusion This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.
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Affiliation(s)
- Jianyu Zou
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Huihuang Chen
- Department of Rehabilitation, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Cuiqing Liu
- Department of Nursing, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhenbin Cai
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jie Yang
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yunlong Zhang
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shaojin Li
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongsheng Lin
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
- *Correspondence: Hongsheng Lin,
| | - Minghui Tan
- Department of Orthopaedics, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Minghui Tan,
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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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Chen Y, Cao D, Guo ZQ, Ma XL, Ou YB, He Y, Chen X, Chen J. The Attenuation Value Within the Non-hypodense Region on Non-contrast Computed Tomography of Spontaneous Cerebral Hemorrhage: A Long-Neglected Predictor of Hematoma Expansion. Front Neurol 2022; 13:785670. [PMID: 35463149 PMCID: PMC9024072 DOI: 10.3389/fneur.2022.785670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/08/2022] [Indexed: 11/25/2022] Open
Abstract
Background and Purpose The ability of attenuation value of the non-hypodense region of hematoma in non-contrast computed tomography (NCCT) for predicting hematoma expansion (HE) remains unclear. Our purpose is to explore this relationship. Methods Two cohorts of patients were collected for analysis. The region where we measured hematoma attenuation values was limited to the non-hypodense region that was not adjacent to the normal brain tissue on NCCT. The critical attenuation value was derived via receiver operating characteristic (ROC) curve analysis in the derivation cohort and its predictive ability was validated in the validation cohort. Independent relationships between predictors, such as critical attenuation value of the non-hypodense region and HE were analyzed using the least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic analysis. Results The results showed that the attenuation value <64 Hounsfield units (HU) was independently associated with HE [odds ratio (OR), 4.118; 95% confidential interval (CI), 1.897–9.129, p < 0.001] and the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve (AUC) for predicting HE were 36.11%, 81.71%, 1.97, 0.78, 44.8%, 75.7%, and 0.589, respectively. Conclusions Our research explored and validated the relationship between the attenuation value of the non-hypodense region of hematoma and HE. The attenuation value < 64 HU was an appropriate indicator of early HE.
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Affiliation(s)
- Yong Chen
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Cao
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zheng-Qian Guo
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Ling Ma
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi-Bo Ou
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yue He
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Chen
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian Chen
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Jian Chen
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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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Liu J, Tao W, Wang Z, Chen X, Wu B, Liu M. Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning. Ther Adv Neurol Disord 2022; 14:17562864211060029. [PMID: 35173809 PMCID: PMC8842178 DOI: 10.1177/17562864211060029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/28/2021] [Indexed: 02/05/2023] Open
Abstract
Introduction: Patients with hemorrhagic transformation (HT) were reported to have
hemorrhage expansion. However, identification these patients with high risk
of hemorrhage expansion has not been well studied. Objectives: We aimed to develop a radiomic score to predict hemorrhage expansion after HT
among patients treated with thrombolysis/thrombectomy during acute phase of
ischemic stroke. Methods: A total of 104 patients with HT after reperfusion treatment from the West
China hospital, Sichuan University, were retrospectively included in this
study between 1 January 2012 and 31 December 2020. The preprocessed initial
non-contrast-enhanced computed tomography (NECT) imaging brain images were
used for radiomic feature extraction. A synthetic minority oversampling
technique (SMOTE) was applied to the original data set. The after-SMOTE data
set was randomly split into training and testing cohorts with an 8:2 ratio
by a stratified random sampling method. The least absolute shrinkage and
selection operator (LASSO) regression were applied to identify candidate
radiomic features and construct the radiomic score. The performance of the
score was evaluated by receiver operating characteristic (ROC) analysis and
a calibration curve. Decision curve analysis (DCA) was performed to evaluate
the clinical value of the model. Results: Among the 104 patients, 17 patients were identified with hemorrhage expansion
after HT detection. A total of 154 candidate predictors were extracted from
NECT images and five optimal features were ultimately included in the
development of the radiomic score by using logistic regression
machine-learning approach. The radiomic score showed good performance with
high area under the curves in both the training data set (0.91, sensitivity:
0.83; specificity: 0.89), test data set (0.87, sensitivity: 0.60;
specificity: 0.85), and original data set (0.82, sensitivity: 0.77;
specificity: 0.78). The calibration curve and DCA also indicated that there
was a high accuracy and clinical usefulness of the radiomic score for
hemorrhage expansion prediction after HT. Conclusions: The currently established NECT-based radiomic score is valuable in predicting
hemorrhage expansion after HT among patients treated with reperfusion
treatment after ischemic stroke, which may aid clinicians in determining
patients with HT who are most likely to benefit from anti-expansion
treatment.
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Affiliation(s)
- Junfeng Liu
- Center of Cerebrovascular Diseases, Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Wendan Tao
- Center of Cerebrovascular Diseases, Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhetao Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xinyue Chen
- CT collaboration, Siemens Healthineers,China
| | - Bo Wu
- Center of Cerebrovascular Diseases, Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Ming Liu
- Center of Cerebrovascular Diseases, Department of Neurology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu 610041, China
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Sotoudeh H, Sarrami AH, Roberson GH, Shafaat O, Sadaatpour Z, Rezaei A, Choudhary G, Singhal A, Sotoudeh E, Tanwar M. Emerging Applications of Radiomics in Neurological Disorders: A Review. Cureus 2021; 13:e20080. [PMID: 34987940 PMCID: PMC8719529 DOI: 10.7759/cureus.20080] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2021] [Indexed: 12/13/2022] Open
Abstract
Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.
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Affiliation(s)
- Houman Sotoudeh
- Radiology, University of Alabama at Birmingham, Birmingham, USA
| | | | | | - Omid Shafaat
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Zahra Sadaatpour
- Radiology, University of Alabama at Birmingham School of Medicine, Birmingham, USA
| | - Ali Rezaei
- Radiology, University of Alabama at Birmingham, Birmingham, USA
| | | | - Aparna Singhal
- Radiology, University of Alabama at Birmingham School of Medicine, Birmingham, USA
| | | | - Manoj Tanwar
- Radiology, University of Alabama at Birmingham School of Medicine, Birmingham, USA
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Ultrasound-based radiomics score: a potential biomarker for the prediction of progression-free survival in ovarian epithelial cancer. Abdom Radiol (NY) 2021; 46:4936-4945. [PMID: 34120235 DOI: 10.1007/s00261-021-03163-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE More than 80% of patients with ovarian epithelial cancer (OEC) show complete remission after initial treatment but eventually experience recurrence of the disease. This study aimed to develop a radiomics signature to identify a new prognostic indicator based on preoperative ultrasound imaging. METHODS A total of 111 patients with OEC who underwent transvaginal ultrasound before surgery were included. Of these, 76 were divided into the training cohort and 35 into the test cohort. We defined the region of interest (ROI) of the tumor by manually drawing the tumor contour on the ultrasound image of the lesion. The radiomics features were extracted from ultrasound images. The radiomics score (Rad-Score) was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Combined with the ultrasound radiomics features, significant clinical variables were also used to establish predictive models for 5-year progression-free survival (PFS) prediction. The efficiency of the model was evaluated using the area under the curve (AUC). Kaplan-Meier analysis was used to evaluate the association between the Rad-Score and PFS. RESULTS The combined model was superior to the clinical and Rad-Score models in estimating 5-year PFS and achieved an AUC of 0.868 (95%CI 0.766-0.971) in the training cohort. The Rad-Score was negatively correlated with prognosis in the training and test cohorts. CONCLUSIONS The combined model that incorporated both clinical parameters and ultrasound radiomics features achieved a good prognosis in patients with OEC, which might aid clinical decision-making.
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Haider SP, Qureshi AI, Jain A, Tharmaseelan H, Berson ER, Zeevi T, Majidi S, Filippi CG, Iseke S, Gross M, Acosta JN, Malhotra A, Kim JA, Sansing LH, Falcone GJ, Sheth KN, Payabvash S. Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population. Eur J Neurol 2021; 28:2989-3000. [PMID: 34189814 DOI: 10.1111/ene.15000] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/24/2021] [Accepted: 06/27/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT). METHODS We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume. RESULTS In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts. CONCLUSIONS Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.
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Affiliation(s)
- Stefan P Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.,Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany
| | - Adnan I Qureshi
- Zeenat Qureshi Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, USA
| | - Abhi Jain
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hishan Tharmaseelan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Elisa R Berson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Shahram Majidi
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Simon Iseke
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Moritz Gross
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Julian N Acosta
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jennifer A Kim
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Lauren H Sansing
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
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Li Q, Dong F, Wang Q, Xu F, Zhang M. A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features. Eur Radiol 2021; 31:9131-9138. [PMID: 34109487 DOI: 10.1007/s00330-021-08061-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/17/2021] [Accepted: 05/07/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To predict early intracerebral haemorrhage expansion (HE) by comprehensive evaluation of commonly used noncontrast computed tomography (NCCT) features. METHODS Two hundred eighty-eight patients who had a spontaneous intracerebral haemorrhage (ICH) were included. All of the patients had undergone baseline NCCT within 6 h after ICH symptom onset. Ten NCCT features were extracted. Univariate analysis and multivariable logistic regression analysis were used to select the features. Using the finally selected features, a logistic regression model was built with a training cohort (n = 202) and subsequently validated in an independent test cohort (n = 86). Additionally, stratification analysis was performed in cases with and without anticoagulant therapy. RESULTS HE was found in 78 patients (27.1%). The blend sign and black hole sign were finally selected. The logistic regression model built with the two features exhibited accuracies of 76.7% and 75.6%, specificities of 98.6% and 98.4%, and positive predictive values (PPVs) of 83.3% and 75.0% for the training and test cohorts, respectively. The model also showed specificities of 100% and 98.5% and PPVs of 100% and 76.9% for the anticoagulant and non-anticoagulant drug use groups, respectively. These performances were better than those of each of the separate features. CONCLUSIONS By comprehensive evaluation, the model comprising the blend sign and black hole sign showed good performance for predicting early intracerebral haemorrhage expansion, particularly for high specificity and PPV, regardless of the anticoagulant status. KEY POINTS • Early identification of patients who are more likely to have haematoma expansion is important for therapeutic intervention. • Many radiological features have been reported to correlate with intracerebral haemorrhage expansion. • By integrating only the blend sign and black hole sign, the logistic regression model showed good performance for predicting early intracerebral haemorrhage expansion.
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Affiliation(s)
- Qian Li
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Fei Dong
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
| | - Qiyuan Wang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Fangfang Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
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32
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Zhan C, Chen Q, Zhang M, Xiang Y, Chen J, Zhu D, Chen C, Xia T, Yang Y. Radiomics for intracerebral hemorrhage: are all small hematomas benign? Br J Radiol 2021; 94:20201047. [PMID: 33332987 DOI: 10.1259/bjr.20201047] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES We hypothesized that not all small hematomas are benign and that radiomics could predict hematoma expansion (HE) and short-term outcomes in small hematomas. METHODS We analyzed 313 patients with small (<10 ml) intracerebral hemorrhage (ICH) who underwent baseline non-contrast CT within 6 h of symptom onset between September 2013 and February 2019. Poor outcome was defined as a Glasgow Outcome Scale score ≤3. A radiomic model and a clinical model were built using least absolute shrinkageand selection operator algorithm or multivariate analysis. A combined model that incorporated the developed radiomic score and clinical factors was then constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. RESULTS The addition of radiomics to clinical factors significantly improved the prediction performance of HE compared with the clinical model alone in both the training {AUC, 0.762 [95% CI (0.665-0.859)] versus AUC, 0.651 [95% CI (0.556-0.745)], p = 0.007} and test {AUC, 0.776 [95% CI (0.655-0.897) versus AUC, 0.631 [95% CI (0.451-0.810)], p = 0.001} cohorts. Moreover, the radiomic-based model achieved good discrimination ability of poor outcomes in the 3-10 ml group (AUCs 0.720 and 0.701). CONCLUSION Compared with clinical information alone, combined model had greater potential for discriminating between benign and malignant course in patients with small ICH, particularly 3-10 ml hematomas. ADVANCES IN KNOWLEDGE Radiomics can be used as a supplement to conventional medical imaging, improving clinical decision-making and facilitating personalized treatment in small ICH.
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Affiliation(s)
- Chenyi Zhan
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qian Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingyue Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yilan Xiang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jie Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Dongqin Zhu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Chao Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tianyi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Alwalid O, Long X, Xie M, Yang J, Cen C, Liu H, Han P. CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture. Front Neurol 2021; 12:619864. [PMID: 33692741 PMCID: PMC7937935 DOI: 10.3389/fneur.2021.619864] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/18/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms. Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89-0.95] and 0.86 [95% CI: 0.80-0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. -1.60 and 2.35 vs. -1.01 on training and test cohorts, respectively, p < 0.001). Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.
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Affiliation(s)
- Osamah Alwalid
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xi Long
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingfei Xie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jiehua Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Chunyuan Cen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | | | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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