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HajiEsmailPoor Z, Kargar Z, Baradaran M, Shojaeshafiei F, Tabnak P, Mandalou L, Klontzas ME, Shahidi R. Prognostic value of CT scan-based radiomics in intracerebral hemorrhage patients: A systematic review and meta-analysis. Eur J Radiol 2024; 178:111652. [PMID: 39079323 DOI: 10.1016/j.ejrad.2024.111652] [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: 04/05/2024] [Revised: 07/04/2024] [Accepted: 07/25/2024] [Indexed: 08/18/2024]
Abstract
OBJECTIVES We conducted a systematic review and meta-analysis of current publications on the potential role of non-contrast-enhanced computed tomography (NCCT) radiomics as a prognostic indicator in patients with intracerebral hemorrhage (ICH). METHODS We systematically searched PubMed, EMBASE, and the Web of Science from inception until January 8, 2024. Studies with NCCT-based radiomics features for predicting the prognostic outcomes of ICH patients were included. We calculated the pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under curve (AUC) values. The radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and the quality assessment of diagnostic accuracy studies (QUADAS-2) were used for quality assessment. RESULTS Twenty-two studies were included. The pooled sensitivity, specificity, DOR, and AUC of radiomics models were 0.73, 0.78, 10.03, and 0.83, respectively, while on the combined radiomics models with other non-radiomics features were 0.80, 0.80, 16.28, and 0.86. Subgroup analysis showed that studies with the following covariates have a higher accuracy: single center, modified Rankin Scale (mRS) criteria for the ICH outcomes assessment, following patients for evaluation of ICH outcomes for more than a month, automatic segmentation, capturing the radiomics feature from the only intra-hematomal region, using PyRadiomic tool for features extraction, and using non-logistic regression for modeling. The quality of literature using QUADAS-2 and METRICS tools was good and was under-average using the RQS tool. No publication bias was detected. CONCLUSIONS Radiomics features showed moderate to high accuracy for predicting ICH prognostic outcomes. Although the QUADAS-2 and METRICS assessments indicated good quality, the radiomics pipeline quality was under-average. CLINICAL RELEVANCE NCCT-based radiomics features can provide information about the prognostic outcomes of ICH patients after patient admission. This study exploits the value of current evidence on NCCT-based radiomics methodology in the prediction of ICH prognosis.
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Affiliation(s)
| | - Zana Kargar
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mansoureh Baradaran
- Department of Radiology, Imam Ali Hospital, North Khorasan University of Medical Science, Bojnurd, Iran
| | | | - Peyman Tabnak
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Leila Mandalou
- Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Heraklion 71110, Crete, Greece; Department of Radiology, School of Medicine, University of Crete, Heraklion 71003, Crete, Greece
| | - Ramin Shahidi
- School of Medicine, Bushehr University of Medical Sciences, Bushehr, Iran
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Zhao X, Zhou B, Luo Y, Chen L, Zhu L, Chang S, Fang X, Yao Z. CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage. Eur Radiol 2024; 34:4417-4426. [PMID: 38127074 DOI: 10.1007/s00330-023-10505-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 10/18/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVES To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images. METHODS A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS). RESULTS A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively). CONCLUSIONS Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage. CLINICAL RELEVANCE STATEMENT The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage. KEY POINTS • Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.
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Affiliation(s)
- Xianjing Zhao
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Bijing Zhou
- Department of Radiology, Huashan Hospital, Fudan University, Jing'an District, 12 Middle Urumqi Road, Shanghai, 200040, China
| | - Yong Luo
- Department of Radiology, Luzhou People's Hospital, Luzhou, China
| | - Lei Chen
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lequn Zhu
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Shixin Chang
- Department of Radiology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangming Fang
- Department of Medical Imaging, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214023, Jiangsu, China.
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Jing'an District, 12 Middle Urumqi Road, Shanghai, 200040, China.
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Li J, Liang C, Dang J, Zhang Y, Chen H, Yan X, Liu Q. Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features. Front Surg 2024; 11:1344263. [PMID: 38389861 PMCID: PMC10882084 DOI: 10.3389/fsurg.2024.1344263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/19/2024] [Indexed: 02/24/2024] Open
Abstract
Hypertensive Intracerebral Hemorrhage (HICH) is one of the most common types of cerebral hemorrhage with a high mortality and disability rate. Currently, preoperative non-contrast computed tomography (NCCT) scanning-guided stereotactic hematoma removal has achieved good results in treating HICH, but some patients still have poor prognoses. This study collected relevant clinical and radiomic data by retrospectively collecting and analyzing 432 patients who underwent stereotactic hematoma removal for HICH from January 2017 to December 2020 at the Liuzhou Workers Hospital. The prognosis of patients after 90 days was judged by the modified Rankin Scale (mRS) scale and divided into the good prognosis group (mRS ≤ 3) and the poor prognosis group (mRS > 3). The 268 patients were randomly divided into training and test sets in the ratio of 8:2, with 214 patients in the training set and 54 patients in the test set. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomics features. They were combining clinical features and radiomic features to build a joint prediction model of the nomogram. The AUCs of the clinical model for predicting different prognoses of patients undergoing stereotactic HICH were 0.957 and 0.922 in the training and test sets, respectively, while the AUCs of the radiomics model were 0.932 and 0.770, respectively, and the AUCs of the combined prediction model for building a nomogram were 0.987 and 0.932, respectively. Compared with a single clinical or radiological model, the nomogram constructed by fusing clinical variables and radiomic features could better identify the prognosis of HICH patients undergoing stereotactic hematoma removal after 90 days.
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Affiliation(s)
- Jinwei Li
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Cong Liang
- Department of Pharmacy, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Junsun Dang
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Yang Zhang
- Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Hongmou Chen
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
| | - Xianlei Yan
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Quan Liu
- Department of Neurosurgery, Liuzhou Workers Hospital, Liuzhou, Guangxi, China
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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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Development and Validation of Machine Learning Prediction Model for Post-Rehabilitation Functional Outcome After Intracerebral Hemorrhage. INTERDISCIPLINARY NEUROSURGERY 2022. [DOI: 10.1016/j.inat.2022.101560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
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