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Beaudoin AM, Ho JK, Lam A, Thijs V. Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment. Can Assoc Radiol J 2024; 75:549-557. [PMID: 38420881 DOI: 10.1177/08465371241234545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.
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Affiliation(s)
- Ann-Marie Beaudoin
- Université de Sherbrooke, Sherbrooke, QC, Canada
- The Florey, Heidelberg, VIC, Australia
| | - Jan Kee Ho
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | | | - Vincent Thijs
- The Florey, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
- Department of Medicine, University of Melbourne, Heidelberg, VIC, Australia
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Hu S, Hong J, Liu F, Wang Z, Li N, Wang S, Yang M, Fu J. An integrated nomogram combining clinical and radiomic features of hyperattenuated imaging markers to predict malignant cerebral edema following endovascular thrombectomy. Quant Imaging Med Surg 2024; 14:4936-4949. [PMID: 39022281 PMCID: PMC11250307 DOI: 10.21037/qims-24-99] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/07/2024] [Indexed: 07/20/2024]
Abstract
Background Malignant cerebral edema (MCE), a potential complication following endovascular thrombectomy (EVT) in the treatment of acute ischemic stroke (AIS), can result in significant disability and mortality. This study aimed to develop a nomogram model based on the hyperattenuated imaging marker (HIM), characterized by hyperattenuation on head noncontrast computed tomography (CT) immediately after thrombectomy, to predict MCE in patients receiving EVT. Methods In this retrospective cohort study, we selected 151 patients with anterior circulation large-vessel occlusion who received endovascular treatment. The patients were randomly allocated into training (n=121) and test (n=30) cohorts. HIM was used to extract radiomics characteristics. Conventional clinical and radiological features associated with MCE were also extracted. A model based on extreme gradient boosting (XGBoost) machine learning using fivefold cross-validation was employed to acquire radiomics and clinical features. Based on HIM, clinical and radiological signatures were used to construct a prediction nomogram for MCE. Subsequently, the signatures were merged through logistic regression (LR) analysis in order to create a comprehensive clinical radiomics nomogram. Results A total of 28 patients out of 151 (18.54%) developed MCE. The analysis of the receiver operating characteristic curve indicated an area under the curve (AUC) of 0.999 for the prediction of MCE in the training group and an AUC of 0.938 in the test group. The clinical and radiomics nomogram together showed the highest accuracy in predicting outcomes in both the training and test groups. Conclusions The novel nomogram, which combines clinical manifestations and imaging findings based on postinterventional HIM, may serve as a predictor for MCE in patients experiencing AIS after EVT.
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Affiliation(s)
- Sheng Hu
- Department of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Jiayi Hong
- Department of Neurology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Feifan Liu
- Department of Neurology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Ziwen Wang
- Department of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Na Li
- Department of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Shenghu Wang
- Department of Neurosurgery, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Mi Yang
- Department of Neurology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
| | - Jingjing Fu
- Department of Neurology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China
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Zhao Z, Zhang Y, Su J, Yang L, Pang L, Gao Y, Wang H. A comprehensive review for artificial intelligence on neuroimaging in rehabilitation of ischemic stroke. Front Neurol 2024; 15:1367854. [PMID: 38606275 PMCID: PMC11007047 DOI: 10.3389/fneur.2024.1367854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/08/2024] [Indexed: 04/13/2024] Open
Abstract
Stroke is the second leading cause of death worldwide, with ischemic stroke accounting for a significant proportion of morbidity and mortality among stroke patients. Ischemic stroke often causes disability and cognitive impairment in patients, which seriously affects the quality of life of patients. Therefore, how to predict the recovery of patients can provide support for clinical intervention in advance and improve the enthusiasm of patients for rehabilitation treatment. With the popularization of imaging technology, the diagnosis and treatment of ischemic stroke patients are often accompanied by a large number of imaging data. Through machine learning and Deep Learning, information from imaging data can be used more effectively. In this review, we discuss recent advances in neuroimaging, machine learning, and Deep Learning in the rehabilitation of ischemic stroke.
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Affiliation(s)
- Zijian Zhao
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yuanyuan Zhang
- Rehabilitation Center, ShengJing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Jiuhui Su
- Department of Orthopedics, Haicheng Bonesetting Hospital, Haicheng, Liaoning Province, China
| | - Lianbo Yang
- Department of Reparative and Reconstructive Surgery, The Second Hospital of Dalian Medical University, Dalian Liaoning Province, China
| | - Luhang Pang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Yingshan Gao
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - Hongbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China
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Zhang L, Wu J, Yu R, Xu R, Yang J, Fan Q, Wang D, Zhang W. Non-contrast CT radiomics and machine learning for outcomes prediction of patients with acute ischemic stroke receiving conventional treatment. Eur J Radiol 2023; 165:110959. [PMID: 37437435 DOI: 10.1016/j.ejrad.2023.110959] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/12/2023] [Accepted: 07/03/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE Accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. In this study, we developed prediction models based on non-contrast computed tomography (NCCT) radiomics and clinical features to predict the modified Rankin Scale (mRS) six months after hospital discharge. METHOD A two-center retrospective cohort of 240 AIS patients receiving conventional treatment was included. Radiomics features of the infarct area were extracted from baseline NCCT scans. We applied Kruskal-Wallis (KW) test and recursive feature elimination (RFE) to select features for developing clinical, radiomics, and fusion models (with clinical data and radiomics features), using support vector machine (SVM) algorithm. The prediction performance of the models was assessed by accuracy, sensitivity, specificity, F1 score, and receiver operating characteristic (ROC) curve. Shapley Additive exPlanations (SHAP) was applied to analyze the interpretability and predictor importance of the model. RESULTS A total of 1454 texture features were extracted from the NCCT images. In the test cohort, the ROC analysis showed that the radiomics model and the fusion model showed AUCs of 0.705 and 0.857, which outperformed the clinical model (0.643), with the fusion model exhibiting the best performance. Additionally, the accuracy and sensitivity of the fusion model were also the best among the models (84.8% and 93.8%, respectively). CONCLUSIONS The model based on NCCT radiomics and machine learning has high predictive efficiency for the prognosis of AIS patients receiving conventional treatment, which can be used to assist early personalized clinical therapy.
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Affiliation(s)
- Limin Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Wu
- Department of Radiology, the 958th Hospital, Southwest Hospital, Army Medical University, Chongqing, China
| | - Ruize Yu
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing 100025, China
| | - Ruoyu Xu
- Department of Neurology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jiawen Yang
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China
| | - Qianrui Fan
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing 100025, China
| | - Dawei Wang
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing 100025, China
| | - Wei Zhang
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Wen X, Xiao Y, Hu X, Chen J, Song F. Prediction of hemorrhagic transformation via pre-treatment CT radiomics in acute ischemic stroke patients receiving endovascular therapy. Br J Radiol 2023; 96:20220439. [PMID: 37086070 PMCID: PMC10321256 DOI: 10.1259/bjr.20220439] [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: 04/24/2022] [Revised: 03/01/2023] [Accepted: 03/28/2023] [Indexed: 04/23/2023] Open
Abstract
OBJECTIVE This study aimed to extract radiomics features (RFs) from pre-treatment CT scans in patients with acute ischemic stroke (AIS), and to establish a radiomics model to predict hemorrhagic transformation (HT) after endovascular therapy (EVT). METHODS A total of 105 patients who were diagnosed with AIS [with occlusion of the M1 segment of the middle cerebral artery (MCA) and/or internal carotid artery] and received EVT were enrolled. They were randomly divided into the development cohort (n = 73) and the validation cohort (n = 32). The clinicoradiological data of all patients, including pre-treatment cranial CT without contrast enhancement, CT perfusion, and CT angiography, were obtained. The MCA territory on pre-treatment CT images was segmented to extract RFs associated with HT after EVT. Then, a CT radiomics model based on the selected RFs was constructed to predict HT after EVT. RESULTS The sensitivity, specificity, and area under the curve of the CT radiomics model for predicting HT after EVT based on pre-treatment CT RFs was 0.806, 0.649, and 0.781 (95% confidence interval (CI): 0.675-0.886), respectively, in the development cohort. The sensitivity, specificity, and area under the curve in the validation cohort was 0.625, 0.875, and 0.797 (95% CI: 0.642-0.951), respectively. CONCLUSION CT radiomics analysis is a valuable tool for predicting HT in AIS patients receiving EVT. It may guide the selection of patients in practice and improve procedural safety and effectiveness. ADVANCES IN KNOWLEDGE Identification of the importance of pre-treatment CT radiomics in the prediction of HT in AIS patients after EVT.
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Affiliation(s)
- Xuehua Wen
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yanan Xiao
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xingfei Hu
- Department of Radiology, The First People’s Hospital of Daishan, Zhoushan, Zhejiang, China
| | - Junfa Chen
- Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Fahuan Song
- Department of Nuclear Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
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Ye W, Chen X, Li P, Tao Y, Wang Z, Gao C, Cheng J, Li F, Yi D, Wei Z, Yi D, Wu Y. OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features. Front Neurol 2023; 14:1158555. [PMID: 37416306 PMCID: PMC10321134 DOI: 10.3389/fneur.2023.1158555] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/22/2023] [Indexed: 07/08/2023] Open
Abstract
Background Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.
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Affiliation(s)
- Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Pengpeng Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yongjun Tao
- Department of Neurology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Jian Cheng
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Fang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dali Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
- Department of Health Education, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
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Sun Y, Zhuang Y, Zhu J, Song B, Wang H. Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke. Front Neurol 2023; 14:1132318. [PMID: 37251234 PMCID: PMC10213640 DOI: 10.3389/fneur.2023.1132318] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/11/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose To investigate texture analysis (TA) based on apparent diffusion coefficient (ADC) map in predicting acute ischemic stroke (AIS) prognosis and discriminating TA features in stroke subtypes. Methods This retrospective study included patients with AIS between January 2018 and April 2021. The patients were assigned to the favorable [modified Rankin Scale (mRS) score ≤ 2] and unfavorable (mRS score > 2) outcome groups. All patients underwent stroke subtyping according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification. The TA features were extracted from infarction lesions on the ADC map. The demographic characteristics, clinical characteristics, and texture features were used to construct prediction models with recurrent neural network (RNN). The receiver operating characteristic (ROC) curves were implemented to evaluate the performance of the predictive models. Results A total of 1,003 patients (682 male; mean age 65.90 ± 12.44) with AIS having documented the 90-day mRS score were identified, including 840 with favorable outcomes. In the validation set, the area under the curve (AUC) of the predictive model using only clinical characteristics achieved an AUC of 0.56, texture model 0.77, the model combining both clinical and texture features showed better with an AUC of 0.78. The texture feature profiles differed between large artery atherosclerosis (LAA) and small artery occlusion (SAO) subtypes (all p < 0.05). The AUC of combined prediction models for LAA and SAO subtypes was 0.80 and 0.81. Conclusion Texture analysis based on ADC map could be useful as an adjunctive tool for predicting ischemic stroke prognosis.
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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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Zhong JG, Shi L, Liu J, Cao F, Ma YQ, Zhang Y. Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance. Sci Rep 2023; 13:4846. [PMID: 36964192 PMCID: PMC10038986 DOI: 10.1038/s41598-023-31869-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T2-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.
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Affiliation(s)
- Jian-Guo Zhong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Shi
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Fang Cao
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yan-Qing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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MRI Radiomics and Predictive Models in Assessing Ischemic Stroke Outcome-A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050857. [PMID: 36900001 PMCID: PMC10000411 DOI: 10.3390/diagnostics13050857] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.
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Xu Q, Zhu Y, Zhang X, Kong D, Duan S, Guo L, Yin X, Jiang L, Liu Z, Yang W. Clinical features and FLAIR radiomics nomogram for predicting functional outcomes after thrombolysis in ischaemic stroke. Front Neurosci 2023; 17:1063391. [PMID: 36908776 PMCID: PMC9992187 DOI: 10.3389/fnins.2023.1063391] [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: 10/07/2022] [Accepted: 01/13/2023] [Indexed: 02/25/2023] Open
Abstract
Objective We explored whether radiomics features extracted from diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images can predict the clinical outcome of patients with acute ischaemic stroke. This study was conducted to investigate and validate a radiomics nomogram for predicting acute ischaemic stroke prognosis. Methods A total of 257 patients with acute ischaemic stroke from three clinical centres were retrospectively assessed from February 2019 to July 2022. According to the modified Rankin scale (mRS) at 3 months, the patients were divided into a favourable outcome group (mRS of 0-2) and an unfavourable outcome group (mRS of 3-6). The high-throughput features from the regions of interest (ROIs) within the radiologist-drawn contour by AK software were extracted. We used two feature selection methods, minimum redundancy and maximum (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO), to select the features. Three radiomics models (DWI, FLAIR, and DWI-FLAIR) were established. A radiomics nomogram with patient characteristics and radiomics signature was built using a multivariate logistic regression model. The performance of the nomogram was evaluated in the test and validation sets. Ultimately, decision curve analysis was implemented to assess the clinical value of the nomogram. Results The FLAIR, DWI, and DWI-FLAIR radiomics model exhibited good prediction performance, with area under the curve (AUCs) of 0.922 (95% CI: 0.876-0.968), 0.875 (95% CI: 0.815-0.935), and 0.895 (95% CI: 0.840-0.950). The radiomics nomogram with clinical characteristics including the overall cerebral small vessel disease (CSVD) burden score, hemorrhagic transformation (HT) and admission National Institutes of Health Stroke Scale score (NIHSS) score and the FLAIR Radscore presented good discriminatory potential in the training set (AUC = 0.94; 95% CI: 0.90-0.98) and test set (AUC = 0.94; 95% CI: 0.87-1), which was validated in the validation set 1 (AUC = 0.95; 95% CI: 0.88-1) and validation set 2 (AUC = 0.90; 95% CI: 0.768-1). In addition, it demonstrated good calibration, and decision curve analysis confirmed the clinical value of this nomogram. Conclusion This non-invasive clinical-FLIAR radiomics nomogram shows good performance in predicting ischaemic stroke prognosis after thrombolysis.
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Affiliation(s)
- Qingqing Xu
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, China
| | - Yan Zhu
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, China
| | - Xi Zhang
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, China
| | - Dan Kong
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, China
| | | | - Lili Guo
- Department of Radiology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huaian, China
| | - Xindao Yin
- Department of Radiology, Nanjing Medical University Affiliated Nanjing Hospital, Nanjing, China
| | - Liang Jiang
- Department of Radiology, Nanjing Medical University Affiliated Nanjing Hospital, Nanjing, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong Provincial People’s Hospital, Guangzhou, China
| | - Wanqun Yang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong Provincial People’s Hospital, Guangzhou, China
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12
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Understanding Acquired Brain Injury: A Review. Biomedicines 2022; 10:biomedicines10092167. [PMID: 36140268 PMCID: PMC9496189 DOI: 10.3390/biomedicines10092167] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/02/2022] [Accepted: 08/26/2022] [Indexed: 01/19/2023] Open
Abstract
Any type of brain injury that transpires post-birth is referred to as Acquired Brain Injury (ABI). In general, ABI does not result from congenital disorders, degenerative diseases, or by brain trauma at birth. Although the human brain is protected from the external world by layers of tissues and bone, floating in nutrient-rich cerebrospinal fluid (CSF); it remains susceptible to harm and impairment. Brain damage resulting from ABI leads to changes in the normal neuronal tissue activity and/or structure in one or multiple areas of the brain, which can often affect normal brain functions. Impairment sustained from an ABI can last anywhere from days to a lifetime depending on the severity of the injury; however, many patients face trouble integrating themselves back into the community due to possible psychological and physiological outcomes. In this review, we discuss ABI pathologies, their types, and cellular mechanisms and summarize the therapeutic approaches for a better understanding of the subject and to create awareness among the public.
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Li Z, Huang H, Wang C, Zhao Z, Ma W, Wang D, Mao H, Liu F, Yang Y, Pan W, Lu Z. DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer. Front Oncol 2022; 12:881341. [PMID: 36106114 PMCID: PMC9465298 DOI: 10.3389/fonc.2022.881341] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/01/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Low-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients. Methods One hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups. Results Four radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models. Conclusion DCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning.
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Affiliation(s)
- Zhiheng Li
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Huizhen Huang
- Shaoxing University School of Medicine, Shaoxing, China
| | - Chuchu Wang
- Shaoxing University School of Medicine, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Haijia Mao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Fang Liu
- Department of Pathology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Ye Yang
- Department of Pathology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Weihuo Pan
- Department of Colon and Rectal Surgery, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
- *Correspondence: Zengxin Lu,
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Sui H, Wu J, Zhou Q, Liu L, Lv Z, Zhang X, Yang H, Shen Y, Liao S, Shi F, Mo Z. Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke. Front Neurosci 2022; 16:912287. [PMID: 35937898 PMCID: PMC9355636 DOI: 10.3389/fnins.2022.912287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 06/24/2022] [Indexed: 12/12/2022] Open
Abstract
Background Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients. Purpose We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke. Materials and methods A total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation. Results In the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model. Conclusion The novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency. Summary Combining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay. Key Results Using a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).
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Affiliation(s)
- He Sui
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lin Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Zhongwen Lv
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xintan Zhang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Haibo Yang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Yi Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Zhanhao Mo
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Zhanhao Mo,
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Optimized Deconvolutional Algorithm-based CT Perfusion Imaging in Diagnosis of Acute Cerebral Infarction. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:8728468. [PMID: 35800236 PMCID: PMC9192278 DOI: 10.1155/2022/8728468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/26/2022]
Abstract
To apply deconvolution algorithm in computer tomography (CT) perfusion imaging of acute cerebral infarction (ACI), a convolutional neural network (CNN) algorithm was optimized first. RIU-Net was applied to segment CT image, and then equipped with SE module to enhance the feature extraction ability. Next, the BM3D algorithm, Dn CNN, and Cascaded CNN were compared for denoising effects. 80 patients with ACI were recruited and grouped for a retrospective analysis. The control group utilized the ordinary method, and the observation group utilized the algorithm proposed. The optimized model was utilized to extract the feature information of the patient's CT images. The results showed that after the SE module pooling was added to the RIU-Net network, the utilization rate of the key features was raised. The specificity of patients in observation group was 98.7%, the accuracy was 93.7%, and the detected number was (1.6 ± 0.2). The specificity of patients in the control group was 93.2%, the accuracy was 87.6%, and the detected number was (1.3 ± 0.4). Obviously, the observation group was superior to the control group in all respects (P < 0.05). In conclusion, the optimized model demonstrates superb capabilities in image denoising and image segmentation. It can accurately extract the information to diagnose ACI, which is suggested clinically.
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16
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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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Wen X, Shu Z, Li Y, Hu X, Gong X. Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion. BMC Med Imaging 2021; 21:147. [PMID: 34635087 PMCID: PMC8507216 DOI: 10.1186/s12880-021-00678-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). METHODS A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. RESULTS The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665-0.875) and 0.792 (95% CI: 0.633-0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701-0.916) and 0.833 (95% CI: 0.702-0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614-0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712-0.928). CONCLUSIONS The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.
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Affiliation(s)
- Xuehua Wen
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yumei Li
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xingfei Hu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangyang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China. .,Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Quan G, Ban R, Ren JL, Liu Y, Wang W, Dai S, Yuan T. FLAIR and ADC Image-Based Radiomics Features as Predictive Biomarkers of Unfavorable Outcome in Patients With Acute Ischemic Stroke. Front Neurosci 2021; 15:730879. [PMID: 34602971 PMCID: PMC8483716 DOI: 10.3389/fnins.2021.730879] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/13/2021] [Indexed: 11/14/2022] Open
Abstract
At present, it is still challenging to predict the clinical outcome of acute ischemic stroke (AIS). In this retrospective study, we explored whether radiomics features extracted from fluid-attenuated inversion recovery (FLAIR) and apparent diffusion coefficient (ADC) images can predict clinical outcome of patients with AIS. Patients with AIS were divided into a training (n = 110) and an external validation (n = 80) sets. A total of 753 radiomics features were extracted from each FLAIR and ADC image of the 190 patients. Interquartile range (IQR), Wilcoxon rank sum test, and least absolute shrinkage and selection operator (LASSO) were used to reduce the feature dimension. The six strongest radiomics features were related to an unfavorable outcome of AIS. A logistic regression analysis was employed for selection of potential predominating clinical and conventional magnetic resonance imaging (MRI) factors. Subsequently, we developed several models based on clinical and conventional MRI factors and radiomics features to predict the outcome of AIS patients. For predicting unfavorable outcome [modified Rankin scale (mRS) > 2] in the training set, the area under the receiver operating characteristic curve (AUC) of ADC radiomics model was 0.772, FLAIR radiomics model 0.731, ADC and FLAIR radiomics model 0.815, clinical model 0.791, and clinical and conventional MRI model 0.782. In the external validation set, the AUCs for the prediction with ADC radiomics model was 0.792, FLAIR radiomics model 0.707, ADC and FLAIR radiomics model 0.825, clinical model 0.763, and clinical and conventional MRI model 0.751. When adding radiomics features to the combined model, the AUCs for predicting unfavorable outcome in the training and external validation sets were 0.926 and 0.864, respectively. Our results indicate that the radiomics features extracted from FLAIR and ADC can be instrumental biomarkers to predict unfavorable clinical outcome of AIS and would additionally improve predictive performance when adding to combined model.
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Affiliation(s)
- Guanmin Quan
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ranran Ban
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | | | - Yawu Liu
- Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Weiwei Wang
- Department of Radiology, Handan Central Hospital, Handan, China
| | - Shipeng Dai
- Department of Radiology, Cangzhou City Hospital, Cangzhou, China
| | - Tao Yuan
- Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, China
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Zhang Y, Shu Z, Ye Q, Chen J, Zhong J, Jiang H, Wu C, Yu T, Pang P, Ma T, Lin C. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics. Front Oncol 2021; 11:633596. [PMID: 33747956 PMCID: PMC7968223 DOI: 10.3389/fonc.2021.633596] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/29/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives To systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model. Methods A total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models. Results Among nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model. Conclusions The combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.
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Affiliation(s)
- Yang Zhang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Qin Ye
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Junfa Chen
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Jianguo Zhong
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Hongyang Jiang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Cuiyun Wu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Taihen Yu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Tianshi Ma
- Department of Pathology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Chunmiao Lin
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
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