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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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Gupta R, Bilgin C, Jabal MS, Kandemirli S, Ghozy S, Kobeissi H, Kallmes DF. Quality Assessment of Radiomics Studies on Functional Outcomes After Acute Ischemic Stroke-A Systematic Review. World Neurosurg 2024; 183:164-171. [PMID: 38056625 DOI: 10.1016/j.wneu.2023.11.154] [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: 10/17/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVE Radiomics is a machine-learning method that extracts features from medical images. The objective of the present systematic review was to assess the quality of existing studies that use radiomics methods to predict functional outcomes in patients after acute ischemic stroke. METHODS Studies using radiomics-extracted features to predict functional outcomes among patients with acute ischemic stroke using the modified Rankin Scale were included. PubMed, Scopus, Web of Science, and Embase were screened using the terms "radiomics" and "texture" in combination with "stroke." Quality scores were calculated based on Radiomics Quality Score, the IBSI (Image Biomarkers Standardization Initiative), and the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2). RESULTS Fourteen studies were included. The median total Radiomics Quality Score was 14.5 (13-16) out of 36. Domains 1, 5, and 6 on protocol quality and stability of imaging and segmentation, level of evidence, and use of open science and data, respectively, were poor. Median IBSI score was 2.5 (1-5) out of 6. Few studies included bias-field correction algorithms, isovoxel resampling, skull stripping, or gray-level discretization. Of 14 studies, none received +6 points, 1 received +5 points, 5 received +4 points, 1 study received +3 points, 5 received +2 points, 2 received +1 points, and none received 0 points. As per the QUADAS-2, 6/14 (42.9%) studies had a risk of bias concern and 0/14 (0%) had applicability concern. CONCLUSIONS The quality of the included studies was low to moderate. With increasing use of radiomics, future studies should attempt to adhere to and report established radiomics quality guidelines.
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Affiliation(s)
- Rishabh Gupta
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA; University of Minnesota Medical School, Minneapolis, Minnesota, USA.
| | - Cem Bilgin
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohamed S Jabal
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Sedat Kandemirli
- Department of Radiology, University of Iowa, Iowa City, Iowa, USA
| | - Sherief Ghozy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Hassan Kobeissi
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA; Central Michigan University College of Medicine, Mount Pleasant, Michigan, USA
| | - David F Kallmes
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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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 2024:8465371241234545. [PMID: 38420881 DOI: 10.1177/08465371241234545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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 by the 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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4
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Avery EW, Abou-Karam A, Abi-Fadel S, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke. Diagnostics (Basel) 2024; 14:485. [PMID: 38472957 PMCID: PMC10930945 DOI: 10.3390/diagnostics14050485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics. METHODS We extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy. RESULTS We included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort. CONCLUSIONS Automated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.
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Affiliation(s)
- Emily W. Avery
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
| | - Anthony Abou-Karam
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
| | - Sandra Abi-Fadel
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
| | - Jonas Behland
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
- CLAIM—Charité Lab for Artificial Intelligence in Medicine, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Adrian Mak
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
- CLAIM—Charité Lab for Artificial Intelligence in Medicine, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Stefan P. Haider
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, 81377 Munich, Germany
| | - Tal Zeevi
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
| | - Pina C. Sanelli
- Section of Neuroradiology, Department of Radiology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Manhasset, NY 11030, USA
| | - Christopher G. Filippi
- Section of Neuroradiology, Department of Radiology, Tufts School of Medicine, Boston, MA 02111, USA
| | - Ajay Malhotra
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
| | - Charles C. Matouk
- Division of Neurovascular Surgery, Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Guido J. Falcone
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Nils Petersen
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Lauren H. Sansing
- Division of Stroke and Vascular Neurology, Department of Neurology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Kevin N. Sheth
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale School of Medicine, New Haven, CT 06520, USA
| | - Seyedmehdi Payabvash
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA; (E.W.A.); (A.M.)
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Kong J, Zhang D. Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis. Front Neurol 2024; 14:1335851. [PMID: 38229595 PMCID: PMC10789857 DOI: 10.3389/fneur.2023.1335851] [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: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
Background Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients. Methods We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients. Results Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS. Conclusion Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.
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Affiliation(s)
- Jinfen Kong
- Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China
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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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Yusuying S, Lu Y, Zhang S, Wang J, Chen J, Wang D, Lu J, Qi P. CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion. Front Neurol 2023; 14:1152730. [PMID: 37251225 PMCID: PMC10213392 DOI: 10.3389/fneur.2023.1152730] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/14/2023] [Indexed: 05/31/2023] Open
Abstract
Background and aims Secondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images. Materials and methods A total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (n = 42) and testing (n = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram. Results In the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort. Conclusion This nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.
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Affiliation(s)
- Shadamu Yusuying
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Yao Lu
- Beijing Hospital, National Center of Gerontology, Beijing Institute of Geriatrics, Beijing, China
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Shun Zhang
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junjie Wang
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Juan Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Daming Wang
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Jun Lu
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Peng Qi
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Avery EW, Behland J, Mak A, Haider SP, Zeevi T, Sanelli PC, Filippi CG, Malhotra A, Matouk CC, Griessenauer CJ, Zand R, Hendrix P, Abedi V, Falcone GJ, Petersen N, Sansing LH, Sheth KN, Payabvash S. Dataset on acute stroke risk stratification from CT angiographic radiomics. Data Brief 2022; 44:108542. [PMID: 36060820 PMCID: PMC9428796 DOI: 10.1016/j.dib.2022.108542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/02/2022] [Accepted: 08/10/2022] [Indexed: 01/05/2023] Open
Abstract
With advances in high-throughput image processing technologies and increasing availability of medical mega-data, the growing field of radiomics opened the door for quantitative analysis of medical images for prediction of clinically relevant information. One clinical area in which radiomics have proven useful is stroke neuroimaging, where rapid treatment triage is vital for patient outcomes and automated decision assistance tools have potential for significant clinical impact. Recent research, for example, has applied radiomics features extracted from CT angiography (CTA) images and a machine learning framework to facilitate risk-stratification in acute stroke. We here provide methodological guidelines and radiomics data supporting the referenced article "CT angiographic radiomics signature for risk-stratification in anterior large vessel occlusion stroke." The data were extracted from the stroke center registry at Yale New Haven Hospital between 1/1/2014 and 10/31/2020; and Geisinger Medical Center between 1/1/2016 and 12/31/2019. It includes detailed radiomics features of the anterior circulation territories on admission CTA scans in stroke patients with large vessel occlusion stroke who underwent thrombectomy. We also provide the methodological details of the analysis framework utilized for training, optimization, validation and external testing of the machine learning and feature selection algorithms. With the goal of advancing the feasibility and quality of radiomics-based analyses to improve patient care within and beyond the field of stroke, the provided data and methodological support can serve as a baseline for future studies applying radiomics algorithms to machine-learning frameworks, and allow for analysis and utilization of radiomics features extracted in this study.
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Affiliation(s)
- Emily W. Avery
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Jonas Behland
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Charitépl.1, Berlin 10117, Germany
| | - Adrian Mak
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
- CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Charitépl.1, Berlin 10117, Germany
| | - Stefan P. Haider
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
- Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Ziemssenstraße 1, München 80336, Germany
| | - Tal Zeevi
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Pina C. Sanelli
- Section of Neuroradiology, Department of Radiology, Northwell Health, 300 Community Dr, Manhasset, NY 11030, USA
| | - Christopher G. Filippi
- Section of Neuroradiology, Department of Radiology, Tufts School of Medicine, 1 Washington St, Boston, MA 02111, USA
| | - Ajay Malhotra
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Charles C. Matouk
- Division of Neurovascular Surgery, Department of Neurosurgery, Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Christoph J. Griessenauer
- Department of Neurosurgery, Geisinger Medical Center, 100N Academy Ave, Danville, PA 17822, USA
- Research Institute of Neurointervention, Paracelsus Medical University, Strubergasse 21, Salzburg 5020, Austria
- Department of Neurosurgery, Paracelsus Medical University, Strubergasse 21, Salzburg 5020, Austria
| | - Ramin Zand
- Department of Neurology, Geisinger Medical Center, 100N Academy Ave, Danville, PA 17822, USA
| | - Philipp Hendrix
- Department of Neurosurgery, Geisinger Medical Center, 100N Academy Ave, Danville, PA 17822, USA
- Department of Neurosurgery, Saarland University Medical Center, Kirrberger Str 100, Homburg 66421, Germany
| | - Vida Abedi
- Department of Molecular and Functional Genomics, Geisinger Medical Center, 100N Academy Ave, Danville, PA 17822, USA
- Biocomplexity Institute, Virginia Tech, 1015 Life Science Cir, Blacksburg, VA 24061, USA
| | - Guido J. Falcone
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Nils Petersen
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Lauren H. Sansing
- Division of Stroke and Vascular Neurology, Department of Neurology, Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Kevin N. Sheth
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
| | - Seyedmehdi Payabvash
- Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar St, New Haven, CT 06510, USA
- Corresponding author. @SamPayabvash
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