CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke
Background and Purpose: As “time is brain” in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) st...
Main Authors: | , , , , , , , , , , , , , , , , , |
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Format: | Article |
Language: | English |
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Elsevier
2022-01-01
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Series: | NeuroImage: Clinical |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158222000997 |
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author | Emily W. Avery Jonas Behland Adrian Mak Stefan P. Haider Tal Zeevi Pina C. Sanelli Christopher G. Filippi Ajay Malhotra Charles C. Matouk Christoph J. Griessenauer Ramin Zand Philipp Hendrix Vida Abedi Guido J. Falcone Nils Petersen Lauren H. Sansing Kevin N. Sheth Seyedmehdi Payabvash |
author_facet | Emily W. Avery Jonas Behland Adrian Mak Stefan P. Haider Tal Zeevi Pina C. Sanelli Christopher G. Filippi Ajay Malhotra Charles C. Matouk Christoph J. Griessenauer Ramin Zand Philipp Hendrix Vida Abedi Guido J. Falcone Nils Petersen Lauren H. Sansing Kevin N. Sheth Seyedmehdi Payabvash |
author_sort | Emily W. Avery |
collection | DOAJ |
description | Background and Purpose: As “time is brain” in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics. Methods: We automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: “Radiomics”, “Radiomics + Treatment” (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), “Clinical + Treatment” (baseline clinical variables and treatment), and “Combined” (radiomics, treatment, and baseline clinical variables). Results: For discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction. Conclusion: Machine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool. |
first_indexed | 2024-12-12T11:08:49Z |
format | Article |
id | doaj.art-bb4bf1af3c8544edbf1615d58e844010 |
institution | Directory Open Access Journal |
issn | 2213-1582 |
language | English |
last_indexed | 2024-12-12T11:08:49Z |
publishDate | 2022-01-01 |
publisher | Elsevier |
record_format | Article |
series | NeuroImage: Clinical |
spelling | doaj.art-bb4bf1af3c8544edbf1615d58e8440102022-12-22T00:26:21ZengElsevierNeuroImage: Clinical2213-15822022-01-0134103034CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion strokeEmily W. Avery0Jonas Behland1Adrian Mak2Stefan P. Haider3Tal Zeevi4Pina C. Sanelli5Christopher G. Filippi6Ajay Malhotra7Charles C. Matouk8Christoph J. Griessenauer9Ramin Zand10Philipp Hendrix11Vida Abedi12Guido J. Falcone13Nils Petersen14Lauren H. Sansing15Kevin N. Sheth16Seyedmehdi Payabvash17Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United StatesSection of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States; CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, GermanySection of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States; CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, GermanySection of Neuroradiology, 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, GermanySection of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United StatesSection of Neuroradiology, Department of Radiology, Northwell Health, Manhasset, NY, United StatesSection of Neuroradiology, Department of Radiology, Tufts School of Medicine, Boston, MA, United StatesSection of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United StatesDivision of Neurovascular Surgery, Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, United StatesDepartment of Neurosurgery, Geisinger Medical Center, Danville, PA, United States; Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria; Department of Neurosurgery, Paracelsus Medical University, Salzburg, AustriaDepartment of Neurology, Geisinger, Danville, PA, United StatesDepartment of Neurosurgery, Geisinger Medical Center, Danville, PA, United States; Department of Neurosurgery, Saarland University Medical Center, Homburg, GermanyDepartment of Molecular and Functional Genomics, Geisinger, Danville, PA, United States; Biocomplexity Institute, Virginia Tech, Blacksburg, VA, USADivision of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United StatesDivision of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United StatesDivision of Stroke and Vascular Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United StatesDivision of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale University School of Medicine, New Haven, CT, United StatesSection of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States; Corresponding author at: Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, PO Box 208042, New Haven, CT 06519, United States.Background and Purpose: As “time is brain” in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics. Methods: We automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: “Radiomics”, “Radiomics + Treatment” (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), “Clinical + Treatment” (baseline clinical variables and treatment), and “Combined” (radiomics, treatment, and baseline clinical variables). Results: For discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction. Conclusion: Machine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.http://www.sciencedirect.com/science/article/pii/S2213158222000997RadiomicsStrokeLarge vessel occlusionCTAQuantitative imagingMechanical thrombectomy |
spellingShingle | Emily W. Avery Jonas Behland Adrian Mak Stefan P. Haider Tal Zeevi Pina C. Sanelli Christopher G. Filippi Ajay Malhotra Charles C. Matouk Christoph J. Griessenauer Ramin Zand Philipp Hendrix Vida Abedi Guido J. Falcone Nils Petersen Lauren H. Sansing Kevin N. Sheth Seyedmehdi Payabvash CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke NeuroImage: Clinical Radiomics Stroke Large vessel occlusion CTA Quantitative imaging Mechanical thrombectomy |
title | CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke |
title_full | CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke |
title_fullStr | CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke |
title_full_unstemmed | CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke |
title_short | CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke |
title_sort | ct angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke |
topic | Radiomics Stroke Large vessel occlusion CTA Quantitative imaging Mechanical thrombectomy |
url | http://www.sciencedirect.com/science/article/pii/S2213158222000997 |
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