Dataset on acute stroke risk stratification from CT angiographic radiomics
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
Affiliations
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.
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.
