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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number108542
JournalData in Brief
Volume44
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • General

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