Avery Emily W, Behland Jonas, Mak Adrian, Haider Stefan P, Zeevi Tal, Sanelli Pina C, Filippi Christopher G, Malhotra Ajay, Matouk Charles C, Griessenauer Christoph J, Zand Ramin, Hendrix Philipp, Abedi Vida, Falcone Guido J, Petersen Nils, Sansing Lauren H, Sheth Kevin N, Payabvash Seyedmehdi
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.
Data Brief. 2022 Aug 14;44:108542. doi: 10.1016/j.dib.2022.108542. eCollection 2022 Oct.
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.
随着高通量图像处理技术的进步以及医学大数据的日益可得，不断发展的放射组学领域为医学图像的定量分析打开了大门，以预测临床相关信息。放射组学已被证明有用的一个临床领域是中风神经影像学，在该领域中，快速治疗分诊对患者预后至关重要，自动化决策辅助工具具有重大临床影响的潜力。例如，最近的研究已应用从CT血管造影（CTA）图像中提取的放射组学特征和机器学习框架来促进急性中风的风险分层。我们在此提供方法指南和放射组学数据，以支持参考文献“用于前循环大血管闭塞性中风风险分层的CT血管造影放射组学特征”。数据从耶鲁纽黑文医院2014年1月1日至2020年10月31日的中风中心登记处以及盖辛格医疗中心2016年1月1日至2019年12月31日的中风中心登记处提取。它包括接受血栓切除术的大血管闭塞性中风患者入院时CTA扫描上前循环区域的详细放射组学特征。我们还提供了用于机器学习和特征选择算法的训练、优化、验证和外部测试的分析框架的方法细节。为了提高基于放射组学的分析的可行性和质量，以改善中风领域内外的患者护理，所提供的数据和方法支持可作为未来将放射组学算法应用于机器学习框架的研究的基线，并允许对本研究中提取的放射组学特征进行分析和利用。
