Abstract #1910

A machine learning model using T2-weighted FLAIR radiomics features to predict patient outcome in ICH

Jinghua Wang¹, Ming Chen^2,3, Lili He^2,4, Hailong Li², Vivek Khandwala¹, David Wang¹, Brady Williamson¹, Daniel Woo⁵, and Achala Vagal¹

¹Radiology, University of Cincinnati, Cincinnati, OH, United States, ²The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, ³Electrical Engineering and Computer Science, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States, ⁴Pediatrics, University of Cincinnati, Cincinnati, OH, United States, ⁵Neurology and Rehabilitation Medicine, University of Cincinnati, Cincinnati, OH, United States

Intracerebral hemorrhage (ICH) accounts for 10% - 30% of all strokes and is associated with high short-term mortality (≤50% at 3 month). There is a critical unmet need for an effective prognostic tool using imaging markers to identify patients at risk for poor outcome and thereby better facilitating treatments at individual level as well as tailoring personalized interventions and optimizing rehabilitation strategies. In this work, we developed a machine learning method using radiomics features derived from T₂-weighted FLAIR images to predict recovery outcome in patients with ICH at 3 months with a accuracy of 80.8% (95% confidence interval: 78.9%, 82.8%).

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