Meeting Banner
Abstract #1910

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

Jinghua Wang1, Ming Chen2,3, Lili He2,4, 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%).

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here