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Abstract #0445

Supervised shallow learning of 129Xe MRI texture features to predict response to Anti-IL-5 biologic therapy in severe asthma

Marrissa McIntosh1, Rachel Eddy1, Danielle Knipping2, Tamas Lindenmaier2, David McCormack3, Christopher Licskai3, Cory Yamashita3, and Grace Parraga4
1Department of Medical Biophysics, Robarts Research Institute, Western University, London, ON, Canada, 2Robarts Research Institute, Western University, London, ON, Canada, 3Division of Respirology, Department of Medicine, Western University, London, ON, Canada, 4Department of Medical Biophysics, Division of Respirology, Department of Medicine, Robarts Research Institute, Western University, London, ON, Canada

129Xe MRI ventilation images consist of embedded texture features that help explain abnormal ventilation heterogeneity. We postulated that such texture features may help predict severe asthma patient response to anti-IL-5 therapies. Therefore, we employed supervised shallow learning techniques to identify specific 129Xe MRI features that help predict anti-IL-5 responders. Texture analysis yielded features that were superior to clinical measurements in identifying severe asthma patients that responded to anti-IL-5 therapy after 28 days. These promising results suggest that texture analysis may help predict asthmatics more likely to respond, before treatment is initiated.

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