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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