Abstract #3135

Feature selection to facilitate surgical planning from MRI of Placenta Accreta Spectrum Disorder

Joanna Chappell¹, Nada Mufti^1,2, Hassna Irzan¹, Pat O’Brien³, George Attilakos³, Magda Sokolska⁴, Priya Narayanan³, Trevor Gaunt⁵, Paul Humpheries⁵, Premal Patel⁵, Elspeth Whitby⁶, Eric Jauniaux^2,3, J. Ciaran Hutchinson⁵, Neil Sebire⁵, David Atkinson⁷, Giles Kendall^2,4, Sebastien Ourselin ¹, Tom Vercauteren¹, Anna L David^2,3, and Andrew Melbourne¹

¹School of Biomedical Engineering and Imaging Sciences (BMEIS), Kings College London, London, United Kingdom, ²Elizabeth Garret Anderson Institute for Women’s Health, University College London, London, United Kingdom, ³University College London Hospital, London, United Kingdom, ⁴Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, United Kingdom, ⁵Great Ormond Street Hospital for Children, London, United Kingdom, ⁶Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom, ⁷Centre for Medical Imaging, University College London, London, United Kingdom

Synopsis

Keywords: Data Processing, PlacentaFeature Selection Models provide a ranking of pathological MRI markers able to predict the outcome of Placenta Accreta Spectrum Disorder, which could be used to aid in clinical decision-making and improve maternal outcome. The potential being to reduce the workload of radiologists by establishing the most clinically relevant pathological MRI markers that predict outcome. Our results found three pathological markers to have the highest ranking to the outcomes with an average accuracy of 75% using a Random Forest Selection Model and Boruta algorithm.

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