Abstract #2281

Prediction of Knee Osteoarthritis using MRI-based Radiomic Features of the Subchondral Bone and Infrapatellar Fat Pad

Inês R. Campos^1,2,3, Rianne A. van der Heijden^1,4, Edwin H.G. Oei¹, Stefan Klein¹, Jaime S. Cardoso^2,5, and Jukka Hirvasniemi^1,6

¹Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands, ²Faculdade de Engenharia, Universidade do Porto, Porto, Portugal, ³Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal, ⁴Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ⁵Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (INESC TEC), Porto, Portugal, ⁶Department of Biomechanical Engineering, Delft University of Technology, Delft, Netherlands

Synopsis

Keywords: Osteoarthritis, MSK

Motivation: While radiomics has been applied to various MRI data to predict knee osteoarthritis (KOA) incidence, there is a lack of knowledge on the combination of radiomics features from different knee structures.

Goal(s): To assess the ability of MRI-based radiomic features extracted from automatically segmented femur, tibia, patella, and infrapatellar fat pad to predict KOA incidence.

Approach: 710 DESS MRIs were segmented using deep learning, trained with 30 manually delineated images. KOA incidence was predicted using Elastic Net, based on radiomic features from the four knee structures.

Results: The model combining features from the four knee structures resulted in a ROC AUC of 0.65.

Impact: While further research should be conducted to improve the accuracy of the developed radiomics pipeline in order to improve its applicability in clinical practice, radiomic features gathered from different knee structures are promising imaging biomarkers for early KOA prediction.

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