A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone.

ABSTRACT:

Objectives

Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)-based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.

Methods

The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning-based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without an

SUBMITTER: Hirvasniemi J 

PROVIDER: S-EPMC8523397 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature