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A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone by Jukka Hirvasniemi; Stefan Klein; Sita Bierma-Zeinstra; Meike W. Vernooij; Dieuwke Schiphof; Edwin H. G. Oei is a Medicine article available to read on EtoBox.
What is A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone about?
## 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 and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). ## Results Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval
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- Author
- Jukka Hirvasniemi; Stefan Klein; Sita Bierma-Zeinstra; Meike W. Vernooij; Dieuwke Schiphof; Edwin H. G. Oei
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Medicine (Health Sciences)