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Article

Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip

1
Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Crete, Greece
2
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), 70013 Heraklion, Crete, Greece
3
Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), 70013 Heraklion, Crete, Greece
4
Department of Radiology, School of Medicine, University of Crete, Voutes Campus, 71003 Heraklion, Crete, Greece
5
Department of Anatomy, Medical School, University of Thessaly, 41334 Larissa, Greece
6
Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 71004 Heraklion, Crete, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Sven Nebelung
Diagnostics 2021, 11(9), 1686; https://doi.org/10.3390/diagnostics11091686
Received: 15 August 2021 / Revised: 12 September 2021 / Accepted: 14 September 2021 / Published: 15 September 2021
(This article belongs to the Special Issue Advanced MRI Techniques for Musculoskeletal Imaging)
Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose of this study was to utilize MRI-based radiomics and machine learning (ML) for accurate differentiation between the two entities. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included. Femoral heads and necks with segmented radiomics features were extracted. Three ML classifiers (XGboost, CatBoost and SVM) using 38 relevant radiomics features were trained on 70% and validated on 30% of the dataset. ML performance was compared to two musculoskeletal radiologists, a general radiologist and two radiology residents. XGboost achieved the best performance with an area under the curve (AUC) of 93.7% (95% CI from 87.7 to 99.8%) among ML models. MSK radiologists achieved an AUC of 90.6% (95% CI from 86.7% to 94.5%) and 88.3% (95% CI from 84% to 92.7%), respectively, similar to residents. The general radiologist achieved an AUC of 84.5% (95% CI from 80% to 89%), significantly lower than of XGboost (p = 0.017). In conclusion, radiomics-based ML achieved a performance similar to MSK radiologists and significantly higher compared to general radiologists in differentiating between TOH and AVN. View Full-Text
Keywords: hip; avascular necrosis of bone; osteoporosis; machine learning; artificial intelligence; transient osteoporosis; radiomics; XGboost hip; avascular necrosis of bone; osteoporosis; machine learning; artificial intelligence; transient osteoporosis; radiomics; XGboost
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MDPI and ACS Style

Klontzas, M.E.; Manikis, G.C.; Nikiforaki, K.; Vassalou, E.E.; Spanakis, K.; Stathis, I.; Kakkos, G.A.; Matthaiou, N.; Zibis, A.H.; Marias, K.; Karantanas, A.H. Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip. Diagnostics 2021, 11, 1686. https://doi.org/10.3390/diagnostics11091686

AMA Style

Klontzas ME, Manikis GC, Nikiforaki K, Vassalou EE, Spanakis K, Stathis I, Kakkos GA, Matthaiou N, Zibis AH, Marias K, Karantanas AH. Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip. Diagnostics. 2021; 11(9):1686. https://doi.org/10.3390/diagnostics11091686

Chicago/Turabian Style

Klontzas, Michail E., Georgios C. Manikis, Katerina Nikiforaki, Evangelia E. Vassalou, Konstantinos Spanakis, Ioannis Stathis, George A. Kakkos, Nikolas Matthaiou, Aristeidis H. Zibis, Kostas Marias, and Apostolos H. Karantanas 2021. "Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip" Diagnostics 11, no. 9: 1686. https://doi.org/10.3390/diagnostics11091686

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