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

Diagnostics

Georgios C. Manikis¹  Katerina Nikiforaki¹  Kostas Marias¹  Aristeidis H. Zibis²  Ioannis Stathis³  Evangelia E. Vassalou³  George A. Kakkos³  Apostolos H. Karantanas³  Nikolas Matthaiou³  Michail E. Klontzas³  Konstantinos Spanakis³ 

[1] Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology (FORTH), 70013 Heraklion, Crete, Greece;Department of Anatomy, Medical School, University of Thessaly, 41334 Larissa, Greece;Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Crete, Greece;

关键词: hip;    avascular necrosis of bone;    osteoporosis;    machine learning;    artificial intelligence;    transient osteoporosis;   

DOI  :  10.3390/diagnostics11091686

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown