Avascular necrosis and transient osteoporosis of the hip are conditions related to bone marrow edema of the proximal femur. Differentiation between the two entities can be extremely complicated, requiring significant MSK radiology expertise and the combination of imaging and clinical data. The aim of this study was to employ radiomics for the differentiation of the two entities with the use of MRI data. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included and femoral heads were manually segmented. Radiomics features were extracted with the use of PyRadiomics as implemented in 3D Slicer. Relevant radiomics features (n=38) were selected by removing collinearities and applying Boruta tree-based feature selection. An extreme gradient boosting (XGboost) machine learning model was trained on 70% and validated on 30% of the dataset and the results were compared to the performance of two fellowship-trained MSK radiologists. XGboost achieved an Area Under the Curve (AUC) of 93.7% (95%CI from 87.7 to 99.8%) whereas the 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. In conclusion, radiomics-based machine learning achieved excellent performance, similar to MSK radiologists, in differentiating between TOH and AVN.

Radiomics analysis enables accurate differential diagnosis between avascular necrosis and transient osteoporosis of the hip

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)