A single sequence MRI-based deep learning radiomics model in the diagnosis of early osteonecrosis of femoral head - Alkhatatbeh, Tariq; Alkhatatbeh, Ahmad; Li, Xiaohui; Wang, Wei (Frontiers in Bioengineering and Biotechnology, 2024)
Purpose: The objective of this study was to create and assess a Deep Learning-Based Radiomics model that could accurately predict early Femoral Head Osteonecrosis (ONFH). This model has the potential to be highly beneficial in the early stages of diagnosis and treatment planning. Methods: MRI scans from 150 patients in total (80 healthy, 70 necrotic) were used, and split into training and testing sets in a 7:3 ratio. Handcrafted as well as deep learning features were retrieved from Tesla 2 weighted (T2W1) MRI slices. After a rigorous selection process, these features were used to construct three models: a Radiomics-based (Rad-model), a Deep Learning-based (DL-model), and a Deep Learning-based Radiomics (DLR-model). The performance of these models in predicting early ONFH was evaluated by comparing them using the receiver operating characteristic (ROC) and decision curve analysis (DCA).Results: 1197 handcrafted radiomics and 512 DL features were extracted then processed; after the final selection: 15 features were used for the Rad-model, 12 features for the DL-model, and only 9 features were selected for the DLRmodel. The most effective algorithm that was used in all of the models was Logistic regression (LR). The Rad-model depicted good results outperforming the DL-model; AUC=0.944 (95%CI 0.862-1.000) and AUC=0.930 (95%CI 0.838-1.000) respectively. The DLR-model showed superior results to both Rad-model and the DL-model; AUC= 0.968 (95%CI 0.909-1.000); and a sensitivity of 0.95 and specificity of 0.920. The DCA showed that DLR had a greater net clinical benefit in detecting early ONFH.We created and tested a Deep Learning-Based Radiomics model that properly predicted early ONFH. This model has the capacity to significantly aid surgeons in detecting early ONFH and arranging prompt therapy.