Development and validation of ultrasound-based radiomics deep learning model to identify bone erosion in rheumatoid arthritis.
Authors
Affiliations (7)
Affiliations (7)
- Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, 20# Chazhong Road, Fuzhou, 350005, Fujian, China.
- Department of Ultrasound, National Regional Medical Center, First Affiliated Hospital of Fujian Medical University Binhai Campus, Fuzhou, China.
- Department of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
- Department of Ultrasound, Fuqing City Hospital, Fujian Medical University, Fuzhou, China.
- Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, 20# Chazhong Road, Fuzhou, 350005, Fujian, China. [email protected].
- Department of Ultrasound, National Regional Medical Center, First Affiliated Hospital of Fujian Medical University Binhai Campus, Fuzhou, China. [email protected].
- Department of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China. [email protected].
Abstract
To develop and validate a deep learning radiomics fusion model (DLR) based on ultrasound (US) images to identify bone erosion in rheumatoid arthritis (RA) patients. A total of 432 patients with RA at two institutions were collected. Three hundred twelve patients from center 1 were randomly divided into a training set (N = 218) and an internal test set (N = 94) in a 7:3 ratio; meanwhile, 124 patients from center 2 were as an external test set. Radiomics (Rad) and deep learning (DL) features were extracted based on hand-crafted radiomics and deep transfer learning networks. The least absolute shrinkage and selection operator regression was employed to establish DLR fusion feature from the Rad and DL features. Subsequently, 10 machine learning algorithms were used to construct models and the final optimal model was selected. The performance of models was evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA). The diagnostic efficacy of sonographers was compared with and without the assistance of the optimal model. LR was chosen as the optimal algorithm for model construction account for superior performance (Rad/DL/DLR: area under the curve [AUC] = 0.906/0.974/0.979) in the training set. In the internal test set, DLR_LR as the final model had the highest AUC (AUC = 0.966), which was also validated in the external test set (AUC = 0.932). With the aid of DLR_LR model, the overall performance of both junior and senior sonographers improved significantly (P < 0.05), and there was no significant difference between the junior sonographer with DLR_LR model assistance and the senior sonographer without assistance (P > 0.05). DLR model based on US images is the best performer and is expected to become an important tool for identifying bone erosion in RA patients. Key Points • DLR model based on US images is the best performer in identifying BE in RA patients. • DLR model may assist the sonographers to improve the accuracy of BE evaluations.