Schlereth M, Kleyer A, Utz J, Folle L, Bayat S, Fagni F, Minopoulou I, Tascilar K, Uderhardt S, Heimann T, Qiu J, Schett G, Breininger K, Simon D (2023)
Publication Type: Journal article
Publication year: 2023
Book Volume: 82
Pages Range: 758-759
DOI: 10.1136/annrheumdis-2023-eular.1028
Background
Rheumatoid Arthritis (RA) Magnetic Resonance Imaging (MRI) scoring system (RAMRIS) [1] is used to manually assess severity of disease activity and monitor treatment response, but it is dependent on observer variability and is time-consuming. Deep learning techniques have the potential to improve the reproducibility and efficiency of RAMRIS scoring by automating the analysis of hand MRI scans, however, there are limited data on an automated assessment approach.
Objectives
To investigate whether a deep neural network (DNN) can be trained to automatically detect erosion, synovitis, and oedema in RA patients using hand MRI scans and RAMRIS.
Methods
We used 1,5 Tesla hand MRI (Siemens Magnetom Vida and Aera) from the BARE BONE trial, a prospective, single-arm, interventional, open-label, phase 4 trial (EUDRACT 2018-001164-32) in which RA patients were treated with baricitinib (4 mg/day) for 48 weeks. One of the objectives of BARE BONE was to assess the effect of baricitinib on joint damage and synovial inflammation. Participants of BARE BONE received hand MRI at week 0, 24, 48 following a standardized scanning protocol [2]. All images were scored according to RAMRIS. DNNs were applied on coronal T1 (pre/post contrast enhancement) and T2 MR images. 3-D landmarks for each location for RAMRIS scoring were identified and a region of interest (ROI) around each landmark was extracted to train a DNN. Three separate DNNs were trained, one for each of the RAMRIS subcomponents (erosion, synovitis, oedema). Each DNN is based on a ResNet-3D [3] architecture that was pretrained on a video classification task [4]. The networks were trained to predict the severity scores of each disease characteristic into three classes ranging from 0 (no pathological change) to 2 (high disease burden). The performance of the DNNs was evaluated using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PR-AUC). Three-fold cross-validation were used and the network performance on a hold-out test set was evaluated.
Results
In total, we obtained 212 coronal MR images with both T1 and T2 weighting from 30 RA patients (24 woman/6 men, age 53.5±12.6 years, disease duration 4.3±4.4 years). The overall RAMRIS score decreased from 20.6 (CI95% 14.4 to 27.8) to 18.3 (CI95% 11.5 to 26.5) at week 48. For the evaluation of erosions and oedema, 23 landmarks respectively were used per hand, and 7 landmarks for synovitis. In total 4608 landmarks for erosion and oedema were available, and for synovitis 1152 landmarks. The AUROC for predicting erosions was 86±2% with a PR-AUC of 83±4%. For the prediction of oedema the AUROC was 78±14% and PR-AUC was 83%±10%. Despite a low number of ROI for synovitis scoring, the respective AUROC was 60±4% and PR-AUC was of 69±3%.
Conclusion
This proof-of-concept study demonstrated that fully automated extraction of synovitis, bone oedema, and erosion is feasible. In the future, our deep neural network approach may help to automatically assess MRI data of the hand in routine clinical practice and trials with high accuracy while keeping costs and human resources manageable.
APA:
Schlereth, M., Kleyer, A., Utz, J., Folle, L., Bayat, S., Fagni, F.,... Simon, D. (2023). POS0900 AUTOMATIC SCORING OF EROSION, SYNOVITIS AND BONE OEDEMA IN RHEUMATOID ARTHRITIS USING DEEP LEARNING ON HAND MAGNETIC RESONANCE IMAGING. Annals of the Rheumatic Diseases, 82, 758-759. https://doi.org/10.1136/annrheumdis-2023-eular.1028
MLA:
Schlereth, Maja, et al. "POS0900 AUTOMATIC SCORING OF EROSION, SYNOVITIS AND BONE OEDEMA IN RHEUMATOID ARTHRITIS USING DEEP LEARNING ON HAND MAGNETIC RESONANCE IMAGING." Annals of the Rheumatic Diseases 82 (2023): 758-759.
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