Op0292 classification of psoriatic arthritis, seronegative rheumatoid arthritis, and seropositive rheumatoid arthritis using deep learning on magnetic resonance imaging

BackgroundWhile MRI evaluation of joints has been primarily used to quantify inflammation at a cross-sectional and longitudinal level, less is known about the potential of MRI in distinguishing different patterns of inflammation in the various forms of arthritis.ObjectivesTo evaluate (i) whether deep learning using neural networks can be trained to distinguish between seropositive rheumatoid arthritis (RA+), seronegative RA (RA-), and psoriatic arthritis (PsA) based on structural inflammatory patterns on hand magnetic resonance imaging and (ii) to assess if psoriasis patients with subclinical inflammation fit into such patterns.MethodsResNet 3D [1] neural networks were trained to distinguish (i) RA+ vs. PsA, (ii) RA- vs. PsA and (iii) RA+ vs. RA- with respect to hand MRI data. Diagnosis of patients was determined using the following guidelines: ACR/EULAR 2010 [2] for RA and CASPAR [3] for PsA. Results from T1 coronal, T2 coronal, T1 coronal and axial fat suppressed contrast-enhanced (CE) and T2 fat suppressed axial sequences were used. The performance of such trained networks was analyzed by the area-under-the-receiver-operating-characteristic curve (AUROC) with and without imputation of demographic and clinical parameters (Figure 1A). Additionally, the trained networks were applied to psoriasis patients without clinical signs of PsA.Figure 1.(A) Neural network combining MR sequences with optional additional clinical data. The prediction for a single case is formed by averaging the prediction of all sequences and the clinical data. (B) Plot of the AUROC for increasing percentages (0.6 – 60%) of training data for the differentiation between RA+ and PsA by the neural network. The light blue area around the dark blue mean indicates the uncertainty measured using a 5-fold cross-validation.ResultsMRI scans from 649 patients (135 RA-, 190 RA+, 177 PsA, 147 psoriasis) were included (Table 1). The AUROC for differentiation between disease entities was 75% (SD 3%) for RA+ vs. PsA, 74% (SD 8%) for RA- vs. PsA, and 67% (6%) for RA+ vs. RA-. All MRI sequences were relevant for classification, however, when deleting CE sequences, the loss of performance was only marginal. The addition of patient-specific data to the networks did not provide significant improvements. Increasing amounts of training data demonstrated improved performance of the networks (Figure 1B). Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that PsA-like MRI pattern may be present early in the course of psoriatic disease.Table 1.Overview of demographic and clinical information.RA+RA-PsAPsoriasisTotal Number (N)649Number (N)190135177147Age (years), mean±SD56.9±12.660.5±10.356.3±12.049.6±13.8Sex (female/male)126/6493/4292/8571/76BMI (kg/m2), mean±SD26.6±10.527.6 ±9.329.1±11.326.7±6.9Disease duration (years), mean±SD2.6±4.91.3±2.30.8±2.34.2±5.1DAS28, mean±SD3.3±1.33.4±1.23.2±1.3-CRP (mg/L), mean±SD0.9±2.50.7±1.20.5±0.80.5±1.3HAQ, mean±SD0.8±0.60.9±0.80.6±0.60.3±0.4MedicationbDMARD88.46%83.87%81.32%35.01%csDMARD89.52%88.89%80.54%12.28%ConclusionDeep learning can be successfully applied to differentiate MRI inflammatory patterns related to RA+, RA-, and PsA. Early changes in psoriasis patients can be recognized by neural networks and are characterized by a pattern that allowed the networks to classify them as PsA.References[1]Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh 2018. Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6546–6555).[2]Aletaha D, Neogi T et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010 Sep;62(9):2569-81.[3]Helliwell PS, Taylor WJ. Classification and diagnostic criteria for psoriatic arthritis. Annals of the Rheumatic Diseases 2005;64:ii3-ii8.AcknowledgementsThe study was supported by the Deutsche Forschungsgemeinschaft (DFG-FOR2886 PANDORA and the CRC1181 Checkpoints for Resolution of Inflammation). Additional funding was received by the Bundesministerium für Bildung und Forschung (BMBF; project MASCARA), the ERC Synergy grant 4D Nanoscope, the IMI funded projects HIPPOCRATES and RTCure, the Emerging Fields Initiative MIRACLE of the Friedrich-Alexander-Universität Erlangen-Nürnberg and the Else Kröner-Memorial Scholarship (DS, no. 2019_EKMS.27). Furthermore, infrastructural and hardware support was provided by the d.hip Digital Health Innovation Platform.Disclosure of InterestsNone declared