POS0958 RESPONSIVENESS OF CONVENTIONAL, SEMI-AUTOMATIC AND FULL-AUTOMATIC METHODS TO QUANTIFY MARROW BONE EDEMA LESIONS IN MRI OF AXIAL SPONDYLOARTHRITIS PATIENTS: A PILOT STUDY
Authors: C. Garrido-González, M. L. Ladehesa Pineda, J. L. Garrido-Castro, Z. Tabor, I. Kucybała, W. Wojciechowski, P. Zarco-Montejo, R. Almodovar, E. Collantes Estevez
Journal: Annals of the Rheumatic Diseases (2021)

Background: The presence or absence of marrow bone edema (MBE) in the sacroiliac joints (SIJ) is very important in the diagnosis of axial Spondyloarthritis (axSpA). The quantification of this lesion and its extension may be important to analyze responsiveness of the treatment. Several scoring systems have been proposed for MRI images of SIJ, some of them being observer dependent (Berlin, SPARCC). Others, works in a semi-automatically way, such as the s-SCAISS[1], which makes it possible to quantify the size of the lesion, based on the indication of the expert. Recently, methods like the KITs4R[2], based on a deep patch-based classification network. allow a fully automated detection and quantification of the MBE lesions. Objectives: To analyze responsiveness of several scores (observer independent, semiautomatic and full automatic) for quantify MBE in SIJ of axSpA patients. Methods: Two rheumatologists independently quantified SIJ images from axSpA patients by visual inspection methods (Berlin and SPARCC indexes) and a semiautomatic system (s-SCAISS) on a single semi-coronal MRI slide (STIR). Full semi-coronal MRI images (15 to 18 slices) were used for an automatic detection algorithm (KITs4R), where total MBE was calculated as sum of areas of MBE in each slice. Patients were assessed before TNF-α therapy (PRE) and 3 months later (POST). Spearman correlations was used to analyze relationship between variables, Wilcoxon signed-rank test for significant differences and Cohen’s d for calculating the effect size of improvement. Figure 1 shows processing of the MRI images: A) Area of MBE selected by the SCAISS, when the rheumatologist “click” on each MBE; B) Lesions detected automatically by KITs4R; C) Automated deep segmentation of bones and subchondral regions (with split into quadrants with central axes of joints also shown. D) Superposed A, B and C images. Results: 12 axSpA patients were recruited from the CASTRO cohort (42% female, age 46±11 years, disease duration 16±13 years, BMI 28±5). Results PRE and POST are shown in Table 1: mean values (sd), statistical significance (NS, not significant; *, p&lt;0.05; **, p&lt;0.01), and effect size. Activity indexes and CRP were lower. ASDAS and CRP shown significant differences and a large effect size. All MRI scores showed good responsiveness (ES medium-large, p&lt;0.01), specially KITs4R. Agreement between all MRI scores were high (r&gt;0.8;p&lt;0.01). Between semiautomatic and automatic methods, this agreement was also excellent (r=0.92;p&lt;0.001). Correlation in improvements (reductions in scores) were also significant between all MRI scores (r&gt;0.7;p&lt;0.05). PRE POST Sign ES BASDAI 6.12 (2.45) 4.96 (2.74) N.S. 0.44-Small ASDAS 3.61 (1.04) 2.58 (1.25) * 0.89-Large CRP 11.26 (8.93) 4.81 (4.08) ** 0.84-Large BERLIN 2.58 (1.98) 1.17 (1.85) ** 0.74-Medium SPARCC 3.92 (3.42) 1.58 (2.43) ** 0.69-Medium SCAISS 295 (332) 95 (163) ** 0.68-Medium KITs4R 1671 (1596) 258 (421) ** 1.04-Large NS, not significant; *, p&lt;0.05; **, p&lt;0.01 Conclusion: All MRI scores have good level of agreement between them and good responsiveness. Berlin and SPARCC are observer dependent, and do not quantify the extension of the MBE area. s-SCAIS helps to this quantification. KITs4R is not observer dependent but clinimetric validation, analizing agreement level with human expert, is necessary. New advanced tools are improving quantitative and objective measurement of BME which is important to analyze responsiveness. References: [1]Development and validation of SCAISS, a tool for semi-automated quantification of sacroilitis by magnetic resonance in spondyloarthritis. Rheumatol Int. 2018 Oct;38(10):1919-1926. [2]The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis. Rheumatol Int. 2020 Apr;40(4):625-633. Disclosure of Interests: None declared.