Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast‐Enhanced MRI

• Published in: Journal of magnetic resonance imaging Vol. 61; no. 2; pp. 771 - 783
• Main Authors: Mao, Yijun, Imahori, Kiko, Fang, Wanxuan, Sugimori, Hiroyuki, Kiuch, Shinji, Sutherland, Kenneth, Kamishima, Tamotsu
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2025; Wiley Subscription Services, Inc
• Subjects: Adult; Aged; Arteries; Arthritis; Arthritis, Rheumatoid - diagnostic imaging; Artificial Intelligence; Autoimmune diseases; Contrast Media; Correlation coefficient; Correlation coefficients; dynamic contrast‐enhanced MRI; Female; Field strength; Hand - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Middle Aged; one‐dimensional time‐intensity data; Phases; Pixels; Retrospective Studies; Rheumatoid arthritis; Rheumatoid synovitis; ROC Curve; Segmentation; Statistical analysis; Statistical tests; Synovial Membrane - diagnostic imaging; Synovial Membrane - pathology; Synovitis; Synovitis - diagnostic imaging; one‐dimensional time‐intensity data; dynamic contrast‐enhanced MRI; rheumatoid arthritis; artificial intelligence
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.29463

Background Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast‐enhanced (DCE) MRI in rheumatoid arthritis (RA) patients. Purpose To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels. Study Type Retrospective. Subjects Twelve RA patients underwent DCE‐MRI with 27 phases for creating the AI model and tested on images with a variable number of phases from 35 RA patients. Field Strength/Sequence 3.0 T/DCE T1‐weighted gradient echo sequence (mDixon, water image). Assessment The model was trained with various DCE‐MRI time‐intensity number of phases. Evaluations were conducted for similarity between AI segmentation and manual outlining in 51 ROIs with synovitis. The relationship between synovial volume via AI segmentation with rheumatoid arthritis magnetic resonance imaging scoring (RAMRIS) across whole hands was then evaluated. The reference standard was determined by an experienced musculoskeletal radiologist. Statistical Test Area under the curve (AUC) of receiver operating characteristic (ROC), Dice and Spearman's rank correlation coefficients, and interclass correlation coefficient (ICC). A P‐value <0.05 was considered statistically significant. Results A minimum of 15 phases (acquisition time at least 2.5 minutes) was found to be necessary. AUC ranged from 0.941 ± 0.009 to 0.965 ± 0.009. The Dice score was 0.557–0.615. Spearman's correlation coefficients between the AI model and ground truth were 0.884–0.927 and 0.736–0.831, for joint ROIs and whole hands, respectively. The Spearman's correlation coefficient for the additional test set between the model trained with 15 phases and RAMRIS was 0.768. Conclusion The AI‐based classification model effectively identified synovitis pixels while excluding arteries. The optimal performance was achieved with at least 15 phases, providing a quantitative assessment of inflammatory activity in RA while minimizing acquisition time. Evidence Level 3 Technical Efficacy Stage 2