RMD open · 2025
To optimise the diagnostic process of rheumatic diseases affecting the hands using fluorescence optical imaging (FOI).
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Stumper Nele, Berger Jörn, Klotsche Jens, Gedat Egbert, Hoff Paula, Schmittat Gabriela, Burmester Gerd R, Krönke Gerhard, Backhaus Marina, Haugen Ida K, Ohrndorf Sarah
Аннотация
BACKGROUND: Accurate and rapid diagnosis of rheumatic diseases is essential for further treatment decision. Different rheumatic diseases present characteristic patterns (image features) in fluorescence optical imaging (FOI). We developed an atlas of FOI image features and tested its ability to differentiate various rheumatic diseases.
METHODS: FOI images from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), connective tissue diseases (CTD) and osteoarthritis (OA) were analysed by two readers blinded for diagnosis and calibrated against each other, using the prima vista mode (PVM) and an automated 5-phase model. Twenty-six different reoccurring typical signal enhancement patterns (features) indicating inflamed joints, nail or skin were defined and all FOI images were scored accordingly. The feature frequency in each patient cohort and phase (PVM, 5-phase) was counted. Contingency tables were created with categorical variable counts and diagnosis using common formulae.
FINDINGS: Four hundred thirty-eight patients with RA (n=117), PsA (n=110), CTD (n=121) and OA (n=90) were included. Once the data had been categorised, a two-step diagnostic pathway was developed: in the first step, OA was best distinguished from the other diseases with high specificity by five patterns (specificity >0.9, diagnostic OR between 2.34 and 8.24). In a second step, the remaining autoimmune diseases were differentiated from each other by a certain number of features (five for RA, 12 for PsA and four for CTD).
INTERPRETATION: This was the first study to show that feature analysis in FOI helps to differentiate typical rheumatic diseases from each other, potentially simplifying and speeding up the diagnostic process. Therefore, FOI could be considered an additional component of a wider range of imaging techniques used in rheumatology.
METHODS: FOI images from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), connective tissue diseases (CTD) and osteoarthritis (OA) were analysed by two readers blinded for diagnosis and calibrated against each other, using the prima vista mode (PVM) and an automated 5-phase model. Twenty-six different reoccurring typical signal enhancement patterns (features) indicating inflamed joints, nail or skin were defined and all FOI images were scored accordingly. The feature frequency in each patient cohort and phase (PVM, 5-phase) was counted. Contingency tables were created with categorical variable counts and diagnosis using common formulae.
FINDINGS: Four hundred thirty-eight patients with RA (n=117), PsA (n=110), CTD (n=121) and OA (n=90) were included. Once the data had been categorised, a two-step diagnostic pathway was developed: in the first step, OA was best distinguished from the other diseases with high specificity by five patterns (specificity >0.9, diagnostic OR between 2.34 and 8.24). In a second step, the remaining autoimmune diseases were differentiated from each other by a certain number of features (five for RA, 12 for PsA and four for CTD).
INTERPRETATION: This was the first study to show that feature analysis in FOI helps to differentiate typical rheumatic diseases from each other, potentially simplifying and speeding up the diagnostic process. Therefore, FOI could be considered an additional component of a wider range of imaging techniques used in rheumatology.