Quantifying Cutaneous Dermatomyositis: A Novel 3D Image-based Approach

Nantakarn Pongtarakulpanit¹, Tanya Chandra¹, shiri keret², Eugenia Gkiaouraki³, Vladimir Liarski¹, Dana Ascherman³, Siamak Mogahadam¹, Chester Oddis¹ and Rohit Aggarwal⁴, ¹Division of Rheumatology and Clinical Immunology, University of Pittsburgh, Pittsburgh, PA, ²Rheumatology unit, Bnai-Zion medical center and the faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel., Atlit, Israel, ³Division of Rheumatology and Clinical Immunology, University of Pittsburgh Medical Center, Pittsburgh, PA, ⁴Division of Rheumatology and Clinical Immunology, University of Pittsburgh, Pittsburgh, PA, USA, Pittsburgh, PA

Meeting: ACR Convergence 2024

Keywords: dermatomyositis, Imaging, Outcome measures

Session Information

Date: Monday, November 18, 2024

Title: Muscle Biology, Myositis & Myopathies – Basic & Clinical Science Poster III

Session Type: Poster Session C

Session Time: 10:30AM-12:30PM

Background/Purpose: Rapid advancement in image analysis now has a critical role in the diagnosis and assessment of skin lesions. The current standard of visual examination and scoring has considerable subjectivity, recall bias for assessing change, and inter-rater reliability, particularly when reported by non-dermatologists. This study assesses the feasibility of 3D image-based assessment to evaluate cutaneous disease activity in dermatomyositis (DM).

Methods: DM patients seen in the University of Pittsburgh Myositis Center between August 2022 and April 2024 had 3D images of their rashes generated using a 3D camera. Each set of images included 7 “standard” areas (dorsum of left hand, dorsum of right hand, left side of the upper chest, right side of the upper chest, left side of the upper back, right side of the upper back, and face), and “other” involved areas based on rash distribution. DM rashes were manually mapped and measured for area and redness using the 3D image application. A “3D image disease activity score” was calculated based on the percentage of the rashes relative to the total body surface area, multiplied by the degree of redness (based on average red/green coordinate in the CIELAB color Space system). The construct validity and responsiveness of the score were evaluated using the Spearman rank correlation coefficient against a standard in-clinic myositis expert evaluation of “Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI)” and a standard patient-reported outcome termed the “Skindex”. A linear regression model assessed the relationship between the 3D image-derived rash area and redness with the CDASI score.

Results: 27 DM patients with 1-2 clinic visits spaced at least 3 months apart were enrolled. Subjects were predominantly female (81.5%) and Caucasian (96.3%). The median (IQR) CDASI and Skindex scores were 6.0 (0.0 – 17.0) and 28.0 (4.0 – 45.0), respectively. For the construct validity, the 3D image disease activity score correlated strongly with the physician-derived CDASI and patient-reported Skindex scores (Spearman rank correlation coefficients 0.83 and 0.67, respectively, p< 0.01). The linear regression analysis identified rash area and redness from 3D images as significant predictors of the CDASI score. The model explained 79.5% of the variance in CDASI scoring (R² = 0.795). Regarding responsiveness, absolute changes from baseline in 3D image disease activity score correlated strongly with absolute changes in the CDASI score (the Spearman rank correlation coefficient 0.61, P = 0.004).

Conclusion: Our results demonstrate favorable validity and responsiveness in the 3D images for evaluating rash in DM patients. The 3D image-derived rash area and redness are significant predictors of CDASI scores, supporting the utility of 3D imaging in objective disease activity assessments of cutaneous DM.

Table 1. The correlations of 3D image parameters with Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI) and Skindex scores

Table 2. The correlations of absolute change from the baseline of 3D image parameters with absolute change from the baseline of Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI) and Skindex scores

Table 3. A linear regression model with regression coefficients for the relationship between the 3D image-derived rash area and redness with the Cutaneous Dermatomyositis Disease Area and Severity Index (CDASI).

Disclosures: N. Pongtarakulpanit: None; T. Chandra: None; s. keret: None; E. Gkiaouraki: None; V. Liarski: None; D. Ascherman: None; S. Mogahadam: None; C. Oddis: Abcuro, 5, Alexion, 5, Argenx, 5, Boehringer Ingelheim, 5, CSL Behring, 5, EMD Serono, 5, Janssen, 5, Pfizer, 5, Priovant, 5; R. Aggarwal: Alexion, 2, ANI Pharmaceuticals, 2, Argenx, 2, AstraZeneca, 2, Boehringer-Ingelheim, 2, 5, Bristol-Myers Squibb(BMS), 2, 5, CabalettaBio, 2, Capella Bioscience, 2, Capstanx, 2, Corbus, 2, CSL Behring, 2, EMD Serono, 2, 5, Galapagos, 2, Horizon Therapeutics, 2, I-Cell, 2, Immunovant, 2, Janssen, 1, 2, 5, Kezar, 2, Kyverna, 2, Lilly, 2, Mallinckrodt, 5, Manta Medicines Corporation, 2, Merck, 2, Novartis, 2, Nuvig Therapeutics, 2, Octapharma, 2, Pfizer, 2, 5, Q32, 5, Roivant, 2, Sanofi, 2, Teva, 2.

To cite this abstract in AMA style:

Pongtarakulpanit N, Chandra T, keret s, Gkiaouraki E, Liarski V, Ascherman D, Mogahadam S, Oddis C, Aggarwal R. Quantifying Cutaneous Dermatomyositis: A Novel 3D Image-based Approach [abstract]. Arthritis Rheumatol. 2024; 76 (suppl 9). https://acrabstracts.org/abstract/quantifying-cutaneous-dermatomyositis-a-novel-3d-image-based-approach/. Accessed .

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