Quantitative analysis of chest CT with deep learning to assess the efficacy of tofacitinib in the treatment of anti-MDA5+ dermatomyositis.

INTRODUCTION AND

OBJECTIVES

This study aimed to clarify whether quantitative high-resolution computed tomography (HRCT) analysis can assess the condition of interstitial lung disease (ILD) associated with anti-melanoma differentiation-associated gene 5 positive (anti MDA5+) dermatomyositis (DM) and investigate the efficacy of tofacitinib in the treatment of anti-MDA5+ DM.

MATERIALS AND

METHODS

Seventy patients were included in this retrospective study: 39 in the tofacitinib group and 31 in the group without tofacitinib. Patients' HRCT were uploaded to a deep learning system to assess ILD regression. Based on patients' quantitative HRCT results, survival and glucocorticoids (GCs) usage, the efficacy of tofacitinib in the treatment of anti-MDA5+ DM were assessed. The safety was assessed by recording the incidence of adverse reactions. Data were analyzed using SPSS26.0 and R4.4.1.

RESULTS

No significant differences for baseline characteristics were observed between the two groups of patients, except for cutaneous involvement. Tofacitinib group showed higher 3-year survival and it was an independent protective factor against mortality. Elevated serum ferritin (>1000μg/L) increased the risk of death. Quantitative HRCT analysis showed a significant reduction in the percentage of whole-lung involvement in the tofacitinib group between the baseline and follow-up. The total lesion volume reduction in the whole lung after treatment was substantially higher in the tofacitinib group. The tofacitinib group had a shorter duration of GCs tapering and a higher risk of EBV infection.

CONCLUSIONS

Quantitative HRCT analysis can be used to assess the response of ILD to tofacitinib treatment. Tofacitinib is effective in patients with anti-MDA5+ DM-ILD but increases the risk of infection.