Advanced imaging in systemic sclerosis: distinguishing activity from damage across organ systems

Area of Science:

• Advanced medical imaging
• Quantitative analysis in rheumatology
• Molecular imaging in fibrotic diseases

Background:

• Systemic sclerosis (SSc) diagnosis is challenged by the inability to distinguish reversible disease activity from irreversible fibrosis.
• Current imaging like HRCT and echocardiography primarily show structural damage, not active disease.
• There is a critical need for imaging that can assess disease activity and reversibility in SSc.

Purpose of the Study:

• To review next-generation imaging modalities for systemic sclerosis (SSc).
• To highlight the role of quantitative machine learning algorithms and molecular imaging in SSc assessment.
• To discuss the potential of new imaging techniques for distinguishing active disease from fibrosis.

Main Methods:

• Review of emerging imaging technologies for SSc.
• Focus on quantitative machine learning applied to imaging data (e.g., HRCT).
• Integration of molecular imaging techniques (e.g., PET scans) and advanced ultrasound/OCT.

Main Results:

• Machine learning-enhanced HRCT detects interstitial lung disease (ILD) activity in SSc.
• Cardiac MRI mapping visualizes diffuse fibrosis missed by conventional methods.
• Positron emission tomography (PET) with fibroblast activation protein inhibitors (FAPI-PET) visualizes active fibrogenesis.
• [18F]Sodium fluoride PET detects active calcinosis.
• High-frequency ultrasound, elastography, and OCT-angiography improve assessment of skin and microvascular changes.

Conclusions:

• Emerging imaging modalities offer a shift from documenting damage to measuring disease activity directly in SSc.
• These techniques may enable earlier detection of treatable SSc.
• Advanced imaging can provide more sensitive endpoints for clinical trials in SSc.