Deep-learning CT imaging algorithm to detect usual interstitial pneumonia pattern in patients with systemic sclerosis-associated interstitial lung disease: association with disease progression and survival.

OBJECTIVES

Interstitial lung disease (ILD) is the most common cause of death in patients with systemic sclerosis (SSc), although disease behaviour is highly heterogeneous. While a usual interstitial pneumonia (UIP) pattern is associated with worse survival in other ILDs, its significance in SSc-ILD is unclear. We sought to assess the prognostic utility of a deep-learning high resolution CT (HRCT) algorithm of UIP probability in SSc-ILD.

METHODS

Patients with SSc-ILD were included if HRCT images, concomitant lung function tests and follow-up data were available. We used the Systematic Objective Fibrotic Imaging analysis Algorithm (SOFIA), a convolution neural network algorithm that provides probabilities of a UIP pattern on HRCT images. These were converted into the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED)-based UIP probability categories. Decline in lung function was assessed by mixed-effect model analysis and relationship with survival by Cox proportional hazards analysis.

RESULTS

Five hundred and twenty-two patients were included in the study; 19.5% were classified as UIP not in the differential, 53.5% as low probability of UIP, 25.7% as intermediate probability of UIP, and 1.3% as high probability of UIP. A higher likelihood of UIP probability expressed as PIOPED categories was associated with worse baseline forced vital capacity (FVC), as well as with decline in FVC (P = 0.008), and worse 15-year survival (P = 0.001), both independently of age, gender, ethnicity, smoking history and baseline FVC or Goh et al. staging system.

CONCLUSION

A higher probability of a SOFIA-determined UIP pattern is associated with more advanced ILD, disease progression and worse survival, suggesting that it may be a useful prognostic marker in SSc-ILD.