Radiomic analysis of high-resolution computed tomography predicts interstitial lung disease progression and mortality in systemic sclerosis.

OBJECTIVE: The automated assessment of chest computed tomography (HRCT) scans allows the objective quantification of interstitial lung disease (ILD) related features. Here, we explored the ability of HRCT-derived radiomic features to predict all-cause mortality and ILD progression in systemic sclerosis (SSc)-ILD patients. METHODS: We analyzed baseline and follow-up HRCT scans of SSc-ILD patients using lung texture analysis (LTA™, Imbio). Lung parenchymal alterations (honeycombing, ground-glass, reticulation, hyperlucency) and pulmonary vessel volume (PVV) were quantified as percentage of the respective lung volume - for whole lungs and for upper, middle and lower zones. Univariable and multivariable Cox regression and generalized estimating equation (GEE) models were applied to identify radiomics predictors of all-cause mortality and ILD progression, respectively. RESULTS: Of 313 SSc-ILD patients, 267 (85%) were eligible for radiomic analysis and included in the mortality analysis; 160 (60%) were included in the progression analysis.Over 42 (IQR 27-110) months of follow-up, 65 (24%) patients died. They presented with greater extent of PVV% and parenchymal alterations across all lung zones, compared to patients who survived. PVV% independently predicted mortality [adjusted HR 1.21, 95% CI 1.094-1.354], particularly in the upper zones [adjusted HR 1.28, 95% CI 1.11-1.46].Among 261 yearly follow-up visits from 160 SSc-ILD patients, 50.6% showed at least one episode of ILD progression, for a total of 99 (37.7%) episodes. Baseline radiomic features were overall comparable between progressors and non-progressors. The extent of whole-lung honeycombing was identified as the only independent radiomic predictor of ILD progression [adjusted OR 1.53, 95% CI 1.17-2.01]. CONCLUSION: The PVV% and the extent of honeycombing independently predict mortality and ILD progression, respectively. These radiomic features might support risk stratification of SSc-ILD patients, on top of functional and clinical characteristics.