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

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.

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.

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].

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.