Brainomix e-Lung is an FDA-cleared, AI-powered imaging software platform that automatically identifies and quantifies features on CT lung scans enabling clinicians to more easily identify changes including subtle deteriorations over multiple scan timepoints.
Through a research collaboration and partnership with Boehringer Ingelheim, the global leader in pulmonary fibrosis therapies, Brainomix were granted privileged access to the landmark INBUILD clinical trial dataset to run the first quantitative CT analysis.
The analysis used metrics from CT images.
The authors were able to accurately and sensitively facilitate identification of PPF, including the assessment of progression
Results from a retrospective study with the University of Chicago, Weill Cornell Medicine, and the University of Alabama at Birmingham were presented at ERS and CHEST, demonstrating:
They could identify CT progression in 74% of patients deemed clinically stable.
They accurately identified patients at risk of developing future PPF from a single baseline scan.
Imaging metrics on the first patient scan are robust, independent predictors of mortality.
“The data we have shown for e-Lung is very promising, and the ability to objectively assess parenchymal changes to predict disease trajectory and treatment responses could really help us personalize treatment decisions and improve outcomes for patients living with pulmonary fibrosis.”
A new study was presented at ECR 2026 by Dr Logan Sun (Royal Brompton Hospital, London). Five readers, blinded to clinical data, independently reviewed serial CTs side-by-side from 102 patients with non-IPF fibrotic ILD. All patients in this cohort demonstrated marginal FVC decline of 5 - 10%.
Readers categorized each case as either stable or progressive disease based on visually estimated changes in ILD extent.
Overlays configured to quantitatively visualize parenchymal radiological features were applied.
In cases initially categorized as stable, quantitative CT imaging features were used to flag 22 to 40 cases per reader for re-evaluation, where readers could retain the original categorization or change it to progressive.
Readers changed PPF categorization in 45 - 94% of these cases, demonstrating improved reader performance.
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Studies have explored a novel configuration of density and volume parameters, the WRVS, which characterizes the total extent of peripheral fibrosis.
WRVS is a strong predictor of FVC decline in IPF patients.¹
An increase in WRVS is linked to greater risk of mortality.¹
A change in WRVS of 3% was a more accurate predictor of mortality compared with FVC decline or radiologically-defined progression.²
Discover how you can integrate e-Lung into your Lung CT clinical workflows.
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