Uncovering Mechanisms of Structural Airway Damage in Bronchiectasis Disease
Addressing current challenges
Bronchiectasis is defined by irreversible bronchial dilatation and characterized by chronic respiratory symptoms, recurrent infections, and frequent exacerbations. As a structurally driven airway disease arising from diverse underlying conditions, it exhibits pronounced heterogeneity in severity and regional distribution, both between patients and within individual lungs.
White central to disease assessment, routine CT image evaluation relies largely on visual interpretation, which is limited in capturing early inflammatory changes, subtle airway abnormalities and fully characterizing underlying disease heterogeneity. As a result, robust patient phenotyping, monitoring of disease progression, and objective assessment of treatment effects over time remain challenging, particularly for the development of targeted therapeutic strategies and in clinical trials.
680
prevalence as number of cases per 100K
per year
Advancing treatment strategies with AI
To address the structural complexity and heterogeneity of bronchiectasis, Thirona applies AI-powered quantitative lung image analysis to transform CT imaging into objective, reproducible biomarkers that support patient stratification, longitudinal assessment of treatment effects at regional level.
This capability is built on disease specific research and validation in large patient cohort including collaboration with the international EMBARC network with analysis of CT data from more than 600 bronchiectasis patients.
- Early and sensitive detection of structural airway changes, including subtle abnormalities in peripheral airways
- Quantification of disease heterogeneity and regional progression
- Accurate and sensitive quantification of bronchial wall thickening, bronchial dilatation and mucus plugs
- Lung structural data to support research on underlying causes of disease
Bronchiectasis is more common than often recognized and frequently coexists with other respiratory diseases. Given the complexity of diagnosis — and the overlap of CT features across conditions — we need more precise, data-driven approaches. What if, with the help of AI, we could integrate multiple quantitative data points to detect subtle structural patterns and guide more personalized treatment decisions?
Dr. Eva Polverino, MD Hospital Vall d’Hebron Spain
Use cases accelerating precision medicine
Precise, accurate and sensitive quantification of Bronchiectasis Disease
Bronchial dilatation, wall thickening and mucus plugs (MP) are the key CT hallmarks of bronchiectasis patients. While visual scoring systems are used to quantify these changes, but it’s time consuming and highly inter-observer variable. To address it , LungQ® analyses was developed to automatically measure bronchial abnormalities and the number of MP within a large cohort from the European Bronchiectasis Registry.
LungQ® analyses has successfully identified substantial heterogeneity in the structural lung abnormalities present in bronchiectasis patient, which is consistent with diverse clinical presentations; Those AI-derived metrics showed a correlation with clinical characteristics and visual BEST-CT scores.
Validation studies
European Respiratory Journal (ERS Congress)
| October 2023
The spectrum of structural lung changes in bronchiectasis: Analysis of 524 EMBARC CTs.
American Journal of Respiratory and Critical Care Medicine
| May 2024
Fully Automatic Analysis of Bronchus-artery Dimensions and Ratios of 625 Chest CTS of Bronchiectasis Patients Participating in the EMBARC Registry
European Respiratory Journal (ERS Congress)
| October 2024
Automatic analysis of bronchus-artery ratios and mucus plugs of 640 chest CTs of EMBARC bronchiectasis patients
Capturing heterogeneity of Bronchiectasis Disease between and within patients
As a complex structural consequence of various underlying conditions, Bronchiectasis Disease is a clinically and radiologically heterogeneous disease. A deep understanding on disease mechanisms and optimized patient phenotyping will be essential for developing optimized patient treatment strategy.
In the recent EMBARC project, LungQ analyses has demonstrated that bronchiectasis is a characterized by a wide range of bronchial wall thickening and dilatation with varying degrees of severity and extent across patient cohorts. Furthermore, a substantial heterogeneous disease patterns across different lobes within the same patient has also been observed.
Validation studies
European Respiratory Journal
| January 2026
Lobar differences on chest CTs of bronchiectasis patients from the EMBARC registry
European Respiratory Journal (ERS Congress)
| October 2024
Automatic analysis of bronchus-artery ratios and mucus plugs of 640 chest CTs of EMBARC bronchiectasis patients
Exploring new therapeutic potential in bronchiectasis disease
While Dornase alfa is an approved treatment for Cystic Fibrosis (CF), its efficacy in bronchiectasis has not previously been quantified. Using LungQ AI-based analyses, researchers have now validated its efficacy in reducing mucus plugs and bronchial wall thickness, correlating objective imaging metrics with clinical symptomatic improvement.
The application of LungQ metrics can serve not only as a sensitive endpoint to measure the efficacy of Dornase alfa therapy but also define inclusion criteria for clinical trials to recruit subjects most likely to benefit from the treatment, increasing the probability of trial success.
