โก Quick Summary
This study explored the use of artificial intelligence (AI) to quantify bronchiectasis in patients with chronic obstructive pulmonary disease (COPD) through CT imaging. The findings revealed that the bronchiectasis score was significantly associated with all-cause mortality and the risk of acute exacerbations (AEs).
๐ Key Details
- ๐ Cohorts: 361 patients in the discovery cohort and 112 in the validation cohort
- ๐งฉ Age: Mean age of 67 years, predominantly male (97.5% in discovery, 93.7% in validation)
- โ๏ธ Methodology: AI-based quantification of bronchial tapering ratio on chest CT
- ๐ Key Metrics: Adjusted HR for mortality: 1.86 (discovery), 5.42 (validation)
๐ Key Takeaways
- ๐ AI-based CT quantification provides a reliable assessment of bronchiectasis in COPD patients.
- ๐ก The bronchiectasis score is independently linked to increased mortality risk.
- ๐ฉโ๐ฌ The study included a total of 473 COPD patients across two cohorts.
- ๐ Significant findings include a 60.9% history of AEs in the discovery cohort.
- ๐ค The AI method may enhance clinical research by providing objective assessments.
- ๐ Potential for improved risk stratification and management in COPD patients.
- ๐๏ธ Follow-up mortality was observed in 16.3% of the discovery cohort and 16.1% of the validation cohort.
- ๐ The study highlights the importance of quantitative imaging in clinical evaluations.
๐ Background
Bronchiectasis is a chronic condition characterized by abnormal dilation of the bronchi, often complicating the clinical picture of patients with COPD. Traditional methods of assessing bronchiectasis through chest CT can be subjective and challenging, necessitating the development of more objective, quantitative approaches. The integration of AI technology into imaging analysis presents a promising avenue for enhancing diagnostic accuracy and patient management.
๐๏ธ Study
This research involved a multicenter analysis of COPD patients, focusing on the bronchial tapering ratio as a novel metric for quantifying bronchiectasis. By employing AI algorithms to analyze baseline CT scans, the study aimed to establish a correlation between the bronchiectasis score and clinical outcomes, including mortality and acute exacerbations.
๐ Results
The results indicated that the bronchiectasis score was significantly associated with increased mortality risk, with an adjusted hazard ratio of 1.86 in the discovery cohort and 5.42 in the validation cohort. Additionally, the score correlated with the risk of any acute exacerbation, severe exacerbations, and a shorter time to the first exacerbation, underscoring its clinical relevance.
๐ Impact and Implications
The findings from this study could have profound implications for the management of COPD. By utilizing AI-based quantification of bronchiectasis, healthcare providers can achieve a more objective assessment of disease severity, which may facilitate better risk stratification and tailored management strategies. This advancement highlights the potential for AI technologies to transform clinical practice and improve patient outcomes in respiratory diseases.
๐ฎ Conclusion
This study demonstrates the significant potential of AI in enhancing the assessment of bronchiectasis in COPD patients. The association between the bronchiectasis score and clinical outcomes such as mortality and acute exacerbations emphasizes the need for further research in this area. As we continue to explore the integration of AI in healthcare, we can look forward to more precise and effective management of chronic respiratory conditions.
๐ฌ Your comments
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Bronchiectasis in patients with chronic obstructive pulmonary disease: AI-based CT quantification using the bronchial tapering ratio.
Abstract
OBJECTIVE: Although chest CT is the primary tool for evaluating bronchiectasis, accurately measuring its extent poses challenges. This study aimed to automatically quantify bronchiectasis using an artificial intelligence (AI)-based analysis of the bronchial tapering ratio on chest CT and assess its association with clinical outcomes in patients with chronic obstructive pulmonary disease (COPD).
MATERIALS AND METHODS: COPD patients from two prospective multicenter cohorts were included. AI-based airway quantification was performed on baseline CT, measuring the tapering ratio for each bronchus in the whole lung. The bronchiectasis score accounting for the extent of bronchi with abnormal tapering (inner lumen tapering ratioโโฅโ1.1, indicating airway dilatation) in the whole lung was calculated. Associations between the bronchiectasis score and all-cause mortality and acute exacerbation (AE) were assessed using multivariable models.
RESULTS: The discovery and validation cohorts included 361 (mean age, 67 years; 97.5% men) and 112 patients (mean age, 67 years; 93.7% men), respectively. In the discovery cohort, 220 (60.9%) had a history of at least one AE and 59 (16.3%) died during follow-up, and 18 (16.1%) died in the validation cohort. Bronchiectasis score was independently associated with increased mortality (discovery: adjusted HR, 1.86 [95% CI: 1.08-3.18]; validation: HR, 5.42 [95% CI: 1.97-14.92]). The score was also associated with risk of any AE, severe AE, and shorter time to first AE (for all, pโ<โ0.05).
CONCLUSIONS: In patients with COPD, the quantified extent of bronchiectasis using AI-based CT quantification of the bronchial tapering ratio was associated with all-cause mortality and the risk of AE over time.
KEY POINTS: Question Can AI-based CT quantification of bronchial tapering reliably assess bronchiectasis relevant to clinical outcomes in patients with COPD? Findings Scores from this AI-based method of automatically quantifying the extent of whole lung bronchiectasis were independently associated with all-cause mortality and risk of AEs in COPD patients. Clinical relevance AI-based bronchiectasis analysis on CT may shift clinical research toward more objective, quantitative assessment methods and support risk stratification and management in COPD, highlighting its potential to enhance clinically relevant imaging evaluation.
Author: [‘Park H’, ‘Choe J’, ‘Lee SM’, ‘Lim S’, ‘Lee JS’, ‘Oh YM’, ‘Lee JB’, ‘Hwang HJ’, ‘Yun J’, ‘Bae S’, ‘Yu D’, ‘Loh LC’, ‘Ong CK’, ‘Seo JB’]
Journal: Eur Radiol
Citation: Park H, et al. Bronchiectasis in patients with chronic obstructive pulmonary disease: AI-based CT quantification using the bronchial tapering ratio. Bronchiectasis in patients with chronic obstructive pulmonary disease: AI-based CT quantification using the bronchial tapering ratio. 2025; (unknown volume):(unknown pages). doi: 10.1007/s00330-025-11969-4