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Multicenter Cohort Study on Predicting Bronchiectasis Progression, Complications, and Prognosis Using Multi-omics

Multicenter Cohort Study on Predicting Bronchiectasis Progression, Complications, and Prognosis Through Multimodal Integration of Radiomics, Clinical Features, and Lung Microbiota

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07263373
Enrollment
2000
Registered
2025-12-04
Start date
2020-01-01
Completion date
2025-07-31
Last updated
2025-12-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Bronchiectasis Adult, Radiomics

Brief summary

Bronchiectasis is a heterogeneous condition with diverse etiologies and clinical manifestations. Its progression involves a vicious cycle of airway inflammation, recurrent infection, and structural damage, leading to persistent symptoms and declining lung function. Current management focuses on airway clearance and antibiotics, with no disease-modifying therapies available. Recognizing this heterogeneity is crucial for advancing targeted treatments and precision medicine. Radiomics converts medical images into mineable data to reveal underlying pathophysiology. While applied in other respiratory diseases, its potential in bronchiectasis remains underexplored. Both radiomics and the lung microbiome are independently linked to disease severity in conditions like COPD, but their interplay is unclear. Integrating these modalities with clinical data could unlock novel insights, identify new therapeutic targets, and improve diagnostic and prognostic models. However, few studies have investigated multimodal models combining radiomics, microbiome, and clinical features to predict outcomes in bronchiectasis. To address this gap, we designed a multicenter, retrospective study. It will analyze data from patients diagnosed between January 2020 and July 2025 to evaluate the combined value of radiomics, microbial features, and clinical parameters in diagnosing and predicting the progression of bronchiectasis.

Interventions

None listed

Sponsors

The First Affiliated Hospital with Nanjing Medical University
Lead SponsorOTHER

Study design

Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No

Inclusion criteria

* ① Diagnosis of bronchiectasis; * Availability of both raw high-resolution computed tomography (HRCT) chest images and the corresponding radiology report; ③ Age ≥ 18 years.

Exclusion criteria

* ① Incomplete clinical data; * Absence of chest CT imaging studies and reports; ③ Other patients deemed ineligible for enrollment at the investigator's discretion.

Design outcomes

Primary

MeasureTime frame
death2025.7

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026