ReviewRespiratory research2026
Precision treatment of COPD based on novel imaging phenotypes: a treatable traits approach.
Review in Respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome. Spirometry, while diagnostic, inadequately characterizes disease complexity. This review explores how thoracic imaging, particularly computed tomography and magnetic resonance imaging, may help identify specific pulmonary "treatable traits" that could inform future precision management approaches. We detail four core imaging phenotypes: emphysema, small airway disease, airway mucus plugs, and airway wall thickening. For each, we discuss validated and emerging quantitative biomarkers-such as the Parametric Response Map, total airway count, mucus plug score, Pi10, and PiSlope-that facilitate phenotypic stratification and prognostication. We further describe two distinct disease trajectories ("Tissue-Airway" and "Airway-Tissue") revealed by progression modeling. Critically, we discuss potential links between these imaging-defined traits to targeted therapeutic strategies, including ultra-fine particle inhalers for small airway disease, mucus clearance strategies, and CT-guided lung volume reduction for emphysema. Despite significant progress, challenges remain in standardizing measurements, validating clinical utility, and integrating imaging biomarkers into routine care. Future integration of artificial intelligence and multimodal imaging holds promise for advancing COPD management towards true personalized medicine.
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