Investigating the Relationship Between Central Computer Tomography Airway Tree Features and Small Airways Disease in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Rationale: Chronic Obstructive Pulmonary Disease (COPD) results in remodeling of both the large and small airways. Computed tomography (CT) imaging allows quantification of morphometric features from the central airways to be measured directly from a single full-inspiration acquisition, and small airway disease (SAD) features indirectly by registering full-inspiration to full-expiration images, known as Disease Probability Measure (DPM SAD ). Our objective is to investigate the relationship between CT central airway tree and DPM SAD features in COPD. Methods: Full-inspiration and full-expiration CT images were obtained from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study. Image registration and airway segmentation was performed (VIDA Diagnostics Inc.). Central CT airway tree features were generated in each of the 19 bronchopulmonary segments, including measurements reflecting the airway dimensions (lumen diameter, wall area, total airway count, airway tapering), the direction (branch angle, directional cosines) and statistical features (mean, min, max of a given feature). CT DPM SAD measurements were generated by co-registration of full-inspiration to full-expiration CT images. Each voxel was classified as either normal, emphysema or SAD and expressed as the percentage of the total lung volume. DPM SAD was generated in each bronchopulmonary segment to be spatially matched with the regional airway features. For statistical analysis, airway features with high collinearity (r>0.70) were removed. The remaining features were inputted into a mixed effects beta regression model treating participants and bronchopulmonary segments as random effects and the airway features as fixed effects. A null model without airway tree features was generated to compare the AIC and BIC values to assess model fit and a likelihood ratio test was performed. Results: A total of 318 participants were evaluated: n=17 Healthy, n=92 At-Risk, n=114 mild COPD, and n=95 with moderate-severe COPD. A total of 79 features were extracted from central airways; after adjusting for collinearity 16 features remained. Of the remaining features, 8/16 were significantly associated with DPM SAD measurements in the mixed effects beta regression model (P<0.05). There was a significant difference (P<0.0001) between the null model (AIC=-8859.8, BIC=-8827.0) and the airway tree model (AIC=-9967.9, BIC=-9830.1). Significant airway features included: average wall thickness (=0.039), max and min wall thickness using the major diameter (=0.017, =-0.031, respectively), total airway count (=-0.020), airway tapering (=0.019), and directional cosines of the airway in each direction (X: =-0.032,Y: =-0.175, Z: =-0.164). Conclusion: Central airway tree features were associated with measurements reflecting small airway disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".