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Association of Contrast Enhanced Dual Energy CT-derived Pulmonary Arterial Tree-to-Lung Ratio (ArtLR) With COPD

2025· article· en· W4410273994 on OpenAlexaff
Saeed Hosseini, Jeremy E. Orr, Nora Newcomb, Melissa S. Dunn, Joel R. Leininger, J. Guo, Sarah E. Gerard, A.P. Comellas, Mathews Jacob, Joyce A. Schroeder, P. Woodruff, J.L. Curtis, Joseph M. Reinhardt, E.A. Hermann, R. Graham Barr, Benjamin M. Smith, Nadia N. Hansel, M.K. Han, E.A. Hoffman

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineCOPDContrast (vision)LungRadiologyCardiologyInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract RATIONALE: Airway-to-lung ratio (AirLR), calculated as the mean of airway lumen diameters at standard anatomic locations divided by the cube-root of total lung volume, is associated with COPD risk regardless of smoking status, and we have previously shown an association between total-pulmonary-vascular-volume-to-lung-size relationship with AirLR. [PMCID: PMC11284327] Using dual energy computed tomography (DECT), we recently developed a deep learning-based algorithm for pulmonary arterial segmentation to compute pulmonary-arterial-to-lung ratio (ArtLR) which correlated significantly with AirLR and markers COPD. Here we automate image processing in a larger cohort to estimate ArtLR, introduce an alternative ArtLR standardization and correlate it to COPD-related measures of lung structure and function. METHODS: Using a SPIROMICS sub-cohort (110 participants), imaged via contrast enhanced DECT at functional residual capacity (FRC), an automatic deep learning pipeline segmented the arteries from the pulmonary vascular tree. Another pipeline automatically extracted the centerlines and calculated average arterial segment diameters. A paired non-contrast total lung capacity (TLC) scan was registered to the DECT FRC scan to transfer airway segment labels from the TLC to FRC domain. AirLR was calculated as the mean of airway lumen diameters at standard anatomic locations (trachea-to-subsegments) divided by cube root of lung volume [TLC or FRC]. A simple Long Short-Term Memory (LSTM) network was used to find corresponding anatomic arterial segments. Associations between ArtLR and %Emphysema, AirLR, pre- and post-bronchodilator FEV1/FVC, and a texture-based (AMFM) measure of %broncho-vascular bundles was assessed using linear regression to adjust for age, sex and BMI. RESULTS: In adjusted analyses, a 1-SD decrement in ArtLR-TLC was associated with 4.198 units increase in %Emphysema-910 (95%CI: 1.360-7.037; p = 0.0041), and 0.8198 units decrease in %Bronchovascular (95%CI: 0.5037-1.136; p < 0.0001). A 1-SD increment in ArtLR-TLC was associated with 0.2515 SD increase in AirLR-Outer (95%CI: 0.04947SD-0.4536SD; p = 0.0152), and 0.02083 units increase in PreBronch-FEV1FVC (95%CI: 0.001814-0.03984; p = 0.0321). Sex and BMI show significant associations with ArtLR-TLC, where female is associated with 1.068 SD decrease in ArtLR, and a unit increase in BMI is associated with 0.05788 SD increase in ArtLR (both p-values < 0.0001), and age was not association with ArtLR. CONCLUSIONS: An automated pipeline for pulmonary arterial tree segmentation and standardization from contrast enhanced DECT FRC scans demonstrates a significant relationship between ArtLR and AirLR as well as between ArtLR and %emphysema, and FEV1/FVC. A standardized method of arterial tree size assessment provides for expanded exploration of vascular vs airway disease etiologies.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.298
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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