Assessment of Airway-To-Lung Ratio on Cardiac CT: Agreement With Full-lung CT and Validation With Dysanapsis Endpoints: The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) CT Harmonization Study
Bibliographic record
Abstract
Abstract INTRODUCTION: Airway-to-lung ratio (ALR) is a quantitative index of airway tree caliber relative to lung size that has been used as an imaging biomarker of dysanapsis. Assessment of ALR on full-lung CT is repeatable and associated with incident chronic obstructive pulmonary disease and respiratory hospitalizations later in life. C4R is examining risk of severe COVID-19 and Long COVID, and includes five cohorts with cardiac CT but not full-lung CTs. This study evaluated the agreement of cardiac CT-and full-lung CT-assessed ALR, repeatability of cardiac CT-assessed ALR and associations of cardiac CT-assessed ALR with dysanapsis endpoints in one C4R cohort. METHODS: The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study included adults 48-85 years old free of clinical cardiovascular disease that underwent repeated cardiac CT scans at Exam 1 and paired cardiac and full-lung CT scans at Exam 5 (Figure).Full-lung ALR was computed as the mean of airway lumen diameters measured at 19 standard anatomic locations (trachea-to-subsegments) divided by the cube-root of total lung volume. Cardiac CT ALR was inferred using deep-learning models trained to estimate full-lung ALR from projections of lungs and airways segmented on cardiac CT. Agreement between cardiac CT and full-lung CT ALR, and repeatability of cardiac CT ALR were both assessed by intra-class correlation (ICC). Validation of cardiac CT ALR with dysanapsis endpoints consisted of computing the variance in forced expired volume in 1-second divided by forced vital capacity (FEV1/FVC) explained in the Exam 5 test-set, and Exam 1 associations with respiratory mortality and genotype-derived dysanapsis genetic risk. Regression models were adjusted for age, sex, height, race-ethnicity, income, educational attainment, cigarette smoking status, pack-years, second-hand smoke exposure (hours per week) and asthma diagnosis. RESULTS: Among n=377 Exam 5 test-set participants, the ICC of paired cardiac CT and full-lung CT ALR was 0.849. Among n=6,310 Exam 1 participants with repeated cardiac CT-assessed ALR, the ICC was 0.951. The cardiac CT ALR increment in FEV1/FVC variance explained was +8.7% (vs. full-lung CT ALR increment: +8.8%). A 1-SD decrement in cardiac CT ALR was associated with higher respiratory mortality (adjusted hazard ratio: 2.22, 95%CI 1.68-2.92) and higher dysanapsis genetic risk score (+0.14 SD dysanapsis genetic risk score [95%CI: 0.11-0.16]). CONCLUSION: Cardiac CT-assessed ALR is repeatable, agrees well with full-lung CT-assessed ALR and associates with airflow obstruction, respiratory mortality and dysanapsis genetic risk. Assessing ALR in cohorts with cardiac CT in C4R may facilitate research into the role of dysanapsis in COVID-19.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".