Quantitative computed tomography in systemic sclerosis-associated interstitial lung disease
Bibliographic record
Abstract
Rationale: Systemic sclerosis (SSc) is frequently complicated by interstitial lung disease (ILD), which is associated with significant morbidity and mortality in this population. Measuring disease extent and progression of SSc-ILD is challenging, with recent studies suggesting potential utility of quantitative measurements from computed tomography (CT) scans. Our objective was to determine the associations of CT density-based measurements with physiological parameters, visual CT scores, and survival in patients with SSc-ILD. Methods: Patients with SSc-ILD and volumetric high-resolution CT images with ≤1.25mm slice thickness were retrospectively identified. Cardiothoracic radiologists with >5 years’ experience produced visual CT scores of ground-glass, reticulation, and honeycombing, to the nearest 5%. Visual fibrosis scores were calculated as the sum of reticulation and honeycombing. CT density measurements included high attenuation areas (HAA), skewness, kurtosis, and mean lung attenuation (MLA), which were determined after excluding large airways and blood vessels. Associations of qCT measures with pulmonary physiology, visual CT scores, and mortality were analyzed using Spearman rank correlation and Cox regression. Results: 502 CT scans and 1084 PFTs from 170 patients with SSc-ILD were included. Baseline HAA, skewness, kurtosis, and MLA were associated with FVC (p<0.001), DLCO (p≤0.001), and visual fibrosis scores (p≤0.004). Changes in the qCT variables also correlated with concurrent changes in FVC (p≤0.02), DLCO (p≤0.01), and visual fibrosis scores (p≤0.004). Associations with physiology measures and visual CT scores did not change after adjustment for age, sex, and pack-years. All four baseline qCT variables (p0.005), ∆HAA (p<0.001), ∆kurtosis (p=0.04), and ∆MLA (p=0.006) predicted mortality on unadjusted analysis. ∆HAA and ∆MLA remained predictors of mortality after adjustment for visual CT scores. Changes in all four qCT variables remained independent predictors of survival after adjustment for baseline FVC, DLCO, and the ILD-GAP and SADL indices, but not when adjusting for changes in lung function. Conclusion: CT density-based measures correlate with physiologic impairment and visual CT scores in patients with SSc-ILD. Baseline and change in CT density measurements predict mortality, but not with adjustment for pulmonary function measures, indicating that the clinical utility of more sophisticated qCT variables should be explored.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".