Quantitative 18F-FDG PET-CT can assess presence and extent of interstitial lung disease in early severe diffuse cutaneous systemic sclerosis
Bibliographic record
Abstract
Abstract Background This study aimed to assess the quantitative uptake of 18 F-FDG PET-CT in the lungs of patients with early severe diffuse cutaneous systemic sclerosis (SSc) with and without interstitial lung disease (ILD), compared to controls. In patients with SSc-ILD, 18 F-FDG uptake was correlated to high-resolution computed tomography (HRCT) and pulmonary function test (PFT) parameters. Methods A prospective, cross-sectional study was conducted, involving 15 patients with SSc-ILD, 5 patients with SSc without ILD, and 7 controls without SSc. 18 F-FDG PET-CT scans were performed following standardized protocols, and quantitative analysis of tracer uptake was conducted in predefined lung regions. In addition, HRCT scans were evaluated for ILD-related radiologic abnormalities. Between-group differences were compared with non-parametric tests, while correlations with PFT parameters were analyzed using Spearman correlation coefficients. Results 18 F-FDG uptake was mainly increased in the dorsobasal lung fields of patients with SSc-ILD compared to SSc without ILD and controls ( p = 0.03 and p < 0.001, respectively). 18 F-FDG uptake was higher in SSc patients with extensive ILD (≥ 20% vs < 20%, p = 0.04) and correlated with lower DLCO% ( R = -0.59, p = 0.02). Ground-glass opacities, with or without reticulation, corresponded to increased 18 F-FDG uptake. Conclusions 18 F-FDG PET-CT can detect metabolic activity in the lungs of patients with early severe diffuse cutaneous SSc and ILD, correlating with higher ILD extent (≥ 20%) and lower DLCO%. These results suggest the potential utility of 18 F-FDG PET-CT in the early detection of ILD (progression) and aiding in risk stratification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".