Cystic Fibrosis: Detecting Changes in Airway Inflammation with FDG PET/CT
Bibliographic record
Abstract
PURPOSE: To determine if fluorine 18 fluorodeoxyglucose (FDG) positron emission tomographic (PET)/computed tomographic (CT) imaging can depict a treatment effect from intravenous antibiotics for pulmonary exacerbation in cystic fibrosis (CF). MATERIALS AND METHODS: The study was approved by the institutional review board of the Hospital for Sick Children and by Health Canada. Consent was obtained from all subjects. Patients with CF who were between 6 and 18 years of age and were admitted for a pulmonary exacerbation were eligible for the study. FDG PET/CT examinations (with low-dose CT) were performed on days 1 and 14 of admission (±72 hours). PET activity was quantified by using standardized uptake values (SUVs) through assessment of background activity (mean SUV [SUV(mean)]) and superimposed focal uptake (maximum SUV [SUV(max)]) for each lung zone. CT studies were scored by using the CF-CT model. SUVs from pre- and posttherapy studies were compared by using paired t tests. Unpaired t tests were used to compare data in patients with CF and data in 10 control subjects. RESULTS: Twenty patients with CF were enrolled. Antibiotic therapy resulted in a significant decrease in SUV(max) (mean difference, 2.3 ± 2.1 [standard deviation], P < .0001). Pretherapy SUV(max) and SUV(mean) and posttherapy SUV(max) were significantly different from those in control subjects. The change in SUV(max) and percentage predicted forced expiratory volume in 1 second was negatively correlated. (R = -0.72, P = .004). Overall CF-CT scores significantly correlated with SUV(max) (R = 0.40, P = .01). CONCLUSION: FDG PET/CT is a useful tool for detecting inflammatory changes resulting from treatment for pulmonary exacerbations in pediatric patients with CF. Inflammatory changes detected by using FDG PET/CT correlated with lung function, sputum neutrophil counts, and CF-CT scores. Analyzing focal lung inflammation (with SUV(max)) may be a feasible way to measure airway inflammation in patients with CF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".