Ultrashort echo time MRI biomarkers of asthma
Bibliographic record
Abstract
Purpose To develop and assess ultrashort echo‐time (UTE) magnetic resonance imaging (MRI) biomarkers of lung function in asthma patients. Materials and Methods Thirty participants including 13 healthy volunteers and 17 asthmatics provided written informed consent to UTE and pulmonary function tests in addition to hyperpolarized‐noble‐gas 3T MRI and computed tomography (CT) for asthmatics only. The difference in MRI signal‐intensity (SI) across four lung volumes (full‐expiration, functional‐residual‐capacity [FRC], FRC+1L, and full‐inspiration) was determined on a voxel‐by‐voxel basis to generate dynamic proton‐density (DPD) maps. MRI ventilation‐defect‐percent (VDP), UTE SI, and DPD values as well as CT radiodensity were determined for whole lung and individual lobes. Results Mean SI at full‐expiration ( P < 0.01), FRC ( P < 0.05), and DPD ( P < 0.01) were greater in healthy volunteers compared to asthmatics. In asthmatics, UTE SI at full‐expiration and DPD were correlated with FEV 1 /FVC (SI r = 0.73/ P = 0.002; DPD r = 0.75/ P = 0.003), RV/TLC (SI r = –0.57/ P = 0.02), or RV (DPD r = –0.62/ P = 0.02), CT radiodensity (SI r = 0.83/ P = 0.006; DPD r = 0.71/ P = 0.01), and lobar VDP (SI r s = –0.33/ P = 0.02; DPD r s = –0.47/ P = 0.01). Conclusion In patients with asthma, UTE SI and dynamic proton‐density were related to pulmonary function measurements, whole lung and lobar VDP, as well as CT radiodensity. Thus, UTE MRI biomarkers may reflect ventilation heterogeneity and/or gas‐trapping in asthmatics using conventional equipment, making this approach potentially amenable for clinical use. Level of Evidence: 2 J. Magn. Reson. Imaging 2017;45:1204–1215
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".